# Tablicious for GNU Octave ¶

This manual is for Tablicious, version 0.4.3.

## Short Table of Contents

## Table of Contents

## 1 Introduction ¶

> Time is an illusion. Lunchtime doubly so.

— Douglas Adams

This is the manual for the Tablicious package version 0.4.3 for GNU Octave.

Tablicious provides somewhat-Matlab-compatible tabular data and date/time support for GNU Octave. This includes a `table` class with support for filtering and join operations; `datetime`, `duration`, and related classes; Missing Data support; `string` and `categorical` data types; and other miscellaneous things.

This document is a work in progress. You are invited to help improve it and submit patches.

Tablicious’s classes are designed to be convenient to use while still being efficient. The data representations used by Tablicious are designed to be efficient and suitable for working with large-ish data sets. A “large-ish” data set is one that can have millions of elements or rows, but still fits in main computer memory. Tablicious’s main relational and arithmetic operations are all implemented using vectorized operations on primitive Octave data types.

Tablicious was written by Andrew Janke <floss@apjanke.net>. Support can be found on the Tablicious project GitHub page.

## 2 Getting Started ¶

The easiest way to obtain Tablicious is by using Octave’s `pkg` package manager. To install the development prerelease of Tablicious, run this in Octave:

    pkg install https://github.com/apjanke/octave-tablicious/releases/download/v0.4.3/tablicious-0.4.3.tar.gz

(Check the releases page at https://github.com/apjanke/octave-tablicious/releases to find out what the actual latest release number is.)

For development, you can obtain the source code for Tablicious from the project repo on GitHub at https://github.com/apjanke/octave-tablicious. Make a local clone of the repo. Then add the `inst` directory in the repo to your Octave path.

## 3 Table Representation ¶

Tablicious provides the `table` class for representing tabular data.

A `table` is an array object that represents a tabular data structure. It holds multiple named “variables”, each of which is a column vector, or a 2-D matrix whose rows are read as records.

A `table` is composed of multiple “variables”, each with a name, which all have the same number of rows. (A `table` variable is like a “column” in SQL tables or in R or Python/pandas dataframes. Whenever you read “variable” here, think “column”.) Taken together, the i-th element or row of each variable compose a single record or observation.

Tables are good ways of arranging data if you have data that would otherwise be stored in a few separate variables which all need to be kept in the same shape and order, especially if you might want to do element-wise comparisons involving two or more of those variables. That’s basically all a `table` is: it holds a collection of variables, and makes sure they are all kept aligned and ordered in the same way.

Tables are a lot like SQL tables or result sets, and are based on the same relational algebra theory that SQL is. Many common, even powerful, SQL operations can be done in Octave using `table` arrays. It’s like having your own in-memory SQL engine.

### 3.1 Table Construction ¶

There are two main ways to construct a `table` array: build one up by combining multiple variables together, or convert an existing tabular-organized array into a `table`.

To build an array from multiple variables, use the `table(…)` constructor, passing in all of your variables as separate inputs. It takes any number of inputs. Each input becomes a table variable in the new `table` object. If you pass your constructor inputs directly from variables, it automatically picks up their names and uses them as the table variable names. Otherwise, if you’re using more complex expressions, you’ll need to supply the `'VariableNames'` option.

To convert a tabular-organized array of another type into a `table`, use the conversion functions like `array2table`, `struct2table` and `cell2table`. `array2table` and `cell2table` take each column of the input array and turn it into a separate table variable in the resulting `table`. `struct2table` takes the fields of a struct and puts them into table variables.

### 3.2 Tables vs SQL ¶

Here’s a table (ha!) of what SQL and relational algebar operations correspond to what Octave `table` operations.

In this table, `t` is a variable holding a `table` array, and `ix` is some indexing expression.

SQL | Relational | Octave `table`
--- | --- | ---
SELECT | PROJECT | `subsetvars`, `t(:,ix)`
WHERE | RESTRICT | `subsetrows`, `t(ix,:)`
INNER JOIN | JOIN | `innerjoin`
OUTER JOIN | OUTER JOIN | `outerjoin`
FROM `table1, table2, …` | Cartesian product | `cartesian`
GROUP BY | SUMMARIZE | `groupby`
DISTINCT | (automatic) | `unique(t)`

Note that there is one big difference between relational algebra and SQL & Octave `table`: Relations in relational algebra are sets, not lists. There are no duplicate rows in relational algebra, and there is no ordering. So every operation there does an implicit `DISTINCT`/`unique()` on its results, and there‘s no `ORDER BY`/`sort()`. This is not the case in SQL or Octave `table`.

Note for users coming from Matlab: Matlab does not provide a general `groupby` function. Instead, you have to variously use `rowfun`, `grpstats`, `groupsummary`, and manual code to accomplish “group by” operations.

Note: I wrote this based on my understanding of relational algebra from reading C. J. Date books. Other people’s understanding and terminology may differ. - apjanke

## 4 Date and Time Representation ¶

Tablicious provides the `datetime` class for representing points in time.

There’s also `duration` and `calendarDuration` for representing periods or durations of time. Like vector quantities along the time line, as opposed to `datetime` being a point along the time line.

### 4.1 `datetime` Class ¶

A `datetime` is an array object that represents points in time in the familiar Gregorian calendar.

This is an attempt to reproduce the functionality of Matlab’s `datetime`. It also contains some Octave-specific extensions.

The underlying representation is that of a datenum (a `double` containing the number of days since the Matlab epoch), but encapsulating it in an object provides several benefits: friendly human-readable display, type safety, automatic type conversion, and time zone support. In addition to the underlying datenum array, a `datetime` inclues an optional `TimeZone` property indicating what time zone the datetimes are in.

So, basically, a `datetime` is an object wrapper around a datenum array, plus time zone support.

#### 4.1.1 Datenum Compatibility ¶

While the underlying data representation of `datetime` is compatible with (in fact, identical to) that of datenums, you cannot directly combine them via assignment, concatenation, or most arithmetic operations.

This is because of the signature of the `datetime` constructor. When combining objects and primitive types like `double`, the primitive type is promoted to an object by calling the other object’s one-argument constructor on it. However, the one-argument numeric-input consstructor for `datetime` does not accept datenums: it interprets its input as datevecs instead. This is due to a design decision on Matlab’s part; for compatibility, Octave does not alter that interface.

To combine `datetime`s with datenums, you can convert the datenums to `datetime`s by calling `datetime.ofDatenum` or `datetime(x, 'ConvertFrom', 'datenum')`, or you can convert the `datetime`s to datenums by accessing its `dnums` field with `x.dnums`.

Examples:

    dt = datetime('2011-03-04')
    dn = datenum('2017-01-01')
    [dt dn]
        ⇒ error: datenum: expected date vector containing [YEAR, MONTH, DAY, HOUR, MINUTE, SECOND]
    [dt datetime.ofDatenum(dn)]
        ⇒ 04-Mar-2011   01-Jan-2017

Also, if you have a zoned `datetime`, you can’t combine it with a datenum, because datenums do not carry time zone information.

### 4.2 Time Zones ¶

Tablicious has support for representing dates in time zones and for converting between time zones.

A `datetime` may be "zoned" or "zoneless". A zoneless `datetime` does not have a time zone associated with it. This is represented by an empty `TimeZone` property on the `datetime` object. A zoneless `datetime` represents the local time in some unknown time zone, and assumes a continuous time scale (no DST shifts).

A zoned `datetime` is associated with a time zone. It is represented by having the time zone’s IANA zone identifier (e.g. `'UTC'` or `'America/New_York'`) in its `TimeZone` property. A zoned `datetime` represents the local time in that time zone.

By default, the `datetime` constructor creates unzoned `datetime`s. To make a zoned `datetime`, either pass the `'TimeZone'` option to the constructor, or set the `TimeZone` property after object creation. Setting the `TimeZone` property on a zoneless `datetime` declares that it’s a local time in that time zone. Setting the `TimeZone` property on a zoned `datetime` turns it back into a zoneless `datetime` without changing the local time it represents.

You can tell a zoned from a zoneless time zone in the object display because the time zone is included for zoned `datetime`s.

    % Create an unzoned datetime
    d = datetime('2011-03-04 06:00:00')
        ⇒  04-Mar-2011 06:00:00

    % Create a zoned datetime
    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
        ⇒  04-Mar-2011 06:00:00 America/New_York
    % This is equivalent
    d_ny = datetime('2011-03-04 06:00:00');
    d_ny.TimeZone = 'America/New_York'
        ⇒  04-Mar-2011 06:00:00 America/New_York

    % Convert it to Chicago time
    d_chi.TimeZone = 'America/Chicago'
        ⇒  04-Mar-2011 05:00:00 America/Chicago

When you combine two zoned `datetime`s via concatenation, assignment, or arithmetic, if their time zones differ, they are converted to the time zone of the left-hand input.

    d_ny = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/New_York')
    d_la = datetime('2011-03-04 06:00:00', 'TimeZone', 'America/Los_Angeles')
    d_la - d_ny
        ⇒ 03:00:00

You cannot combine a zoned and an unzoned `datetime`. This results in an error being raised.

> Warning: Normalization of "nonexistent" times (like between 02:00 and 03:00 on a "spring forward" DST change day) is not implemented yet. The results of converting a zoneless local time into a time zone where that local time did not exist are currently undefined.

#### 4.2.1 Defined Time Zones ¶

Tablicious’s time zone data is drawn from the IANA Time Zone Database, also known as the “Olson Database”. Tablicious includes a copy of this database in its distribution so it can work on Windows, which does not supply it like Unix systems do.

You can use the `timezones` function to list the time zones known to Tablicious. These will be all the time zones in the IANA database on your system (for Linux and macOS) or in the IANA time zone database redistributed with Tablicious (for Windows).

> Note: The IANA Time Zone Database only covers dates from about the year 1880 to 2038. Converting time zones for `datetime`s outside that range is currently unimplemented. (Tablicious needs to add support for proleptic POSIX time zone rules, which are used to govern behavior outside that date range.)

### 4.3 Durations ¶

#### 4.3.1 `duration` Class ¶

A `duration` represents a period of time in fixed-length seconds (or minutes, hours, or whatever you want to measure it in.)

A `duration` has a resolution of about a nanosecond for typical dates. The underlying representation is a `double` representing the number of days elapsed, similar to a datenum, except it’s interpreted as relative to some other reference point you provide, instead of being relative to the Matlab/Octave epoch.

You can add or subtract a `duration` to a `datetime` to get another `datetime`. You can also add or subtract `durations` to each other.

#### 4.3.2 `calendarDuration` Class ¶

A `calendarDuration` represents a period of time in variable-length calendar components. For example, years and months can have varying numbers of days, and days in time zones with Daylight Saving Time have varying numbers of hours. A `calendarDuration` does arithmetic with "whole" calendar periods.

`calendarDuration`s and `duration`s cannot be directly combined, because they are not semantically equivalent. (This may be relaxed in the future to allow `duration`s to be interpreted as numbers of days when combined with `calendarDuration`s.)

    d = datetime('2011-03-04 00:00:00')
        ⇒ 04-Mar-2011
    cdur = calendarDuration(1, 3, 0)
        ⇒ 1y 3mo
    d2 = d + cdur
        ⇒ 04-Jun-2012

## 5 Validation Functions ¶

Tablicious provides several validation functions which can be used to check properties of function arguments, variables, object properties, and other expressions. These can be used to express invariants in your program and catch problems due to input errors, incorrect function usage, or other bugs.

These validation functions are named following the pattern `mustBeXxx`, where `Xxx` is some property of the input it is testing. Validation functions may check the type, size, or other aspects of their inputs.

The most common place for validation functions to be used will probably be at the beginning of functions, to check the input arguments and ensure that the contract of the function is not being violated. If in the future Octave gains the ability to declaratively express object property constraints, they will also be of use there.

Be careful not to get too aggressive with the use of validation functions: while using them can make sure invariants are followed and your program is correct, they also reduce the code’s ability to make use of duck typing, reducing its flexibility. Whether you want to make this trade-off is a design decision you will have to consider.

When a validation function’s condition is violated, it raises an error that includes a description of the violation in the error message. This message will include a label for the input that describes what is being tested. By default, this label is initialized with `inputname()`, so when you are calling a validator on a function argument or variable, you will generally not need to supply a label. But if you’re calling it on an object property or an expression more complex than a simple variable reference, the validator cannot automatically detect the input name for use in the label. In this case, make use of the optional trailing argument(s) to the functions to manually supply a label for the value being tested.

    % Validation of a simple variable does not need a label
    mustBeScalar (x);
    % Validation of a field or property reference does need a label
    mustBeScalar (this.foo, 'this.foo');

## 6 Example Data Sets ¶

Tablicious comes with several example data sets that you can use to explore how its functions and objects work. These are accessed through the `tblish.datasets` and `tblish.dataset` classes.

To see a list of the available data sets, run `tblish.datasets.list()`. Then to load one of the example data sets, run `tblish.datasets.load('examplename')`. For example:

    tblish.datasets.list
    t = tblish.datasets.load('cupcake')

You can also load it by calling `tblish.dataset.<name>`. This does the same thing. For example:

    t = tblish.dataset.cupcake

When you load a data set, it either returns all its data in a single variable (if you capture it), or loads its data into one or more variables in your workspace (if you call it with no outputs).

Each example data set comes with help text that describes the data set and provides examples of how to work with it. This help is found using the `doc` command on `tblish.dataset.<name>`, where <name> is the name of the data set.

For example:

    doc tblish.dataset.cupcake

(The command `help tblish.dataset.<name>` ought to work too, but it currently doesn’t. This may be due to an issue with Octave’s `help` command.)

### 6.1 Data Sets from R ¶

Many of Tablicious’ example data sets are based on the example datasets found in R’s `datasets` package. R can be found at https://www.r-project.org/, and documentation for its `datasets` is at https://rdrr.io/r/datasets/datasets-package.html. Thanks to the R developers for producing the original data sets here.

Tablicious’ examples’ code tries to replicate the R examples, so it can be useful to compare the two of them if you are moving from one language to another.

Core Octave currently lacks some of the plotting features found in the R examples, such as LOWESS smoothing and linear model characteristic plots, so you will just find “TODO” placeholders for these in Tablicious’ example code.

## 7 Missing Functionality ¶

Tablicious is based on Matlab’s table and date/time APIs and supports some of their major functionality. But not all of it is implemented yet. The missing parts are currently:

- File I/O like `readtable()` and `writetable()`
- `summary()` `categorical`
- Assignment to table variables using `.`-indexing
- `timetable`
- POSIX time zone support for years outside the IANA time zone database coverage
- Week-of-year (ISO calendar) calculations
- Various `'ConvertFrom'` forms for `datetime` and `duration` constructors
- Support for LDML formatting for `datetime`
- Various functions:
  - `between`
  - `caldiff`
  - `dateshift`
  - `week`
- `isdst`, `isweekend`
- `calendarDuration.split`
- `duration.Format` support
- Moving window methods in `fillmissing`
- `UTCOffset` and `DSTOffset` fields in the output of `timezones()`
- Plotting support

It is the author’s hope that many these will be implemented some day.

These areas of missing functionality are tracked on the Tablicious issue tracker at https://github.com/apjanke/octave-tablicious/issues and https://github.com/users/apjanke/projects/3.

## 8 API Reference ¶

### 8.1 API by Category ¶

#### 8.1.1 Tables ¶

table

  Tabular data array containing multiple columnar variables.

  See table.

array2table

  Convert an array to a table.

  See array2table.

cell2table

  Convert a cell array to a table.

  See cell2table.

struct2table

  Convert struct to a table.

  See struct2table.

tableOuterFillValue

  See tableOuterFillValue.

vartype

  Filter by variable type for use in suscripting.

  See vartype.

istable

  True if input is a ‘table’ array or other table-like type, false otherwise.

  See istable.

istimetable

  True if input is a ‘timetable’ array or other timetable-like type, false otherwise.

  See istimetable.

istabular

  True if input is eitehr a ‘table’ or ‘timetable’ array, or an object like them.

  See istabular.

tblish.evalWithTableVars

  Evaluate an expression against a table array’s variables.

  See tblish.evalWithTableVars.

tblish.table.grpstats

  Statistics by group for a table array.

  See tblish.table.grpstats.

#### 8.1.2 Strings and Categoricals ¶

string

  A string array of Unicode strings.

  See string.

NaS

  “Not-a-String".

  See NaS.

contains

  Test if strings contain a pattern.

  See contains.

dispstrs

  Display strings for array.

  See dispstrs.

categorical

  Categorical variable array.

  See categorical.

iscategorical

  True if input is a ‘categorical’ array, false otherwise.

  See iscategorical.

NaC

  “Not-a-Categorical".

  See NaC.

discretize

  Group data into discrete bins or categories.

  See discretize.

#### 8.1.3 Dates and Times ¶

datetime

  Represents points in time using the Gregorian calendar.

  See datetime.

NaT

  “Not-a-Time”.

  See NaT.

todatetime

  Convert input to a Tablicious datetime array, with convenient interface.

  See todatetime.

localdate

  Represents a complete day using the Gregorian calendar.

  See localdate.

isdatetime

  True if input is a ‘datetime’ array, false otherwise.

  See isdatetime.

calendarDuration

  Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time.

  See calendarDuration.

iscalendarduration

  True if input is a ‘calendarDuration’ array, false otherwise.

  See iscalendarduration.

calmonths

  Create a ‘calendarDuration’ that is a given number of calendar months long.

  See calmonths.

calyears

  Construct a ‘calendarDuration’ a given number of years long.

  See calyears.

days

  Duration in days.

  See days.

duration

  Represents durations or periods of time as an amount of fixed-length time (i.e.

  See duration.

hours

  Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

  See hours.

isduration

  True if input is a ‘duration’ array, false otherwise.

  See isduration.

milliseconds

  Create a ‘duration’ X milliseconds long, or get the milliseconds in a ‘duration’ X.

  See milliseconds.

minutes

  Create a ‘duration’ X hours long, or get the hours in a ‘duration’ X.

  See minutes.

seconds

  Create a ‘duration’ X seconds long, or get the seconds in a ‘duration’ X.

  See seconds.

timezones

  List all the time zones defined on this system.

  See timezones.

years

  Create a ‘duration’ X years long, or get the years in a ‘duration’ X.

  See years.

#### 8.1.4 Missing Data ¶

missing

  Generic auto-converting missing value.

  See missing.

isnanny

  Test if elements are NaN or NaN-like

  See isnanny.

eqn

  Determine element-wise equality, treating NaNs as equal

  See eqn.

#### 8.1.5 Validation Functions ¶

mustBeA

  See mustBeA.

mustBeCellstr

  See mustBeCellstr.

mustBeCharvec

  See mustBeCharvec.

mustBeFinite

  See mustBeFinite.

mustBeInteger

  See mustBeInteger.

mustBeMember

  See mustBeMember.

mustBeNonempty

  See mustBeNonempty.

mustBeNumeric

  See mustBeNumeric.

mustBeReal

  See mustBeReal.

mustBeSameSize

  See mustBeSameSize.

mustBeScalar

  See mustBeScalar.

mustBeScalarLogical

  See mustBeScalarLogical.

mustBeVector

  See mustBeVector.

#### 8.1.6 Miscellaneous ¶

colvecfun

  Apply a function to column vectors in array.

  See colvecfun.

dispstrs

  Display strings for array.

  See dispstrs.

head

  Get first K rows of an array.

  See head.

isfile

  See isfile.

isfolder

  See isfolder.

pp

  Alias for prettyprint, for interactive use.

  See pp.

scalarexpand

  Expand scalar inputs to match size of non-scalar inputs.

  See scalarexpand.

size2str

  Format an array size for display.

  See size2str.

splitapply

  Split data into groups and apply function.

  See splitapply.

tail

  Get last K rows of an array.

  See tail.

vecfun

  Apply function to vectors in array along arbitrary dimension.

  See vecfun.

tblish.sizeof2

  Approximate size of an array in bytes, with object support.

  See tblish.sizeof2.

#### 8.1.7 Example Datasets ¶

tblish.datasets

  Example dataset collection.

  See tblish.datasets.

tblish.dataset

  The ‘tblish.dataset’ class provides convenient access to the various datasets included with Tablicious.

  See tblish.dataset.

#### 8.1.8 Example Code ¶

tblish.examples.coplot

  Conditioning plot.

  See tblish.examples.coplot.

tblish.examples.plot_pairs

  Plot pairs of variables against each other.

  See tblish.examples.plot_pairs.

tblish.examples.SpDb

  The classic Suppliers-Parts example database.

  See tblish.examples.SpDb.

### 8.2 API Alphabetically ¶

#### 8.2.1 array2table ¶

Function: `out =` array2table `(c)` ¶
Function: `out =` array2table `(…, `'VariableNames'`, VariableNames)` ¶
Function: `out =` array2table `(…, `'RowNames'`, RowNames)` ¶

  Convert an array to a table.

  Converts a 2-D array to a table, with columns in the array becoming variables in the output table. This is typically used on numeric arrays, but it can be applied to any type of array.

  You may not want to use this on cell arrays, though, because you will end up with a table that has all its variables of type cell. If you use `cell2table` instead, columns of the cell array which can be condensed into primitive arrays will be. With `array2table`, they won’t be.

  See also: cell2table, table, struct2table

#### 8.2.2 calendarDuration ¶

Class: calendarDuration ¶

  Durations of time using variable-length calendar periods, such as days, months, and years, which may vary in length over time. (For example, a calendar month may have 28, 30, or 31 days.)

Instance Variable of `calendarDuration`: ``char`` Sign ¶

  The sign (1 or -1) of this duration, which indicates whether it is a positive or negative span of time.

Instance Variable of `calendarDuration`: ``char`` Years ¶

  The number of whole calendar years in this duration. Must be integer-valued.

Instance Variable of `calendarDuration`: ``char`` Months ¶

  The number of whole calendar months in this duration. Must be integer-valued.

Instance Variable of `calendarDuration`: ``char`` Days ¶

  The number of whole calendar days in this duration. Must be integer-valued.

Instance Variable of `calendarDuration`: ``char`` Hours ¶

  The number of whole hours in this duration. Must be integer-valued.

Instance Variable of `calendarDuration`: ``char`` Minutes ¶

  The number of whole minutes in this duration. Must be integer-valued.

Instance Variable of `calendarDuration`: ``char`` Seconds ¶

  The number of seconds in this duration. May contain fractional values.

Instance Variable of `calendarDuration`: ``char`` Format ¶

  The format to display this `calendarDuration` in. Currently unsupported.

  This is a single value that applies to the whole array.

#### 8.2.2.1 calendarDuration.calendarDuration ¶

Constructor: `obj =` calendarDuration `()` ¶

  Constructs a new scalar `calendarDuration` of zero elapsed time.

Constructor: `obj =` calendarDuration `(Y, M, D)` ¶
Constructor: `obj =` calendarDuration `(Y, M, D, H, MI, S)` ¶

  Constructs new `calendarDuration` arrays based on input values.

#### 8.2.2.2 calendarDuration.dispstrs ¶

Method: `out =` dispstrs `(obj)` ¶

  Get display strings for each element of obj.

  Returns a cellstr the same size as obj.

#### 8.2.2.3 calendarDuration.ismissing ¶

Method: `out =` ismissing `(obj)` ¶

  True if input elements are missing.

  This is equivalent to `ismissing`.

  Returns logical array the same size as obj.

#### 8.2.2.4 calendarDuration.isnan ¶

Method: `out =` isnan `(obj)` ¶

  True if input elements are NaN.

  This is equivalent to `ismissing`, and is provided for compatibility and polymorphic programming purposes.

  Returns logical array the same size as obj.

#### 8.2.2.5 calendarDuration.minus ¶

Method: `out =` times `(A, B)` ¶

  Subtraction: Subtracts one `calendarDuration` from another.

  Returns a `calendarDuration`.

#### 8.2.2.6 calendarDuration.plus ¶

Method: `out =` plus `(A, B)` ¶

  Addition: add two `calendarDuration`s.

  All the calendar elements (properties) of the two inputs are added together. No normalization is done across the elements, aside from the normalization of NaNs.

  If B is numeric, it is converted to a `calendarDuration` using `calendarDuration.ofDays`.

  Returns a `calendarDuration`.

#### 8.2.2.7 calendarDuration.times ¶

Method: `out =` times `(obj, B)` ¶

  Multiplication: Multiplies a `calendarDuration` by a numeric factor.

  Returns a `calendarDuration`.

#### 8.2.2.8 calendarDuration.uminus ¶

Method: `out =` uminus `(obj)` ¶

  Unary minus. Negates the sign of obj.

#### 8.2.3 calmonths ¶

Function File: `out =` calmonths `(x)` ¶

  Create a `calendarDuration` that is a given number of calendar months long.

  Input x is a numeric array specifying the number of calendar months.

  This is a shorthand alternative to calling the `calendarDuration` constructor with `calendarDuration(0, x, 0)`.

  Returns a new `calendarDuration` object of the same size as x.

  See calendarDuration.

#### 8.2.4 calyears ¶

Function: `out =` calyears `(x)` ¶

  Construct a `calendarDuration` a given number of years long.

  This is a shorthand for calling `calendarDuration(x, 0, 0)`.

  See calendarDuration.

#### 8.2.5 categorical ¶

Class: categorical ¶

  Categorical variable array.

  A `categorical` array represents an array of values of a categorical variable. Each `categorical` array stores the element values along with a list of the categories, and indicators of whether the categories are ordinal (that is, they have a meaningful mathematical ordering), and whether the set of categories is protected (preventing new categories from being added to the array).

  In addition to the categories defined in the array, a categorical array may have elements of "undefined" value. This is not considered a category; rather, it is the absence of any known value. It is analagous to a `NaN` value.

  This class is not fully implemented yet. Missing stuff:

  - gt, ge, lt, le
  - Ordinal support in general
  - countcats
  - summary

Instance Variable of `categorical`: ``uint16`` code ¶

  The numeric codes of the array element values. These are indexes into the `cats` category list.

  This is a planar property.

Instance Variable of `categorical`: ``logical`` tfMissing ¶

  A logical mask indicating whether each element of the array is missing (that is, undefined).

  This is a planar property.

Instance Variable of `categorical`: ``cellstr`` cats ¶

  The names of the categories in this array. This is the list into which the `code` values are indexes.

Instance Variable of `categorical`: ``scalar_logical`` isOrdinal ¶

  A scalar logical indicating whether the categories in this array have an ordinal relationship.

#### 8.2.5.1 categorical.addcats ¶

Method: `out =` addcats `(obj, newcats)` ¶

  Add categories to categorical array.

  Adds the specified categories to obj, without changing any of its values.

  newcats is a cellstr listing the category names to add to obj.

#### 8.2.5.2 categorical.categorical ¶

Constructor: `obj =` categorical `()` ¶

  Constructs a new scalar categorical whose value is undefined.

Constructor: `obj =` categorical `(vals)` ¶
Constructor: `obj =` categorical `(vals, valueset)` ¶
Constructor: `obj =` categorical `(vals, valueset, category_names)` ¶
Constructor: `obj =` categorical `(…, `'Ordinal'`, Ordinal)` ¶
Constructor: `obj =` categorical `(…, `'Protected'`, Protected)` ¶

  Constructs a new categorical array from the given values.

  vals is the array of values to convert to categoricals.

  valueset is the set of all values from which vals is drawn. If omitted, it defaults to the unique values in vals.

  category_names is a list of category names corresponding to valueset. If omitted, it defaults to valueset, converted to strings.

  Ordinal is a logical indicating whether the category values in obj have a numeric ordering relationship. Defaults to false.

  Protected indicates whether obj should be protected, which prevents the addition of new categories to the array. Defaults to false.

#### 8.2.5.3 categorical.categories ¶

Method: `out =` categories `(obj)` ¶

  Get a list of the categories in obj.

  Gets a list of the categories in obj, identified by their category names.

  Returns a cellstr column vector.

#### 8.2.5.4 categorical.cellstr ¶

Method: `out =` cellstr `(obj)` ¶

  Convert to cellstr.

  Converts obj to a cellstr array. The strings will be the category names for corresponding values, or `''` for undefined values.

  Returns a cellstr array the same size as obj.

#### 8.2.5.5 categorical.dispstrs ¶

Method: `out =` dispstrs `(obj)` ¶

  Display strings.

  Gets display strings for each element in obj. The display strings are either the category string, or `'<undefined>'` for undefined values.

  Returns a cellstr array the same size as obj.

#### 8.2.5.6 categorical.double ¶

Method: `out =` double `(obj)` ¶

  Convert to double array, by getting the underlying code values.

  Converts obj to a string array. The doubles will be the underlying numeric code values of obj, or `NaN` for undefined values.

  The numeric code values of two different categorical arrays do *not* necessarily correspond to the same string values, and can *not* be meaningfully compared for equality or ordering.

  Returns a `double` array the same size as obj.

#### 8.2.5.7 categorical.iscategory ¶

Method: `out =` iscategory `(obj, catnames)` ¶

  Test whether input is a category on a categorical array.

  catnames is a cellstr listing the category names to check against obj.

  Returns a logical array the same size as catnames.

#### 8.2.5.8 categorical.ismissing ¶

Method: `out =` ismissing `(obj)` ¶

  Test whether elements are missing.

  For categorical arrays, undefined elements are considered to be missing.

  Returns a logical array the same size as obj.

#### 8.2.5.9 categorical.isnanny ¶

Method: `out =` isnanny `(obj)` ¶

  Test whethere elements are NaN-ish.

  Checks where each element in obj is NaN-ish. For categorical arrays, undefined values are considered NaN-ish; any other value is not.

  Returns a logical array the same size as obj.

#### 8.2.5.10 categorical.isordinal ¶

Method: `out =` isordinal `(obj)` ¶

  Whether obj is ordinal.

  Returns true if obj is ordinal (as determined by its `IsOrdinal` property), and false otherwise.

#### 8.2.5.11 categorical.isundefined ¶

Method: `out =` isundefined `(obj)` ¶

  Test whether elements are undefined.

  Checks whether each element in obj is undefined. "Undefined" is a special value defined by `categorical`. It is equivalent to a `NaN` or a `missing` value.

  Returns a logical array the same size as obj.

#### 8.2.5.12 categorical.mergecats ¶

Method: `out =` mergecats `(obj, oldcats)` ¶
Method: `out =` mergecats `(obj, oldcats, newcat)` ¶

  Merge multiple categories.

  Merges the categories oldcats into a single category. If newcat is specified, that new category is added if necessary, and all of oldcats are merged into it. newcat must be an existing category in obj if obj is ordinal.

  If newcat is not provided, all of odcats are merged into `oldcats{1}`.

#### 8.2.5.13 categorical.missing ¶

Static Method: `out =` categorical.missing `()` ¶
Static Method: `out =` categorical.missing `(sz)` ¶

  Create an array of missing (undefined) categoricals.

  Creates a categorical array whose elements are all missing (<undefined>).

  This is a convenience alias for categorical.undefined, so you can call it generically. It returns strictly the same results as calling categorical.undefined with the same arguments.

  Returns a categorical array.

  See also: categorical.undefined

#### 8.2.5.14 categorical.removecats ¶

Method: `out =` removecats `(obj)` ¶

  Removes all unused categories from obj. This is equivalent to `out = squeezecats (obj)`.

Method: `out =` removecats `(obj, oldcats)` ¶

  Remove categories from categorical array.

  Removes the specified categories from obj. Elements of obj whose values belonged to those categories are replaced with undefined.

  newcats is a cellstr listing the category names to add to obj.

#### 8.2.5.15 categorical.renamecats ¶

Method: `out =` renamecats `(obj, newcats)` ¶
Method: `out =` renamecats `(obj, oldcats, newcats)` ¶

  Rename categories.

  Renames some or all of the categories in obj, without changing any of its values.

#### 8.2.5.16 categorical.reordercats ¶

Method: `out =` reordercats `(obj)` ¶
Method: `out =` reordercats `(obj, newcats)` ¶

  Reorder categories.

  Reorders the categories in obj to match newcats.

  newcats is a cellstr that must be a reordering of obj’s existing category list. If newcats is not supplied, sorts the categories in alphabetical order.

#### 8.2.5.17 categorical.setcats ¶

Method: `out =` setcats `(obj, newcats)` ¶

  Set categories for categorical array.

  Sets the categories to use for obj. If any current categories are absent from the newcats list, current values of those categories become undefined.

#### 8.2.5.18 categorical.squeezecats ¶

Method: `out =` squeezecats `(obj)` ¶

  Remove unused categories.

  Removes all categories which have no corresponding values in obj’s elements.

  This is currently unimplemented.

#### 8.2.5.19 categorical.string ¶

Method: `out =` string `(obj)` ¶

  Convert to string array.

  Converts obj to a string array. The strings will be the category names for corresponding values, or <missing> for undefined values.

  Returns a `string` array the same size as obj.

#### 8.2.5.20 categorical.summary ¶

Method: summary `(obj)` ¶

  Display summary of array’s values.

  Displays a summary of the values in this categorical array. The output may contain info like the number of categories, number of undefined values, and frequency of each category.

#### 8.2.5.21 categorical.undefined ¶

Static Method: `out =` categorical.undefined `()` ¶
Static Method: `out =` categorical.undefined `(sz)` ¶

  Create an array of undefined categoricals.

  Creates a categorical array whose elements are all <undefined>.

  sz is the size of the array to create. If omitted or empty, creates a scalar.

  Returns a categorical array.

  See also: categorical.missing

#### 8.2.6 cell2table ¶

Function: `out =` cell2table `(c)` ¶
Function: `out =` cell2table `(…, `'VariableNames'`, VariableNames)` ¶
Function: `out =` cell2table `(…, `'RowNames'`, RowNames)` ¶

  Convert a cell array to a table.

  Converts a 2-dimensional cell matrix into a table. Each column in the input c becomes a variable in out. For columns that contain all scalar values of `cat`-compatible types, they are “popped out” of their cells and condensed into a homogeneous array of the contained type.

  See also: array2table, table, struct2table

#### 8.2.7 colvecfun ¶

Function: `out =` colvecfun `(fcn, x)` ¶

  Apply a function to column vectors in array.

  Applies the given function fcn to each column vector in the array x, by iterating over the indexes along all dimensions except dimension 1. Collects the function return values in an output array.

  fcn must be a function which takes a column vector and returns a column vector of the same size. It does not have to return the same type as x.

  Returns the result of applying fcn to each column in x, all concatenated together in the same shape as x.

#### 8.2.8 contains ¶

Function: `out =` colvecfun `(str, pattern)` ¶
Function: `out =` colvecfun `(…, `'IgnoreCase'`, IgnoreCase)` ¶

  Test if strings contain a pattern.

  Tests whether the given strings contain the given pattern(s).

  str (char, cellstr, or string) is a list of strings to compare against pattern.

  pattern (char, cellstr, or string) is a list of patterns to match. These are literal plain string patterns, not regex patterns. If more than one pattern is supplied, the return value is true if the string matched any of them.

  Returns a logical array of the same size as the string array represented by str.

  See also: startsWith, endsWith

#### 8.2.9 datetime ¶

Class: datetime ¶

  Represents points in time using the Gregorian calendar.

  The underlying values are doubles representing the number of days since the Matlab epoch of "January 0, year 0". This has a precision of around nanoseconds for typical times.

  A `datetime` array is an array of date/time values, with each element holding a complete date/time. The overall array may also have a TimeZone and a Format associated with it, which apply to all elements in the array.

  This is an attempt to reproduce the functionality of Matlab’s `datetime`. It also contains some Octave-specific extensions.

Instance Variable of `datetime`: ``double`` dnums ¶

  The underlying datenums that represent the points in time. These are always in UTC.

  This is a planar property: the size of `dnums` is the same size as the containing `datetime` array object.

Instance Variable of `datetime`: ``char`` TimeZone ¶

  The time zone this `datetime` array is in. Empty if this does not have a time zone associated with it (“unzoned”). The name of an IANA time zone if this does.

  Setting the `TimeZone` of a `datetime` array changes the time zone it is presented in for strings and broken-down times, but does not change the underlying UTC times that its elements represent.

Instance Variable of `datetime`: ``char`` Format ¶

  The format to display this `datetime` in. Currently unsupported.

#### 8.2.9.1 datetime.convertDatenumTimeZone ¶

Static Method: `out =` datetime.convertDatenumTimeZone `(dnum, fromZoneId, toZoneId)` ¶

  Convert a datenum from one time zone to another.

  dnum is a datenum array to convert.

  fromZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert from.

  toZoneId is a charvec containing the IANA Time Zone identifier for the time zone to convert to.

  Returns a datenum array the same size as dnum.

#### 8.2.9.2 datetime.datenum ¶

Method: `out =` datenum `(obj)` ¶

  Convert this to datenums that represent the same local time

  Returns double array of same size as this.

#### 8.2.9.3 datetime.datenum2posix ¶

Static Method: `out =` datetime.datenum2posix `(dnums)` ¶

  Converts Octave datenums to Unix dates.

  The input datenums are assumed to be in UTC.

  Returns a double, which may have fractional seconds.

#### 8.2.9.4 datetime.datestr ¶

Method: `out =` datestr `(obj)` ¶
Method: `out =` datestr `(obj, …)` ¶

  Format obj as date strings. Supports all arguments that core Octave’s `datestr` does.

  Returns date strings as a 2-D char array.

#### 8.2.9.5 datetime.datestrs ¶

Method: `out =` datestrs `(obj)` ¶
Method: `out =` datestrs `(obj, …)` ¶

  Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s `datestr` does.

  Returns a cellstr array the same size as obj.

#### 8.2.9.6 datetime.datestruct ¶

Method: `out =` datestruct `(obj)` ¶

  Converts this to a "datestruct" broken-down time structure.

  A "datestruct" is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, Day, Hour, Minute, and Second, each containing a double array the same size as the date array it represents.

  The values in the returned broken-down time are those of the local time in this’ defined time zone, if it has one.

  Returns a struct with fields Year, Month, Day, Hour, Minute, and Second. Each field contains a double array of the same size as this.

#### 8.2.9.7 datetime.datetime ¶

Constructor: `obj =` datetime `()` ¶

  Constructs a new scalar `datetime` containing the current local time, with no time zone attached.

Constructor: `obj =` datetime `(datevec)` ¶
Constructor: `obj =` datetime `(datestrs)` ¶
Constructor: `obj =` datetime `(in, `'ConvertFrom'`, inType)` ¶
Constructor: `obj =` datetime `(Y, M, D, H, MI, S)` ¶
Constructor: `obj =` datetime `(Y, M, D, H, MI, MS)` ¶
Constructor: `obj =` datetime `(…, `'Format'`, Format, `'InputFormat'`, InputFormat, `'Locale'`, InputLocale, `'PivotYear'`, PivotYear, `'TimeZone'`, TimeZone)` ¶

  Constructs a new `datetime` array based on input values.

#### 8.2.9.8 datetime.diff ¶

Method: `out =` diff `(obj)` ¶

  Differences between elements.

  Computes the difference between each successive element in obj, as a `duration`.

  Returns a `duration` array the same size as obj.

#### 8.2.9.9 datetime.dispstrs ¶

Method: `out =` dispstrs `(obj)` ¶

  Get display strings for each element of obj.

  Returns a cellstr the same size as obj.

#### 8.2.9.10 datetime.eq ¶

Method: `out =` eq `(A, B)` ¶

  True if A is equal to B. This defines the `==` operator for `datetime`s.

  Inputs are implicitly converted to `datetime` using the one-arg constructor or conversion method.

  Returns logical array the same size as obj.

#### 8.2.9.11 datetime.ge ¶

Method: `out =` ge `(A, B)` ¶

  True if A is greater than or equal to B. This defines the `>=` operator for `datetime`s.

  Inputs are implicitly converted to `datetime` using the one-arg constructor or conversion method.

  Returns logical array the same size as obj.

#### 8.2.9.12 datetime.gmtime ¶

Method: `out =` gmtime `(obj)` ¶

  Convert to TM_STRUCT structure in UTC time.

  Converts obj to a TM_STRUCT style structure array. The result is in UTC time. If obj is unzoned, it is assumed to be in UTC time.

  Returns a struct array in TM_STRUCT style.

#### 8.2.9.13 datetime.gt ¶

Method: `out =` gt `(A, B)` ¶

  True if A is greater than B. This defines the `>` operator for `datetime`s.

  Inputs are implicitly converted to `datetime` using the one-arg constructor or conversion method.

  Returns logical array the same size as obj.

#### 8.2.9.14 datetime.hms ¶

Method: `[h, m, s] =` hms `(obj)` ¶

  Get the Hour, Minute, and Second components of a obj.

  For zoned `datetime`s, these will be local times in the associated time zone.

  Returns double arrays the same size as `obj`.

#### 8.2.9.15 datetime.isbetween ¶

Method: `out =` isbetween `(obj, lower, upper)` ¶

  Tests whether the elements of obj are between lower and upper.

  All inputs are implicitly converted to `datetime` arrays, and are subject to scalar expansion.

  Returns a logical array the same size as the scalar expansion of the inputs.

#### 8.2.9.16 datetime.isnan ¶

Method: `out =` isnan `(obj)` ¶

  True if input elements are NaT. This is an alias for `isnat` to support type compatibility and polymorphic programming.

  Returns logical array the same size as obj.

#### 8.2.9.17 datetime.isnat ¶

Method: `out =` isnat `(obj)` ¶

  True if input elements are NaT.

  Returns logical array the same size as obj.

#### 8.2.9.18 datetime.le ¶

Method: `out =` le `(A, B)` ¶

  True if A is less than or equal toB. This defines the `<=` operator for `datetime`s.

  Inputs are implicitly converted to `datetime` using the one-arg constructor or conversion method.

  Returns logical array the same size as obj.

#### 8.2.9.19 datetime.linspace ¶

Method: `out =` linspace `(from, to, n)` ¶

  Linearly-spaced values in date/time space.

  Constructs a vector of `datetime`s that represent linearly spaced points starting at from and going up to to, with n points in the vector.

  from and to are implicitly converted to `datetime`s.

  n is how many points to use. If omitted, defaults to 100.

  Returns an n-long `datetime` vector.

#### 8.2.9.20 datetime.localtime ¶

Method: `out =` localtime `(obj)` ¶

  Convert to TM_STRUCT structure in UTC time.

  Converts obj to a TM_STRUCT style structure array. The result is a local time in the system default time zone. Note that the system default time zone is always used, regardless of what TimeZone is set on obj.

  If obj is unzoned, it is assumed to be in UTC time.

  Returns a struct array in TM_STRUCT style.

  Example:

      dt = datetime;
      dt.TimeZone = datetime.SystemTimeZone;
      tm_struct = localtime (dt);

#### 8.2.9.21 datetime.lt ¶

Method: `out =` lt `(A, B)` ¶

  True if A is less than B. This defines the `<` operator for `datetime`s.

  Inputs are implicitly converted to `datetime` using the one-arg constructor or conversion method.

  Returns logical array the same size as obj.

#### 8.2.9.22 datetime.minus ¶

Method: `out =` minus `(A, B)` ¶

  Subtraction (`-` operator). Subtracts a `duration`, `calendarDuration` or numeric B from a `datetime` A, or subtracts two `datetime`s from each other.

  If both inputs are `datetime`, then the output is a `duration`. Otherwise, the output is a `datetime`.

  Numeric B inputs are implicitly converted to `duration` using `duration.ofDays`.

  Returns an array the same size as A.

#### 8.2.9.23 datetime.NaT ¶

Static Method: `out =` datetime.NaT `()` ¶
Static Method: `out =` datetime.NaT `(sz)` ¶

  “Not-a-Time”: Creates NaT-valued arrays.

  Constructs a new `datetime` array of all `NaT` values of the given size. If no input sz is given, the result is a scalar `NaT`.

  `NaT` is the `datetime` equivalent of `NaN`. It represents a missing or invalid value. `NaT` values never compare equal to, greater than, or less than any value, including other `NaT`s. Doing arithmetic with a `NaT` and any other value results in a `NaT`.

#### 8.2.9.24 datetime.ne ¶

Method: `out =` ne `(A, B)` ¶

  True if A is not equal to B. This defines the `!=` operator for `datetime`s.

  Inputs are implicitly converted to `datetime` using the one-arg constructor or conversion method.

  Returns logical array the same size as obj.

#### 8.2.9.25 datetime.ofDatenum ¶

Static Method: `obj =` datetime.ofDatenum `(dnums)` ¶

  Converts a datenum array to a datetime array.

  Returns an unzoned `datetime` array of the same size as the input.

#### 8.2.9.26 datetime.ofDatestruct ¶

Static Method: `obj =` datetime.ofDatestruct `(dstruct)` ¶

  Converts a datestruct to a datetime array.

  A datestruct is a special struct format used by Tablicious that has fields Year, Month, Day, Hour, Minute, and Second. It is not a standard Octave datatype.

  Returns an unzoned `datetime` array.

#### 8.2.9.27 datetime.plus ¶

Method: `out =` plus `(A, B)` ¶

  Addition (`+` operator). Adds a `duration`, `calendarDuration`, or numeric B to a `datetime` A.

  A must be a `datetime`.

  Numeric B inputs are implicitly converted to `duration` using `duration.ofDays`.

  Returns `datetime` array the same size as A.

#### 8.2.9.28 datetime.posix2datenum ¶

Static Method: `dnums =` datetime.posix2datenum `(pdates)` ¶

  Converts POSIX (Unix) times to datenums

  Pdates (numeric) is an array of POSIX dates. A POSIX date is the number of seconds since January 1, 1970 UTC, excluding leap seconds. The output is implicitly in UTC.

#### 8.2.9.29 datetime.posixtime ¶

Method: `out =` posixtime `(obj)` ¶

  Converts this to POSIX time values (seconds since the Unix epoch)

  Converts this to POSIX time values that represent the same time. The returned values will be doubles that may include fractional second values. POSIX times are, by definition, in UTC.

  Returns double array of same size as this.

#### 8.2.9.30 datetime.proxyKeys ¶

Method: `[keysA, keysB] =` proxyKeys `(a, b)` ¶

  Computes proxy key values for two datetime arrays. Proxy keys are numeric values whose rows have the same equivalence relationships as the elements of the inputs.

  This is primarily for Tablicious’s internal use; users will typically not need to call it or know how it works.

  Returns two 2-D numeric matrices of size n-by-k, where n is the number of elements in the corresponding input.

#### 8.2.9.31 datetime.timeofday ¶

Method: `out =` timeofday `(obj)` ¶

  Get the time of day (elapsed time since midnight).

  For zoned `datetime`s, these will be local times in the associated time zone.

  Returns a `duration` array the same size as `obj`.

#### 8.2.9.32 datetime.week ¶

Method: `out =` week `(obj)` ¶

  Get the week of the year.

  This method is unimplemented.

#### 8.2.9.33 datetime.ymd ¶

Method: `[y, m, d] =` ymd `(obj)` ¶

  Get the Year, Month, and Day components of obj.

  For zoned `datetime`s, these will be local times in the associated time zone.

  Returns double arrays the same size as `obj`.

#### 8.2.9.34 datetime.ymdhms ¶

Method: `[y, m, d, h, mi, s] =` ymdhms `(obj)` ¶

  Get the Year, Month, Day, Hour, Minute, and Second components of a obj.

  For zoned `datetime`s, these will be local times in the associated time zone.

  Returns double arrays the same size as `obj`.

#### 8.2.10 days ¶

Function: `out =` days `(x)` ¶

  Duration in days.

  If x is numeric, then out is a `duration` array in units of fixed-length 24-hour days, with the same size as x.

  If x is a `duration`, then returns a `double` array the same size as x indicating the number of fixed-length days that each duration is.

#### 8.2.11 discretize ¶

Function: `[Y, E] =` discretize `(X, n)` ¶
Function: `[Y, E] =` discretize `(X, edges)` ¶
Function: `[Y, E] =` discretize `(X, dur)` ¶
Function: `[Y, E] =` discretize `(…, `'categorical'`)` ¶
Function: `[Y, E] =` discretize `(…, `'IncludedEdge'`, IncludedEdge)` ¶

  Group data into discrete bins or categories.

  n is the number of bins to group the values into.

  edges is an array of edge values defining the bins.

  dur is a `duration` value indicating the length of time of each bin.

  If `'categorical'` is specified, the resulting values are a `categorical` array instead of a numeric array of bin indexes.

  Returns: Y - the bin index or category of each value from X E - the list of bin edge values

#### 8.2.12 dispstrs ¶

Function: `out =` dispstrs `(x)` ¶

  Display strings for array.

  Gets the display strings for each element of x. The display strings should be short, one-line, human-presentable strings describing the value of that element.

  The default implementation of `dispstrs` can accept input of any type, and has decent implementations for Octave’s standard built-in types, but will have opaque displays for most user-defined objects.

  This is a polymorphic method that user-defined classes may override with their own custom display that is more informative.

  Returns a cell array the same size as x.

#### 8.2.13 duration ¶

Class: duration ¶

  Represents durations or periods of time as an amount of fixed-length time (i.e. fixed-length seconds). It does not care about calendar things like months and days that vary in length over time.

  This is an attempt to reproduce the functionality of Matlab’s `duration`. It also contains some Octave-specific extensions.

  Duration values are stored as double numbers of days, so they are an approximate type. In display functions, by default, they are displayed with millisecond precision, but their actual precision is closer to nanoseconds for typical times.

Instance Variable of `duration`: ``double`` days ¶

  The underlying datenums that represent the durations, as number of (whole and fractional) days. These are uniform 24-hour days, not calendar days.

  This is a planar property: the size of `days` is the same size as the containing `duration` array object.

Instance Variable of `duration`: ``char`` Format ¶

  The format to display this `duration` in. Currently unsupported.

#### 8.2.13.1 duration.char ¶

Method: `out =` char `(obj)` ¶

  Convert to char. The contents of the strings will be the same as returned by `dispstrs`.

  This is primarily a convenience method for use on scalar objs.

  Returns a 2-D char array with one row per element in obj.

#### 8.2.13.2 duration.dispstrs ¶

Method: `out =` duration `(obj)` ¶

  Get display strings for each element of obj.

  Returns a cellstr the same size as obj.

#### 8.2.13.3 duration.hours ¶

Method: `out =` hours `(obj)` ¶

  Equivalent number of hours.

  Gets the number of fixed-length 60-minute hours that is equivalent to this duration.

  Returns double array the same size as obj.

#### 8.2.13.4 duration.linspace ¶

Method: `out =` linspace `(from, to, n)` ¶

  Linearly-spaced values in time duration space.

  Constructs a vector of `duration`s that represent linearly spaced points starting at from and going up to to, with n points in the vector.

  from and to are implicitly converted to `duration`s.

  n is how many points to use. If omitted, defaults to 100.

  Returns an n-long `datetime` vector.

#### 8.2.13.5 duration.milliseconds ¶

Method: `out =` milliseconds `(obj)` ¶

  Equivalent number of milliseconds.

  Gets the number of milliseconds that is equivalent to this duration.

  Returns double array the same size as obj.

#### 8.2.13.6 duration.minutes ¶

Method: `out =` minutes `(obj)` ¶

  Equivalent number of minutes.

  Gets the number of fixed-length 60-second minutes that is equivalent to this duration.

  Returns double array the same size as obj.

#### 8.2.13.7 duration.ofDays ¶

Static Method: `obj =` duration.ofDays `(dnums)` ¶

  Converts a double array representing durations in whole and fractional days to a `duration` array. This is the method that is used for implicit conversion of numerics in many cases.

  Returns a `duration` array of the same size as the input.

#### 8.2.13.8 duration.seconds ¶

Method: `out =` seconds `(obj)` ¶

  Equivalent number of seconds.

  Gets the number of seconds that is equivalent to this duration.

  Returns double array the same size as obj.

#### 8.2.13.9 duration.years ¶

Method: `out =` years `(obj)` ¶

  Equivalent number of years.

  Gets the number of fixed-length 365.2425-day years that is equivalent to this duration.

  Returns double array the same size as obj.

#### 8.2.14 eqn ¶

Function: `out =` eqn `(A, B)` ¶

  Determine element-wise equality, treating NaNs as equal

  out = eqn (A, B)

  `eqn` is just like `eq` (the function that implements the `==` operator), except that it considers NaN and NaN-like values to be equal. This is the element-wise equivalent of `isequaln`.

  `eqn` uses `isnanny` to test for NaN and NaN-like values, which means that NaNs and NaTs are considered to be NaN-like, and string arrays’ “missing” and categorical objects’ “undefined” values are considered equal, because they are NaN-ish.

  Developer’s note: the name “`eqn`” is a little unfortunate, because “eqn” could also be an abbreviation for “equation”. But this name follows the `isequaln` pattern of appending an “n” to the corresponding non-NaN-equivocating function.

  See also: `eq`, `isequaln`, isnanny

#### 8.2.15 head ¶

Function: `out =` head `(A)` ¶
Function: `out =` head `(A, k)` ¶

  Get first K rows of an array.

  Returns the array A, subsetted to its first k rows. This means subsetting it to the first `(min (k, size (A, 1)))` elements along dimension 1, and leaving all other dimensions unrestricted.

  A is the array to subset.

  k is the number of rows to get. k defaults to 8 if it is omitted or empty.

  If there are less than k rows in A, returns all rows.

  Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

  See also: tail

#### 8.2.16 hours ¶

Function File: `out =` hours `(x)` ¶

  Create a `duration` x hours long, or get the hours in a `duration` x.

  If input is numeric, returns a `duration` array that is that many hours in time.

  If input is a `duration`, converts the `duration` to a number of hours.

  Returns an array the same size as x.

#### 8.2.17 iscalendarduration ¶

Function: `out =` iscalendarduration `(x)` ¶

  True if input is a `calendarDuration` array, false otherwise.

  Respects `iscalendarduration` override methods on user-defined classes, even if they do not inherit from `calendarDuration` or were known to Tablicious at authoring time.

  Returns a scalar logical.

#### 8.2.18 iscategorical ¶

Function: `out =` iscategorical `(x)` ¶

  True if input is a `categorical` array, false otherwise.

  Respects `iscategorical` override methods on user-defined classes, even if they do not inherit from `categorical` or were known to Tablicious at authoring time.

  Returns a scalar logical.

#### 8.2.19 isdatetime ¶

Function: `out =` isdatetime `(x)` ¶

  True if input is a `datetime` array, false otherwise.

  Respects `isdatetime` override methods on user-defined classes, even if they do not inherit from `datetime` or were known to Tablicious at authoring time.

  Returns a scalar logical.

#### 8.2.20 isduration ¶

Function: `out =` isduration `(x)` ¶

  True if input is a `duration` array, false otherwise.

  Respects `isduration` override methods on user-defined classes, even if they do not inherit from `duration` or were known to Tablicious at authoring time.

  Returns a scalar logical.

#### 8.2.21 isfile ¶

Not documented

#### 8.2.22 isfolder ¶

Not documented

#### 8.2.23 isnanny ¶

Function: `out =` isnanny `(X)` ¶

  Test if elements are NaN or NaN-like

  Tests if input elements are NaN, NaT, or otherwise NaN-like. This is true if `isnan()` or `isnat()` returns true, and is false for types that do not support `isnan()` or `isnat()`.

  This function only exists because:

  1. Matlab decided to call their NaN values for datetime “NaT” instead, and test for them with a different “isnat()” function, and
  2. isnan() errors out for some types that do not support isnan(), like cells.

  `isnanny()` smooths over those differences so you can call it polymorphically on any input type. Hopefully.

  Under normal operation, `isnanny()` should not throw an error for any type or value of input.

  See also: ismissing, `isnan`, `isnat`, eqn, `isequaln`

#### 8.2.24 istable ¶

Function: `out =` istable `(x)` ¶

  True if input is a `table` array or other table-like type, false otherwise.

  Respects `istable` override methods on user-defined classes, even if they do not inherit from `table` or were known to Tablicious at authoring time.

  User-defined classes should only override `istable` to return true if they conform to the `table` public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

  Returns a scalar logical.

#### 8.2.25 istabular ¶

Function: `out =` istabular `(x)` ¶

  True if input is eitehr a `table` or `timetable` array, or an object like them.

  Respects `istable` and `istimetable` override methods on user-defined classes, even if they do not inherit from `table` or were known to Tablicious at authoring time.

  Returns a scalar logical.

#### 8.2.26 istimetable ¶

Function: `out =` istimetable `(x)` ¶

  True if input is a `timetable` array or other timetable-like type, false otherwise.

  Respects `istimetable` override methods on user-defined classes, even if they do not inherit from `table` or were known to Tablicious at authoring time.

  User-defined classes should only override `istimetable` to return true if they conform to the `table` public interface. That interface is not well-defined or documented yet, so maybe you don’t want to do that yet.

  Returns a scalar logical.

#### 8.2.27 localdate ¶

Class: localdate ¶

  Represents a complete day using the Gregorian calendar.

  This class is useful for indexing daily-granularity data or representing time periods that cover an entire day in local time somewhere. The major purpose of this class is "type safety", to prevent time-of-day values from sneaking in to data sets that should be daily only. As a secondary benefit, this uses less memory than datetimes.

Instance Variable of `localdate`: ``double`` dnums ¶

  The underlying datenum values that represent the days. The datenums are at the midnight that is at the start of the day it represents.

  These are doubles, but they are restricted to be integer-valued, so they represent complete days, with no time-of-day component.

Instance Variable of `localdate`: ``char`` Format ¶

  The format to display this `localdate` in. Currently unsupported.

#### 8.2.27.1 localdate.datenum ¶

Method: `out =` datenum `(obj)` ¶

  Convert this to datenums that represent midnight on obj’s days.

  Returns double array of same size as this.

#### 8.2.27.2 localdate.datestr ¶

Method: `out =` datestr `(obj)` ¶
Method: `out =` datestr `(obj, …)` ¶

  Format obj as date strings. Supports all arguments that core Octave’s `datestr` does.

  Returns date strings as a 2-D char array.

#### 8.2.27.3 localdate.datestrs ¶

Method: `out =` datestrs `(obj)` ¶
Method: `out =` datestrs `(obj, …)` ¶

  Format obj as date strings, returning cellstr. Supports all arguments that core Octave’s `datestr` does.

  Returns a cellstr array the same size as obj.

#### 8.2.27.4 localdate.datestruct ¶

Method: `out =` datestruct `(obj)` ¶

  Converts this to a “datestruct” broken-down time structure.

  A “datestruct” is a format of struct that Tablicious came up with. It is a scalar struct with fields Year, Month, and Day, each containing a double array the same size as the date array it represents. This format differs from the “datestruct” used by `datetime` in that it lacks Hour, Minute, and Second components. This is done for efficiency.

  The values in the returned broken-down time are those of the local time in obj’s defined time zone, if it has one.

  Returns a struct with fields Year, Month, and Day. Each field contains a double array of the same size as this.

#### 8.2.27.5 localdate.dispstrs ¶

Method: `out =` dispstrs `(obj)` ¶

  Get display strings for each element of obj.

  Returns a cellstr the same size as obj.

#### 8.2.27.6 localdate.isnan ¶

Method: `out =` isnan `(obj)` ¶

  True if input elements are NaT. This is an alias for `isnat` to support type compatibility and polymorphic programming.

  Returns logical array the same size as obj.

#### 8.2.27.7 localdate.isnat ¶

Method: `out =` isnat `(obj)` ¶

  True if input elements are NaT.

  Returns logical array the same size as obj.

#### 8.2.27.8 localdate.localdate ¶

Constructor: `obj =` localdate `()` ¶

  Constructs a new scalar `localdate` containing the current local date.

Constructor: `obj =` localdate `(datenums)` ¶
Constructor: `obj =` localdate `(datestrs)` ¶
Constructor: `obj =` localdate `(Y, M, D)` ¶
Constructor: `obj =` localdate `(…, `'Format'`, Format)` ¶

  Constructs a new `localdate` array based on input values.

#### 8.2.27.9 localdate.NaT ¶

Static Method: `out =` localdate.NaT `()` ¶
Static Method: `out =` localdate.NaT `(sz)` ¶

  “Not-a-Time”: Creates NaT-valued arrays.

  Constructs a new `datetime` array of all `NaT` values of the given size. If no input sz is given, the result is a scalar `NaT`.

  `NaT` is the `datetime` equivalent of `NaN`. It represents a missing or invalid value. `NaT` values never compare equal to, greater than, or less than any value, including other `NaT`s. Doing arithmetic with a `NaT` and any other value results in a `NaT`.

  This static method is provided because the global `NaT` function creates `datetime`s, not `localdate`s

#### 8.2.27.10 localdate.posixtime ¶

Method: `out =` posixtime `(obj)` ¶

  Converts this to POSIX time values for midnight of obj’s days.

  Converts this to POSIX time values that represent the same date. The returned values will be doubles that will not include fractional second values. The times returned are those of midnight UTC on obj’s days.

  Returns double array of same size as this.

#### 8.2.27.11 localdate.ymd ¶

Method: `[y, m, d] =` ymd `(obj)` ¶

  Get the Year, Month, and Day components of obj.

  Returns double arrays the same size as `obj`.

#### 8.2.28 milliseconds ¶

Function File: `out =` milliseconds `(x)` ¶

  Create a `duration` x milliseconds long, or get the milliseconds in a `duration` x.

  If input is numeric, returns a `duration` array that is that many milliseconds in time.

  If input is a `duration`, converts the `duration` to a number of milliseconds.

  Returns an array the same size as x.

#### 8.2.29 minutes ¶

Function File: `out =` hours `(x)` ¶

  Create a `duration` x hours long, or get the hours in a `duration` x.

#### 8.2.30 missing ¶

Class: missing ¶

  Generic auto-converting missing value.

  `missing` is a generic missing value that auto-converts to other types.

  A `missing` array indicates a missing value, of no particular type. It auto- converts to other types when it is combined with them via concatenation or other array combination operations.

  This class is currently EXPERIMENTAL. Use at your own risk.

  Note: This class does not actually work for assignment. If you do this:

        x = 1:5
        x(3) = missing

  It’s supposed to work, but I can’t figure out how to do this in a normal classdef object, because there doesn’t seem to be any function that’s implicitly called for type conversion in that assignment. Darn it.

#### 8.2.30.1 missing.dispstrs ¶

Method: `out =` dispstrs `(obj)` ¶

  Display strings.

  Gets display strings for each element in obj.

  For `missing`, the display strings are always `'<missing>'`.

  Returns a cellstr the same size as obj.

#### 8.2.30.2 missing.ismissing ¶

Method: `out =` ismissing `(obj)` ¶

  Test whether elements are missing values.

  `ismissing` is always true for `missing` arrays.

  Returns a logical array the same size as obj.

#### 8.2.30.3 missing.isnan ¶

Method: `out =` isnan `(obj)` ¶

  Test whether elements are NaN.

  `isnan` is always true for `missing` arrays.

  Returns a logical array the same size as obj.

#### 8.2.30.4 missing.isnanny ¶

Method: `out =` isnanny `(obj)` ¶

  Test whether elements are NaN-like.

  `isnanny` is always true for `missing` arrays.

  Returns a logical array the same size as obj.

#### 8.2.30.5 missing.missing ¶

Constructor: `obj =` missing `()` ¶

  Constructs a scalar `missing` array.

  The constructor takes no arguments, since there’s only one `missing` value.

#### 8.2.31 mustBeA ¶

Not documented

#### 8.2.32 mustBeCellstr ¶

Not documented

#### 8.2.33 mustBeCharvec ¶

Not documented

#### 8.2.34 mustBeFinite ¶

Not documented

#### 8.2.35 mustBeInteger ¶

Not documented

#### 8.2.36 mustBeMember ¶

Not documented

#### 8.2.37 mustBeNonempty ¶

Not documented

#### 8.2.38 mustBeNumeric ¶

Not documented

#### 8.2.39 mustBeReal ¶

Not documented

#### 8.2.40 mustBeSameSize ¶

Not documented

#### 8.2.41 mustBeScalar ¶

Not documented

#### 8.2.42 mustBeScalarLogical ¶

Not documented

#### 8.2.43 mustBeVector ¶

Not documented

#### 8.2.44 NaC ¶

Function: `out =` NaC `()` ¶
Function: `out =` NaC `(sz)` ¶

  “Not-a-Categorical". Creates missing-valued categorical arrays.

  Returns a new `categorical` array of all missing values of the given size. If no input sz is given, the result is a scalar missing categorical.

  `NaC` is the `categorical` equivalent of `NaN` or `NaT`. It represents a missing, invalid, or null value. `NaC` values never compare equal to any value, including other `NaC`s.

  `NaC` is a convenience function which is strictly a wrapper around `categorical.undefined` and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

  See also: categorical.undefined

#### 8.2.45 NaS ¶

Function: `out =` NaS `()` ¶
Function: `out =` NaS `(sz)` ¶

  “Not-a-String". Creates missing-valued string arrays.

  Returns a new `string` array of all missing values of the given size. If no input sz is given, the result is a scalar missing string.

  `NaS` is the `string` equivalent of `NaN` or `NaT`. It represents a missing, invalid, or null value. `NaS` values never compare equal to any value, including other `NaS`s.

  `NaS` is a convenience function which is strictly a wrapper around `string.missing` and returns the same results, but may be more convenient to type and/or more readable, especially in array expressions with several values.

  See also: string.missing

#### 8.2.46 NaT ¶

Function: `out =` NaT `()` ¶
Function: `out =` NaT `(sz)` ¶

  “Not-a-Time”. Creates missing-valued datetime arrays.

  Constructs a new `datetime` array of all `NaT` values of the given size. If no input sz is given, the result is a scalar `NaT`.

  `NaT` is the `datetime` equivalent of `NaN`. It represents a missing or invalid value. `NaT` values never compare equal to, greater than, or less than any value, including other `NaT`s. Doing arithmetic with a `NaT` and any other value results in a `NaT`.

  `NaT` currently cannot create NaT arrays of type `localdate`. To do that, use localdate.NaT instead.

#### 8.2.47 pp ¶

Function: pp `(X)` ¶
Function: pp `(A, B, C, …)` ¶
Function: pp `(`'A'`, `'B'`, `'C'`, …)` ¶
Function: pp ``A` `B` `C` …` ¶

  Alias for prettyprint, for interactive use.

  This is an alias for prettyprint(), with additional name-conversion magic.

  If you pass in a char, instead of pretty-printing that directly, it will grab and pretty-print the variable of that name from the caller’s workspace. This is so you can conveniently run it from the command line.

#### 8.2.48 scalarexpand ¶

Function: `[out1, out2, …, outN] =` scalarexpand `(x1, x2, …, xN)` ¶

  Expand scalar inputs to match size of non-scalar inputs.

  Expands each scalar input argument to match the size of the non-scalar input arguments, and returns the expanded values in the corresponding output arguments. `repmat` is used to do the expansion.

  Works on any input types that support `size`, `isscalar`, and `repmat`.

  It is an error if any of the non-scalar inputs are not the same size as all of the other non-scalar inputs.

  Returns as many output arguments as there were input arguments.

  Examples:

      x1 = rand(3);
      x2 = 42;
      x3 = magic(3);
      [x1, x2, x3] = scalarexpand (x1, x2, x3)

#### 8.2.49 seconds ¶

Function File: `out =` seconds `(x)` ¶

  Create a `duration` x seconds long, or get the seconds in a `duration` x.

  If input is numeric, returns a `duration` array that is that many seconds in time.

  If input is a `duration`, converts the `duration` to a number of seconds.

  Returns an array the same size as x.

#### 8.2.50 size2str ¶

Function: `out =` size2str `(sz)` ¶

  Format an array size for display.

  Formats the given array size sz as a string for human-readable display. It will be in the format “d1-by-d2-...-by-dN”, for the N dimensions represented by sz.

  sz is an array of dimension sizes, in the format returned by the `size` function.

  Returns a charvec.

  Examples:

      str = size2str (size (magic (4)))
          ⇒ str = 4-by-4

#### 8.2.51 splitapply ¶

Function: `out =` splitapply `(func, X, G)` ¶
Function: `out =` splitapply `(func, X1, …, XN, G)` ¶
Function: `[Y1, …, YM] =` splitapply `(…)` ¶

  Split data into groups and apply function.

  func is a function handle to call on each group of inputs in turn.

  X, X1, …, XN are the input variables that are split into groups for the function calls. If X is a `table`, then its contained variables are “popped out” and considered to be the X1 … XN input variables.

  G is the grouping variable vector. It contains a list of integers that identify which group each element of the X input variables belongs to. NaNs in G mean that element is ignored.

  Vertically concatenates the function outputs for each of the groups and returns them in as many variables as you capture.

  Returns the concatenated outputs of applying func to each group.

  See also: table.groupby, table.splitapply

#### 8.2.52 string ¶

Class: string ¶

  A string array of Unicode strings.

  A string array is an array of strings, where each array element is a single string.

  The string class represents strings, where:

  - Each element of a string array is a single string
  - A single string is a 1-dimensional row vector of Unicode characters
  - Those characters are encoded in UTF-8
    - This last bit depends on the fact that Octave chars are UTF-8 now

  This should correspond pretty well to what people think of as strings, and is pretty compatible with people’s typical notion of strings in Octave.

  String arrays also have a special “missing” value, that is like the string equivalent of NaN for doubles or “undefined” for categoricals, or SQL NULL.

  This is a slightly higher-level and more strongly-typed way of representing strings than cellstrs are. (A cellstr array is of type cell, not a text- specific type, and allows assignment of non-string data into it.)

  Be aware that while string arrays interconvert with Octave chars and cellstrs, Octave char elements represent 8-bit UTF-8 code units, not Unicode code points.

  This class really serves three roles:

  1. It is a type-safe object wrapper around Octave’s base primitive character types.
  2. It adds ismissing() semantics.
  3. And it introduces Unicode support.

  Not clear whether it’s a good fit to have the Unicode support wrapped up in this. Maybe it should just be a simple object wrapper wrapper, and defer Unicode semantics to when core Octave adopts them for char and cellstr. On the other hand, because Octave chars are UTF-8, not UCS-2, some methods like strlength() and reverse() are just going to be wrong if they delegate straight to chars.

  “Missing” string values work like NaNs. They are never considered equal, less than, or greater to any other string, including other missing strings. This applies to set membership and other equivalence tests.

  TODO: Need to decide how far to go with Unicode semantics, and how much to just make this an object wrapper over cellstr and defer to Octave’s existing char/string-handling functions.

  TODO: demote_strings should probably be static or global, so that other functions can use it to hack themselves into being string-aware.

#### 8.2.52.1 string.cell ¶

Method: `out =` cell `(obj)` ¶

  Convert to cell array.

  Converts this to a cell, which will be a cellstr. Missing values are converted to `''`.

  This method returns the same values as `cellstr(obj)`; it is just provided for interface compatibility purposes.

  Returns a cell array of the same size as obj.

#### 8.2.52.2 string.cellstr ¶

Method: `out =` cellstr `(obj)` ¶

  Convert to cellstr.

  Converts obj to a cellstr. Missing values are converted to `''`.

  Returns a cellstr array of the same size as obj.

#### 8.2.52.3 string.char ¶

Method: `out =` char `(obj)` ¶

  Convert to char array.

  Converts obj to a 2-D char array. It will have as many rows as obj has elements.

  It is an error to convert missing-valued `string` arrays to char. (NOTE: This may change in the future; it may be more appropriate) to convert them to space-padded empty strings.)

  Returns 2-D char array.

#### 8.2.52.4 string.cmp ¶

Method: `[out, outA, outB] =` cmp `(A, B)` ¶

  Value ordering comparison, returning -1/0/+1.

  Compares each element of A and B, returning for each element `i` whether `A(i)` was less than (-1), equal to (0), or greater than (1) the corresponding `B(i)`.

  TODO: What to do about missing values? Should missings sort to the end (preserving total ordering over the full domain), or should their comparisons result in a fourth "null"/"undef" return value, probably represented by NaN? FIXME: The current implementation does not handle missings.

  Returns a numeric array out of the same size as the scalar expansion of A and B. Each value in it will be -1, 0, or 1.

  Also returns scalar-expanded copies of A and B as outA and outB, as a programming convenience.

#### 8.2.52.5 string.decode ¶

Static Method: `out =` string.decode `(bytes, charsetName)` ¶

  Decode encoded text from bytes.

  Decodes the given encoded text in bytes according to the specified encoding, given by charsetName.

  Returns a scalar string.

  See also: string.encode

#### 8.2.52.6 string.dispstrs ¶

Method: `out =` dispstrs `(obj)` ¶

  Display strings for array elements.

  Gets display strings for all the elements in obj. These display strings will either be the string contents of the element, enclosed in `"..."`, and with CR/LF characters replaced with `'\r'` and `'\n'` escape sequences, or `"<missing>"` for missing values.

  Returns a cellstr of the same size as obj.

#### 8.2.52.7 string.empty ¶

Function: `out =` empty `(sz)` ¶

  Get an empty string array of a specified size.

  The argument sz is optional. If supplied, it is a numeric size array whose product must be zero. If omitted, it defaults to [0 0].

  The size may also be supplied as multiple arguments containing scalar numerics.

  Returns an empty string array of the requested size.

#### 8.2.52.8 string.encode ¶

Method: `out =` encode `(obj, charsetName)` ¶

  Encode string in a given character encoding.

  obj must be scalar.

  charsetName (charvec) is the name of a character encoding. (TODO: Document what determines the set of valid encoding names.)

  Returns the encoded string as a `uint8` vector.

  See also: string.decode.

#### 8.2.52.9 string.erase ¶

Method: `out =` erase `(obj, match)` ¶

  Erase matching substring.

  Erases the substrings in obj which match the match input.

  Returns a string array of the same size as obj.

#### 8.2.52.10 string.ismissing ¶

Method: `out =` ismissing `(obj)` ¶

  Test whether array elements are missing.

  For `string` arrays, only the special “missing” value is considered missing. Empty strings are not considered missing, the way they are with cellstrs.

  Returns a logical array the same size as `obj`.

#### 8.2.52.11 string.isnanny ¶

Method: `out =` isnanny `(obj)` ¶

  Test whether array elements are NaN-like.

  Missing values are considered nannish; any other string value is not.

  Returns a logical array of the same size as obj.

#### 8.2.52.12 string.isstring ¶

Method: `out =` isstring `(obj)` ¶

  Test if input is a string array.

  `isstring` is always true for `string` inputs.

  Returns a scalar logical.

#### 8.2.52.13 string.lower ¶

Method: `out =` lower `(obj)` ¶

  Convert to lower case.

  Converts all the characters in all the strings in obj to lower case.

  This currently delegates to Octave’s own `lower()` function to do the conversion, so whatever character class handling it has, this has.

  Returns a string array of the same size as obj.

#### 8.2.52.14 string.missing ¶

Static Method: `out =` string.missing `(sz)` ¶

  Missing string value.

  Creates a string array of all-missing values of the specified size sz. If sz is omitted, creates a scalar missing string.

  Returns a string array of size sz or [1 1].

  See also: NaS

#### 8.2.52.15 string.plus ¶

Method: `out =` plus `(a, b)` ¶

  String concatenation via plus operator.

  Concatenates the two input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

  The concatenation is done by calling ‘strcat‘ on the inputs, and has the same behavior.

  Returns a string array the same size as the scalar expansion of its inputs.

  See also: string.strcat

#### 8.2.52.16 string.regexprep ¶

Method: `out =` regexprep `(obj, pat, repstr)` ¶
Method: `out =` regexprep `(…, varargin)` ¶

  Replace based on regular expression matching.

  Replaces all the substrings matching a given regexp pattern pat with the given replacement text repstr.

  Returns a string array of the same size as obj.

#### 8.2.52.17 string.reverse ¶

Method: `out =` reverse `(obj)` ¶

  Reverse string, character-wise.

  Reverses the characters in each string in obj. This operates on Unicode characters (code points), not on bytes, so it is guaranteed to produce valid UTF-8 as its output.

  Returns a string array the same size as obj.

#### 8.2.52.18 string.reverse_bytes ¶

Method: `out =` reverse_bytes `(obj)` ¶

  Reverse string, byte-wise.

  Reverses the bytes in each string in obj. This operates on bytes (Unicode code units), not characters.

  This may well produce invalid strings as a result, because reversing a UTF-8 byte sequence does not necessarily produce another valid UTF-8 byte sequence.

  You probably do not want to use this method. You probably want to use `string.reverse` instead.

  Returns a string array the same size as obj.

  See also: string.reverse

#### 8.2.52.19 string.strcat ¶

Method: `out =` strcat `(varargin)` ¶

  String concatenation.

  Concatenates the corresponding elements of all the input arrays, string-wise. Inputs that are not string arrays are converted to string arrays.

  The semantics of concatenating missing strings with non-missing strings has not been determined yet.

  Returns a string array the same size as the scalar expansion of its inputs.

#### 8.2.52.20 string.strcmp ¶

Method: `out =` strcmp `(A, B)` ¶

  String comparison.

  Tests whether each element in A is exactly equal to the corresponding element in B. Missing values are not considered equal to each other.

  This does the same comparison as `A == B`, but is not polymorphic. Generally, there is no reason to use `strcmp` instead of `==` or `eq` on string arrays, unless you want to be compatible with cellstr inputs as well.

  Returns logical array the size of the scalar expansion of A and B.

#### 8.2.52.21 string.strfind ¶

Method: `out =` strfind `(obj, pattern)` ¶
Method: `out =` strfind `(…, varargin)` ¶

  Find pattern in string.

  Finds the locations where pattern occurs in the strings of obj.

  TODO: It’s ambiguous whether a scalar this should result in a numeric out or a cell array out.

  Returns either an index vector, or a cell array of index vectors.

#### 8.2.52.22 string.string ¶

Constructor: `obj =` string `()` ¶
Constructor: `obj =` string `(in)` ¶

  Construct a new string array.

  The zero-argument constructor creates a new scalar string array whose value is the empty string.

  The other constructors construct a new string array by converting various types of inputs.

  - chars and cellstrs are converted via cellstr()
  - numerics are converted via num2str()
  - datetimes are converted via datestr()

#### 8.2.52.23 string.strlength ¶

Method: `out =` strlength `(obj)` ¶

  String length in characters (actually, UTF-16 code units).

  Gets the length of each string, counted in UTF-16 code units. In most cases, this is the same as the number of characters. The exception is for characters outside the Unicode Basic Multilingual Plane, which are represented with UTF-16 surrogate pairs, and thus will count as 2 characters each.

  The reason this method counts UTF-16 code units, instead of Unicode code points (true characters), is for Matlab compatibility.

  This is the string length method you probably want to use, not `strlength_bytes`.

  Returns double array of the same size as obj. Returns NaNs for missing strings.

  See also: string.strlength_bytes

#### 8.2.52.24 string.strlength_bytes ¶

Method: `out =` strlength_bytes `(obj)` ¶

  String length in bytes.

  Gets the length of each string in obj, counted in Unicode UTF-8 code units (bytes). This is the same as `numel(str)` for the corresponding Octave char vector for each string, but may not be what you actually want to use. You may want `strlength` instead.

  Returns double array of the same size as obj. Returns NaNs for missing strings.

  See also: string.strlength

#### 8.2.52.25 string.strrep ¶

Method: `out =` strrep `(obj, match, replacement)` ¶
Method: `out =` strrep `(…, varargin)` ¶

  Replace occurrences of pattern with other string.

  Replaces matching substrings in obj with a given replacement string.

  varargin is passed along to the core Octave `strrep` function. This supports whatever options it does. TODO: Maybe document what those options are.

  Returns a string array of the same size as obj.

#### 8.2.52.26 string.upper ¶

Method: `out =` upper `(obj)` ¶

  Convert to upper case.

  Converts all the characters in all the strings in obj to upper case.

  This currently delegates to Octave’s own `upper()` function to do the conversion, so whatever character class handling it has, this has.

  Returns a string array of the same size as obj.

#### 8.2.53 struct2table ¶

Function: `out =` struct2table `(s)` ¶
Function: `out =` struct2table `(…, `'AsArray'`, AsArray)` ¶

  Convert struct to a table.

  Converts the input struct s to a `table`.

  s may be a scalar struct or a nonscalar struct array.

  The AsArray option is not implemented yet.

  Returns a `table`.

#### 8.2.54 table ¶

Class: table ¶

  Tabular data array containing multiple columnar variables.

  A `table` is a tabular data structure that collects multiple parallel named variables. Each variable is treated like a column. (Possibly a multi-columned column, if that makes sense.) The types of variables may be heterogeneous.

  A table object is like an SQL table or resultset, or a relation, or a DataFrame in R or Pandas.

  A table is an array in itself: its size is nrows-by-nvariables, and you can index along the rows and variables by indexing into the table along dimensions 1 and 2.

  A note on accessing properties of a `table` array: Because .-indexing is used to access the variables inside the array, it can’t also be directly used to access properties as well. Instead, do `t.Properties.<property>` for a table `t`. That will give you a property instead of a variable. (And due to this mechanism, it will cause problems if you have a `table` with a variable named `Properties`. Try to avoid that.)

  WARNING ABOUT HANDLE CLASSES IN TABLE VARIABLES

  Using a handle class in a table variable (column) value may lead to unpredictable and buggy behavior! A handle class array is a reference type, and it holds shared mutable state, which may be shared with references to it in other table arrays or outside the table array. The table class makes no guarantees about what it will or will not do internally with arrays that are held in table variables, and any operation on a table holding handle arrays may have unpredictable and undesirable side effects. These side effects may change between versions of Tablicious.

  We currently recommend that you do not use handle classes in table variables. It may be okay to use handle classes *inside* cells or other non-handle composite types that are used in table variables, but this hasn’t been fully thought through or tested.

  See also: tblish.table.grpstats, tblish.evalWithTableVars, tblish.examples.SpDb

Instance Variable of `table`: ``cellstr`` VariableNames ¶

  The names of the variables in the table, as a cellstr row vector.

Instance Variable of `table`: ``cell`` VariableValues ¶

  A cell vector containing the values for each of the variables. `VariableValues(i)` corresponds to `VariableNames(i)`.

Instance Variable of `table`: ``cellstr`` RowNames ¶

  An optional list of row names that identify each row in the table. This is a cellstr column vector, if present.

Instance Variable of `table`: ``cellstr`` DimensionNames ¶

  Names for the two dimensions of the table array, as a cellstr row vector. Always exactly 2-long, because tables are always exactly 2-D. Defaults to `{"Row", "Variables"}`. (I feel the singular "Row" and plural "Variables" here are inconsistent, but that’s what Matlab uses, so Tablicious uses it too, for Matlab compatibility.)

#### 8.2.54.1 table.addvars ¶

Method: `out =` addvars `(obj, var1, …, varN)` ¶
Method: `out =` addvars `(…, `'Before'`, Before)` ¶
Method: `out =` addvars `(…, `'After'`, After)` ¶
Method: `out =` addvars `(…, `'NewVariableNames'`, NewVariableNames)` ¶

  Add variables to table.

  Adds the specified variables to a table.

#### 8.2.54.2 table.antijoin ¶

Method: `[outA, ixA, outB, ixB] =` antijoin `(A, B)` ¶

  Natural antijoin (AKA “semidifference”).

  Computes the anti-join of A and B. The anti-join is defined as all the rows from one input which do not have matching rows in the other input.

  Returns: outA - all the rows in A with no matching row in B ixA - the row indexes into A which produced outA outB - all the rows in B with no matching row in A ixB - the row indexes into B which produced outB

  This is a Tablicious/Octave extension, not defined in the Matlab table interface.

#### 8.2.54.3 table.cartesian ¶

Method: `[out, ixs] =` cartesian `(A, B)` ¶

  Cartesian product of two tables.

  Computes the Cartesian product of two tables. The Cartesian product is each row in A combined with each row in B.

  Due to the definition and structural constraints of table, the two inputs must have no variable names in common. It is an error if they do.

  The Cartesian product is seldom used in practice. If you find yourself calling this method, you should step back and re-evaluate what you are doing, asking yourself if that is really what you want to happen. If nothing else, writing a function that calls cartesian() is usually much less efficient than alternate ways of arriving at the same result.

  This implementation does not remove duplicate values. TODO: Determine whether this duplicate-removing behavior is correct.

  The ordering of the rows in the output is not specified, and may be implementation- dependent. TODO: Determine if we can lock this behavior down to a fixed, defined ordering, without killing performance.

  This is a Tablicious/Octave extension, not defined in the Matlab table interface.

#### 8.2.54.4 table.convertvars ¶

Method: `out =` convertvars `(obj, vars, dataType)` ¶

  Convert variables to specified data type.

  Converts the variables in obj specified by vars to the specified data type.

  vars is a cellstr or numeric vector specifying which variables to convert.

  dataType specifies the data type to convert those variables to. It is either a char holding the name of the data type, or a function handle which will perform the conversion. If it is the name of the data type, there must either be a one-arg constructor of that type which accepts the specified variables’ current types as input, or a conversion method of that name defined on the specified variables’ current type.

  Returns a table with the same variable names as obj, but with converted types.

#### 8.2.54.5 table.findgroups ¶

Method: `[G, TID] =` findgroups `(obj)` ¶

  Find groups within a table’s row values.

  Finds groups within a table’s row values and get group numbers. A group is a set of rows that have the same values in all their variable elements.

  Returns: G - A double column vector of group numbers created from obj. TID - A table containing the row values corresponding to the group numbers.

#### 8.2.54.6 table.getvar ¶

Method: `[out, name]` = `getvar (obj, varRef)` ¶

  Get value and name for single table variable.

  varRef is a variable reference. It may be a name or an index. It may only specify a single table variable.

  Returns: out – the value of the referenced table variable name – the name of the referenced table variable

#### 8.2.54.7 table.getvars ¶

Method: `[out1, …]` = `getvars (obj, varRef)` ¶

  Get values for one ore more table variables.

  varRef is a variable reference in the form of variable names or indexes.

  Returns as many outputs as varRef referenced variables. Each output contains the contents of the corresponding table variable.

#### 8.2.54.8 table.groupby ¶

Method: `[out] =` groupby `(obj, groupvars, aggcalcs)` ¶

  Find groups in table data and apply functions to variables within groups.

  This works like an SQL `"SELECT ... GROUP BY ..."` statement.

  groupvars (cellstr, numeric) is a list of the grouping variables, identified by name or index.

  aggcalcs is a specification of the aggregate calculations to perform on them, in the form `{`out_var`,` fcn`,` in_vars`; ...}`, where: out_var (char) is the name of the output variable fcn (function handle) is the function to apply to produce it in_vars (cellstr) is a list of the input variables to pass to fcn

  Returns a table.

  This is a Tablicious/Octave extension, not defined in the Matlab table interface.

#### 8.2.54.9 table.height ¶

Method: `out =` height `(obj)` ¶

  Number of rows in table.

  For a zero-variable table, this currently always returns 0. This is a bug, and will change in the future. It should be possible for zero-variable table arrays to have any number of rows.

#### 8.2.54.10 table.horzcat ¶

Method: `out =` horzcat `(varargin)` ¶

  Horizontal concatenation.

  Combines tables by horizontally concatenating them. Inputs that are not tables are automatically converted to tables by calling table() on them. Inputs must have all distinct variable names.

  Output has the same RowNames as `varargin{1}`. The variable names and values are the result of the concatenation of the variable names and values lists from the inputs.

#### 8.2.54.11 table.innerjoin ¶

Method: `[out, ixa, ixb] =` innerjoin `(A, B)` ¶
Method: `[…] =` innerjoin `(A, B, …)` ¶

  Combine two tables by rows using key variables.

  Computes the relational inner join between two tables. “Inner” means that only rows which had matching rows in the other input are kept in the output.

  TODO: Document options.

  Returns: out - A table that is the result of joining A and B ix - Indexes into A for each row in out ixb - Indexes into B for each row in out

#### 8.2.54.12 table.intersect ¶

Method: `[C, ia, ib] =` intersect `(A, B)` ¶

  Set intersection.

  Computes the intersection of two tables. The intersection is defined to be the unique row values which are present in both of the two input tables.

  Returns: C - A table containing all the unique row values present in both A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

#### 8.2.54.13 table.isempty ¶

Method: `out =` isempty `(obj)` ¶

  Test whether array is empty.

  For tables, `isempty` is true if the number of rows is 0 or the number of variables is 0.

#### 8.2.54.14 table.ismember ¶

Method: `[tf, loc] =` ismember `(A, B)` ¶

  Set membership.

  Finds rows in A that are members of B.

  Returns: tf - A logical vector indicating whether each A(i,:) was present in B. loc - Indexes into B of rows that were found.

#### 8.2.54.15 table.ismissing ¶

Method: `out =` ismissing `(obj)` ¶
Method: `out =` ismissing `(obj, indicator)` ¶

  Find missing values.

  Finds missing values in obj’s variables.

  If indicator is not supplied, uses the standard missing values for each variable’s data type. If indicator is supplied, the same indicator list is applied across all variables.

  All variables in this must be vectors. (This is due to the requirement that `size(out) == size(obj)`.)

  Returns a logical array the same size as obj.

#### 8.2.54.16 table.istable ¶

Method: `tf =` istable `(obj)` ¶

  True if input is a table.

#### 8.2.54.17 table.join ¶

Method: `[C, ib] =` join `(A, B)` ¶
Method: `[C, ib] =` join `(A, B, …)` ¶

  Combine two tables by rows using key variables, in a restricted form.

  This is not a "real" relational join operation. It has the restrictions that: 1) The key values in B must be unique. 2) Every key value in A must map to a key value in B. These are restrictions inherited from the Matlab definition of table.join.

  You probably don’t want to use this method. You probably want to use innerjoin or outerjoin instead.

  See also: table.innerjoin, table.outerjoin

#### 8.2.54.18 table.mergevars ¶

Method: `out =` mergevars `(obj, vars)` ¶
Method: `out =` mergevars `(…, `'NewVariableName'`, NewVariableName)` ¶
Method: `out =` mergevars `(…, `'MergeAsTable'`, MergeAsTable)` ¶

  Merge table variables into a single variable.

#### 8.2.54.19 table.movevars ¶

Method: `out =` movevars `(obj, vars, relLocation, location)` ¶

  Move around variables in a table.

  vars is a list of variables to move, specified by name or index.

  relLocation is `'Before'` or `'After'`.

  location indicates a single variable to use as the target location, specified by name or index. If it is specified by index, it is the index into the list of *unmoved* variables from obj, not the original full list of variables in obj.

  Returns a table with the same variables as obj, but in a different order.

#### 8.2.54.20 table.ndims ¶

Method: `out =` ndims `(obj)` ¶

  Number of dimensions

  For tables, `ndims(obj)` is always 2, because table arrays are always 2-D (rows-by-columns).

#### 8.2.54.21 table.numel ¶

Method: `out =` numel `(obj)` ¶

  Total number of elements in table (actually 1).

  For compatibility reasons with Octave’s OOP interface and subsasgn behavior, table’s numel is defined to always return 1. It is not useful for client code to query a table’s size using numel. This is an incompatibility with Matlab.

#### 8.2.54.22 table.outerfillvals ¶

Method: `out =` outerfillvals `(obj)` ¶

  Get fill values for outer join.

  Returns a table with the same variables as this, but containing only a single row whose variable values are the values to use as fill values when doing an outer join.

#### 8.2.54.23 table.outerjoin ¶

Method: `[out, ixa, ixb] =` outerjoin `(A, B)` ¶
Method: `[…] =` outerjoin `(A, B, …)` ¶

  Combine two tables by rows using key variables, retaining unmatched rows.

  Computes the relational outer join of tables A and B. This is like a regular join, but also includes rows in each input which did not have matching rows in the other input; the columns from the missing side are filled in with placeholder values.

  TODO: Document options.

  Returns: out - A table that is the result of the outer join of A and B ixa - indexes into A for each row in out ixb - indexes into B for each row in out

#### 8.2.54.24 table.prettyprint ¶

Method: prettyprint `(obj)` ¶

  Display table’s values in tabular format. This prints the contents of the table in human-readable, tabular form.

  Variables which contain objects are displayed using the strings returned by their `dispstrs` method, if they define one.

#### 8.2.54.25 table.realjoin ¶

Method: `[out, ixs] =` realjoin `(A, B)` ¶
Method: `[…] =` realjoin `(A, B, …)` ¶

  "Real" relational inner join, without key restrictions

  Performs a "real" relational natural inner join between two tables, without the key restrictions that JOIN imposes.

  Currently does not support tables which have RowNames. This may be added in the future.

  This is a Tablicious/Octave extension, not defined in the Matlab table interface.

  Name/value option arguments are: Keys, LeftKeys, RightKeys, LeftVariables, RightVariables.

  FIXME: Document those options.

  Returns: out - A table that is the result of joining A and B ixs - Indexes into A for each row in out

#### 8.2.54.26 table.removevars ¶

Method: `out =` removevars `(obj, vars)` ¶

  Remove variables from table.

  Deletes the variables specified by vars from obj.

  vars may be a char, cellstr, numeric index vector, or logical index vector.

#### 8.2.54.27 table.renamevars ¶

Method: `out =` renamevars `(obj, renameMap)` ¶

  Rename variables in a table.

  Renames selected variables in the table obj based on the mapping provided in renameMap.

  renameMap is an n-by-2 cellstr array, with the old variable names in the first column, and the corresponding new variable names in the second column.

  Variables which are not included in renameMap are not modified.

  It is an error if any variables named in the first column of renameMap are not present in obj.

  Renames

#### 8.2.54.28 table.repelem ¶

Method: `out =` repelem `(obj, R)` ¶
Method: `out =` repelem `(obj, R_1, R_2)` ¶

  Replicate elements of matrix.

  Replicates elements of this table matrix by applying repelem to each of its variables. This

  Only two dimensions are supported for `repelem` on tables.

#### 8.2.54.29 table.repmat ¶

Method: `out =` repmat `(obj, sz)` ¶

  Replicate matrix.

  Repmats a table by repmatting each of its variables vertically.

  For tables, repmatting is only supported along dimension 1. That is, the values of sz(2:end) must all be exactly 1. This behavior may change in the future to support repmatting horizontally, with the added variable names being automatically changed to maintain uniqueness of variable names within the resulting table.

  Returns a new table with the same variable names and types as tbl, but with a possibly different row count.

#### 8.2.54.30 table.restrict ¶

Method: `out =` restrict `(obj, expr)` ¶
Method: `out =` restrict `(obj, ix)` ¶

  Subset rows using variable expression or index.

  Subsets a table row-wise, using either an index vector or an expression involving obj’s variables.

  If the argument is a numeric or logical vector, it is interpreted as an index into the rows of this. (Just as with ‘subsetrows (this, index)‘.)

  If the argument is a char, then it is evaulated as an M-code expression, with all of this’ variables available as workspace variables, as with `tblish.evalWithTableVars`. The output of expr must be a numeric or logical index vector (This form is a shorthand for `out = subsetrows (this, tblish.evalWithTableVars (this, expr))`.)

  TODO: Decide whether to name this to "where" to be more like SQL instead of relational algebra.

  Examples:

      [s,p,sp] = tblish.examples.SpDb;
      prettyprint (restrict (p, 'Weight >= 14 & strcmp(Color, "Red")'))

  This is a Tablicious/Octave extension, not defined in the Matlab table interface.

  See also: tblish.evalWithTableVars

#### 8.2.54.31 table.rowfun ¶

Method: `out =` varfun `(func, obj)` ¶
Method: `out =` varfun `(…, `'OptionName'`, OptionValue, …)` ¶

  Apply function to rows in table and collect outputs.

  This applies the function func to the elements of each row of obj’s variables, and collects the concatenated output(s) into the variable(s) of a new table.

  func is a function handle. It should take as many inputs as there are variables in obj. Or, it can take a single input, and you must specify `'SeparateInputs', false` to have the input variables concatenated before being passed to func. It may return multiple argouts, but to capture those past the first one, you must explicitly specify the `'NumOutputs'` or `'OutputVariableNames'` options.

  Supported name/value options:

  `'OutputVariableNames'`

    Names of table variables to store combined function output arguments in.

  `'NumOutputs'`

    Number of output arguments to call function with. If omitted, defaults to number of items in OutputVariableNames if it is supplied, otherwise defaults to 1.

  `'SeparateInputs'`

    If true, input variables are passed as separate input arguments to func. If false, they are concatenated together into a row vector and passed as a single argument. Defaults to true.

  `'ErrorHandler'`

    A function to call as a fallback when calling func results in an error. It is passed the caught exception, along with the original inputs passed to func, and it has a “second chance” to compute replacement values for that row. This is useful for converting raised errors to missing-value fill values, or logging warnings.

  `'ExtractCellContents'`

    Whether to “pop out” the contents of the elements of cell variables in obj, or to leave them as cells. True/false; default is false. If you specify this option, then obj may not have any multi-column cell-valued variables.

  `'InputVariables'`

    If specified, only these variables from obj are used as the function inputs, instead of using all variables.

  `'GroupingVariables'`

    Not yet implemented.

  `'OutputFormat'`

    The format of the output. May be `'table'` (the default), `'uniform'`, or `'cell'`. If it is `'uniform'` or `'cell'`, the output variables are returned in multiple output arguments from `'rowfun'`.

  Returns a `table` whose variables are the collected output arguments of func if OutputFormat is `'table'`. Otherwise, returns multiple output arguments of whatever type func returned (if OutputFormat is `'uniform'`) or cells (if OutputFormat is `'cell'`).

#### 8.2.54.32 table.rows2vars ¶

Method: `out =` rows2vars `(obj)` ¶
Method: `out =` rows2vars `(obj, `'VariableNamesSource'`, VariableNamesSource)` ¶
Method: `out =` rows2vars `(…, `'DataVariables'`, DataVariables)` ¶

  Reorient table, swapping rows and variables dimensions.

  This flips the dimensions of the given table obj, swapping the orientation of the contained data, and swapping the row names/labels and variable names.

  The variable names become a new variable named “OriginalVariableNames”.

  The row names are drawn from the column VariableNamesSource if it is specified. Otherwise, if obj has row names, they are used. Otherwise, new variable names in the form “VarN” are generated.

  If all the variables in obj are of the same type, they are concatenated and then sliced to create the new variable values. Otherwise, they are converted to cells, and the new table has cell variable values.

#### 8.2.54.33 table.semijoin ¶

Method: `[outA, ixA, outB, ixB] =` semijoin `(A, B)` ¶

  Natural semijoin.

  Computes the natural semijoin of tables A and B. The semi-join of tables A and B is the set of all rows in A which have matching rows in B, based on comparing the values of variables with the same names.

  This method also computes the semijoin of B and A, for convenience.

  Returns: outA - all the rows in A with matching row(s) in B ixA - the row indexes into A which produced outA outB - all the rows in B with matching row(s) in A ixB - the row indexes into B which produced outB

  This is a Tablicious/Octave extension, not defined in the Matlab table interface.

#### 8.2.54.34 table.setdiff ¶

Method: `[C, ia] =` setdiff `(A, B)` ¶

  Set difference.

  Computes the set difference of two tables. The set difference is defined to be the unique row values which are present in table A that are not in table B.

  Returns: C - A table containing the unique row values in A that were not in B. ia - Row indexes into A of the rows from A included in C.

#### 8.2.54.35 table.setDimensionNames ¶

Method: `out =` setDimensionNames `(obj, names)` ¶
Method: `out =` setDimensionNames `(obj, ix, names)` ¶

  Set dimension names.

  Sets the `DimensionNames` for this table to a new list of names.

  names is a char or cellstr vector. It must have the same number of elements as the number of dimension names being assigned.

  ix is an index vector indicating which dimension names to set. If omitted, it sets all two of them. Since there are always two dimension, the indexes in ix may never be higher than 2.

  This method exists because the `obj.Properties.DimensionNames = …` assignment form did not originally work, possibly due to an Octave bug, or more likely due to a bug in Tablicious prior to the early 0.4.x versions. That was fixed around 0.4.4. This method may be deprecated and removed at some point, since it is not part of the standard Matlab table interface, and is now redundant with the `obj.Properties.DimensionNames = …` assignment form.

#### 8.2.54.36 table.setRowNames ¶

Method: `out =` setRowNames `(obj, names)` ¶

  Set row names.

  Sets the row names on obj to names.

  names is a cellstr column vector, with the same number of rows as obj has.

#### 8.2.54.37 table.setvar ¶

Method: `out =` setvar `(obj, varRef, value)` ¶

  Set value for a variable in table.

  This sets (adds or replaces) the value for a variable in obj. It may be used to change the value of an existing variable, or add a new variable.

  This method exists primarily because I cannot get `obj.foo = value` to work, apparently due to an issue with Octave’s subsasgn support.

  varRef is a variable reference, either the index or name of a variable. If you are adding a new variable, it must be a name, and not an index.

  value is the value to set the variable to. If it is scalar or a single string as charvec, it is scalar-expanded to match the number of rows in obj.

#### 8.2.54.38 table.setVariableNames ¶

Method: `out =` setVariableNames `(obj, names)` ¶
Method: `out =` setVariableNames `(obj, ix, names)` ¶

  Set variable names.

  Sets the `VariableNames` for this table to a new list of names.

  names is a char or cellstr vector. It must have the same number of elements as the number of variable names being assigned.

  ix is an index vector indicating which variable names to set. If omitted, it sets all of them present in obj.

  This method exists because the `obj.Properties.VariableNames = …` assignment form does not work, possibly due to an Octave bug.

#### 8.2.54.39 table.setxor ¶

Method: `[C, ia, ib] =` setxor `(A, B)` ¶

  Set exclusive OR.

  Computes the setwise exclusive OR of two tables. The set XOR is defined to be the unique row values which are present in one or the other of the two input tables, but not in both.

  Returns: C - A table containing all the unique row values in the set XOR of A and B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

#### 8.2.54.40 table.size ¶

Method: `sz =` size `(obj)` ¶
Method: `[nr, nv] =` size `(obj)` ¶
Method: `[nr, nv, …] =` size `(obj)` ¶

  Gets the size of a table.

  For tables, the size is [number-of-rows x number-of-variables]. This is the same as `[height(obj), width(obj)]`.

#### 8.2.54.41 table.splitapply ¶

Method: `out =` splitapply `(func, obj, G)` ¶
Method: `[Y1, …, YM] =` splitapply `(func, obj, G)` ¶

  Split table data into groups and apply function.

  Performs a splitapply, using the variables in obj as the input X variables to the `splitapply` function call.

  See also: splitapply, table.groupby, tblish.table.grpstats

#### 8.2.54.42 table.splitvars ¶

Method: `out =` splitvars `(obj)` ¶
Method: `out =` splitvars `(obj, vars)` ¶
Method: `out =` splitvars `(…, `'NewVariableNames'`, NewVariableNames)` ¶

  Split multicolumn table variables.

  Splits multicolumn table variables into new single-column variables. If vars is supplied, splits only those variables. If vars is not supplied, splits all multicolumn variables.

#### 8.2.54.43 table.squeeze ¶

Method: `obj =` squeeze `(obj)` ¶

  Remove singleton dimensions.

  For tables, this is always a no-op that returns the input unmodified, because tables always have exactly 2 dimensions, and 2-D arrays are unaffected by squeeze.

#### 8.2.54.44 table.stack ¶

Method: `out =` stack `(obj, vars)` ¶
Method: `out =` stack `(…, `'NewDataVariableName'`, NewDataVariableName)` ¶
Method: `out =` stack `(…, `'IndexVariableName'`, IndexVariableName)` ¶

  Stack multiple table variables into a single variable.

#### 8.2.54.45 table.summary ¶

Method: `summary` (obj) ¶

  Display a summary of a table’s data.

  Displays a summary of data in the input table. This will contain some statistical information on each of its variables. The output is printed to the Octave console (command window, stdout, or the like in your current session), in a format suited for human consumption. The output format is not fixed or formally defined, and may change over time. It is only suitable for human display, and not for parsing or programmatic use.

  This method supports, to some degree, extension by other packages. If your Octave session has loaded other packages which supply extension implementaions of ‘summary‘, Tablicious will use those in preference to its own internal implementation, and you will get different, and hopefully better, output.

#### 8.2.54.46 table.table ¶

Constructor: `obj =` table `()` ¶

  Constructs a new empty (0 rows by 0 variables) table.

Constructor: `obj =` table `(var1, var2, …, varN)` ¶

  Constructs a new table from the given variables. The variables passed as inputs to this constructor become the variables of the table. Their names are automatically detected from the input variable names that you used.

  Note: If you call the constructor with exactly three arguments, and the first argument is exactly the value ’__tblish_backdoor__’, that will trigger a special internal-use backdoor calling form, and you will get incorrect results. This is a bug in Tablicious.

Constructor: `obj =` table `(`'Size'`, sz, `'VariableTypes'`, varTypes)` ¶

  Constructs a new table of the given size, and with the given variable types. The variables will contain the default value for elements of that type.

Constructor: `obj =` table `(…, `'VariableNames'`, varNames)` ¶
Constructor: `obj =` table `(…, `'RowNames'`, rowNames)` ¶

  Specifies the variable names or row names to use in the constructed table. Overrides the implicit names garnered from the input variable names.

#### 8.2.54.47 table.table2array ¶

Method: `s =` table2struct `(obj)` ¶

  Converts obj to a homogeneous array.

#### 8.2.54.48 table.table2cell ¶

Method: `c =` table2cell `(obj)` ¶

  Converts table to a cell array. Each variable in obj becomes one or more columns in the output, depending on how many columns that variable has.

  Returns a cell array with the same number of rows as obj, and with as many or more columns as obj has variables.

#### 8.2.54.49 table.table2struct ¶

Method: `s =` table2struct `(obj)` ¶
Method: `s =` table2struct `(…, `'ToScalar'`, trueOrFalse)` ¶

  Converts obj to a scalar structure or structure array.

  Row names are not included in the output struct. To include them, you must add them manually: s = table2struct (tbl, ’ToScalar’, true); s.RowNames = tbl.Properties.RowNames;

  Returns a scalar struct or struct array, depending on the value of the `ToScalar` option.

#### 8.2.54.50 table.union ¶

Method: `[C, ia, ib] =` union `(A, B)` ¶

  Set union.

  Computes the union of two tables. The union is defined to be the unique row values which are present in either of the two input tables.

  Returns: C - A table containing all the unique row values present in A or B. ia - Row indexes into A of the rows from A included in C. ib - Row indexes into B of the rows from B included in C.

#### 8.2.54.51 table.varfun ¶

Method: `out =` varfun `(fcn, obj)` ¶
Method: `out =` varfun `(…, `'OutputFormat'`, outputFormat)` ¶
Method: `out =` varfun `(…, `'InputVariables'`, vars)` ¶
Method: `out =` varfun `(…, `'ErrorHandler'`, errorFcn)` ¶

  Apply function to table variables.

  Applies the given function fcn to each variable in obj, collecting the output in a table, cell array, or array of another type.

#### 8.2.54.52 table.varnames ¶

Method: `out =` varnames `(obj)` ¶
Method: `out =` varnames `(obj, varNames)` ¶

  Get or set variable names for a table.

  Returns cellstr in the getter form. Returns an updated datetime in the setter form.

#### 8.2.54.53 table.vertcat ¶

Method: `out =` vertcat `(varargin)` ¶

  Vertical concatenation.

  Combines tables by vertically concatenating them.

  Inputs that are not tables are automatically converted to tables by calling table() on them.

  The inputs must have the same number and names of variables, and their variable value types and sizes must be cat-compatible. The types of the resulting variables are the types that result from doing a ‘vertcat()‘ on the variables from the corresponding input tables, in the order they were input in.

#### 8.2.54.54 table.width ¶

Method: `out =` width `(obj)` ¶

  Number of variables in table.

  Note that this is not the sum of the number of columns in each variable. It is just the number of variables.

#### 8.2.55 tableOuterFillValue ¶

Not documented

#### 8.2.56 tail ¶

Function: `out =` tail `(A)` ¶
Function: `out =` tail `(A, k)` ¶

  Get last K rows of an array.

  Returns the array A, subsetted to its last k rows. This means subsetting it to the last `(min (k, size (A, 1)))` elements along dimension 1, and leaving all other dimensions unrestricted.

  A is the array to subset.

  k is the number of rows to get. k defaults to 8 if it is omitted or empty.

  If there are less than k rows in A, returns all rows.

  Returns an array of the same type as A, unless ()-indexing A produces an array of a different type, in which case it returns that type.

  See also: head

#### 8.2.57 tblish.dataset ¶

Class: tblish.dataset ¶

  The `tblish.dataset` class provides convenient access to the various datasets included with Tablicious.

  This class just contains a bunch of static methods, each of which loads the dataset of that name. It is provided as a convenience so you can use tab completion or other run-time introspection on the dataset list.

#### 8.2.57.1 tblish.dataset.airmiles ¶

Static Method: `out =` airmiles `()` ¶

  Passenger Miles on Commercial US Airlines, 1937-1960

  #### Description ¶

  The revenue passenger miles flown by commercial airlines in the United States for each year from 1937 to 1960.

  #### Source ¶

  F.A.A. Statistical Handbook of Aviation.

  #### Examples ¶

      t = tblish.dataset.airmiles;
      plot (t.year, t.miles);
      title ("airmiles data");
      xlabel ("Passenger-miles flown by U.S. commercial airlines")
      ylabel ("airmiles");

#### 8.2.57.2 tblish.dataset.AirPassengers ¶

Static Method: `out =` AirPassengers `()` ¶

  Monthly Airline Passenger Numbers 1949-1960

  #### Description ¶

  The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

  #### Source ¶

  Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976). Time Series Analysis, Forecasting and Control. Third Edition. San Francisco: Holden-Day. Series G.

  #### Examples ¶

      ## TODO: This example needs to be ported from R.

#### 8.2.57.3 tblish.dataset.airquality ¶

Static Method: `out =` airquality `()` ¶

  New York Air Quality Measurements from 1973

  #### Description ¶

  Daily air quality measurements in New York, May to September 1973.

  #### Format ¶

  `Ozone`

    Ozone concentration (ppb)

  `SolarR`

    Solar R (lang)

  `Wind`

    Wind (mph)

  `Temp`

    Temperature (degrees F)

  `Month`

    Month (1-12)

  `Day`

    Day of month (1-31)

  #### Source ¶

  New York State Department of Conservation (ozone data) and the National Weather Service (meteorological data).

  #### References ¶

  Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.

  #### Examples ¶

      t = tblish.dataset.airquality
      # Plot a scatter-plot plus a fitted line, for each combination of measurements
      vars = {"Ozone", "SolarR", "Wind", "Temp" "Month", "Day"};
      n_vars = numel (vars);
      figure;
      for i = 1:n_vars
        for j = 1:n_vars
          if (i == j)
            continue
          endif
          ix_subplot = (n_vars * (j - 1) + i);
          hax = subplot (n_vars, n_vars, ix_subplot);
          var_x = vars{i};
          var_y = vars{j};
          x = t.(var_x);
          y = t.(var_y);
          scatter (hax, x, y, 10);
          # Fit a cubic line to these points
          # TODO: Find out exactly what kind of fitted line R's example is using, and
          # port that.
          hold on
          p = polyfit (x, y, 3);
          x_hat = unique(x);
          p_y = polyval (p, x_hat);
          plot (hax, x_hat, p_y, "r");
        endfor
      endfor

#### 8.2.57.4 tblish.dataset.anscombe ¶

Static Method: `out =` anscombe `()` ¶

  Anscombe’s Quartet of “Identical” Simple Linear Regressions

  #### Description ¶

  Four sets of x/y pairs which have the same statistical properties, but are very different.

  #### Format ¶

  The data comes in an array of 4 structs, each with fields as follows:

  `x`

    The X values for this pair.

  `y`

    The Y values for this pair.

  #### Source ¶

  Tufte, Edward R. (1989). The Visual Display of Quantitative Information. 13–14. Cheshire, CT: Graphics Press.

  #### References ¶

  Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17–21.

  #### Examples ¶

      data = tblish.dataset.anscombe

      # Pick good limits for the plots
      all_x = [data.x];
      all_y = [data.y];
      x_limits = [min(0, min(all_x)) max(all_x)*1.2];
      y_limits = [min(0, min(all_y)) max(all_y)*1.2];

      # Do regression on each pair and plot the input and results
      figure;
      haxs = NaN (1, 4);
      for i_pair = 1:4
        x = data(i_pair).x;
        y = data(i_pair).y;
        # TODO: Port the anova and other characterizations from the R code
        # TODO: Do a linear regression and plot its line
        hax = subplot (2, 2, i_pair);
        haxs(i_pair) = hax;
        xlabel (sprintf ("x%d", i_pair));
        ylabel (sprintf ("y%d", i_pair));
        scatter (x, y, "r");
      endfor

      # Fiddle with the plot axes parameters
      linkaxes (haxs);
      xlim (haxs(1), x_limits);
      ylim (haxs(1), y_limits);

#### 8.2.57.5 tblish.dataset.attenu ¶

Static Method: `out =` attenu `()` ¶

  Joyner-Boore Earthquake Attenuation Data

  #### Description ¶

  Event data for 23 earthquakes in California, showing peak accelerations.

  #### Format ¶

  `event`

    Event number

  `mag`

    Moment magnitude

  `station`

    Station identifier

  `dist`

    Station-hypocenter distance (km)

  `accel`

    Peak acceleration (g)

  #### Source ¶

  Joyner, W.B., D.M. Boore and R.D. Porcella (1981). Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 Imperial Valley, California earthquake. USGS Open File report 81-365. Menlo Park, CA.

  #### References ¶

  Boore, D. M. and Joyner, W. B. (1982). The empirical prediction of ground motion. Bulletin of the Seismological Society of America, 72, S269–S268.

  #### Examples ¶

      # TODO: Port the example code from R
      # It does coplot() and pairs(), which are higher-level plotting tools
      # than core Octave provides. This could turn into a long example if we
      # just use base Octave here.

#### 8.2.57.6 tblish.dataset.attitude ¶

Static Method: `out =` attitude `()` ¶

  The Chatterjee-Price Attitude Data

  #### Description ¶

  Aggregated data from a survey of clerical employees at a large financial organization.

  #### Format ¶

  `rating`

    Overall rating.

  `complaints`

    Handling of employee complaints.

  `privileges`

    Does not allow special privileges.

  `learning`

    Opportunity to learn.

  `raises`

    Raises based on performance.

  `critical`

    Too critical.

  `advance`

    Advancement.

  #### Source ¶

  Chatterjee, S. and Price, B. (1977). Regression Analysis by Example. New York: Wiley. (Section 3.7, p.68ff of 2nd ed.(1991).)

  #### Examples ¶

      t = tblish.dataset.attitude

      tblish.examples.plot_pairs (t);

      # TODO: Display table summary

      # TODO: Whatever those statistical linear-model plots are that R is doing

#### 8.2.57.7 tblish.dataset.austres ¶

Static Method: `out =` austres `()` ¶

  Australian Population

  #### Description ¶

  Numbers of Australian residents measured quarterly from March 1971 to March 1994.

  #### Format ¶

  `date`

    The month of the observation.

  `residents`

    The number of residents.

  #### Source ¶

  Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer-Verlag.

  #### Examples ¶

      t = tblish.dataset.austres

      plot (datenum (t.date), t.residents);
      datetick x
      xlabel ("Month"); ylabel ("Residents"); title ("Australian Residents");

#### 8.2.57.8 tblish.dataset.beavers ¶

Static Method: `out =` beavers `()` ¶

  Body Temperature Series of Two Beavers

  #### Description ¶

  Body temperature readings for two beavers.

  #### Format ¶

  `day`

    Day of observation (in days since the beginning of 1990), December 12–13 (beaver1) and November 3–4 (beaver2).

  `time`

    Time of observation, in the form 0330 for 3:30am

  `temp`

    Measured body temperature in degrees Celsius.

  `activ`

    Indicator of activity outside the retreat.

  #### Source ¶

  P. S. Reynolds (1994) Time-series analyses of beaver body temperatures. Chapter 11 of Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L. and Greenhouse, J. (Eds.) (1994) Case Studies in Biometry. New York: John Wiley and Sons.

  #### Examples ¶

      # TODO: This example needs to be ported from R.

#### 8.2.57.9 tblish.dataset.BJsales ¶

Static Method: `out =` BJsales `()` ¶

  Sales Data with Leading Indicator

  #### Description ¶

  Sales Data with Leading Indicator

  #### Format ¶

  `record`

    Index of the record.

  `lead`

    Leading indicator.

  `sales`

    Sales volume.

  #### Source ¶

  The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

  #### References ¶

  Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

  Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Second edition. New York: Springer-Verlag. p. 414.

  #### Examples ¶

      # TODO: Come up with example code here

#### 8.2.57.10 tblish.dataset.BOD ¶

Static Method: `out =` BOD `()` ¶

  Biochemical Oxygen Demand

  #### Description ¶

  Contains biochemical oxygen demand versus time in an evaluation of water quality.

  #### Format ¶

  `Time`

    Time of the measurement (in days).

  `demand`

    Biochemical oxygen demand (mg/l).

  #### Source ¶

  Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: John Wiley & Sons. Appendix A1.4.

  Originally from: Marske (1967). Biochemical Oxygen Demand Data Interpretation Using Sum of Squares Surface, M.Sc. Thesis, University of Wisconsin – Madison.

  #### Examples ¶

      # TODO: Port this example from R

#### 8.2.57.11 tblish.dataset.cars ¶

Static Method: `out =` cars `()` ¶

  Speed and Stopping Distances of Cars

  #### Description ¶

  Speed of cars and distances taken to stop. Note that the data were recorded in the 1920s.

  #### Format ¶

  `speed`

    Speed (mph).

  `dist`

    Stopping distance (ft).

  #### Source ¶

  Ezekiel, M. (1930). Methods of Correlation Analysis. New York: Wiley.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.cars;

      # TODO: Add Lowess smoothed lines to the plots

      figure;
      plot (t.speed, t.dist, "o");
      xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
      title ("cars data");

      figure;
      loglog (t.speed, t.dist, "o");
      xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
      title ("cars data (logarithmic scales)");

      # TODO: Do the linear model plot

      # Polynomial regression
      figure;
      plot (t.speed, t.dist, "o");
      xlabel ("Speed (mph)"); ylabel ("Stopping distance (ft)");
      title ("cars polynomial regressions");
      hold on
      xlim ([0 25]);
      x2 = linspace (0, 25, 200);
      for degree = 1:4
        [P, S, mu] = polyfit (t.speed, t.dist, degree);
        y2 = polyval(P, x2, [], mu);
        plot (x2, y2);
      endfor

#### 8.2.57.12 tblish.dataset.ChickWeight ¶

Static Method: `out =` ChickWeight `()` ¶

  Weight versus age of chicks on different diets

  #### Format ¶

  `weight`

    a numeric vector giving the body weight of the chick (gm).

  `Time`

    a numeric vector giving the number of days since birth when the measurement was made.

  `Chick`

    an ordered factor with levels 18 < ... < 48 giving a unique identifier for the chick. The ordering of the levels groups chicks on the same diet together and orders them according to their final weight (lightest to heaviest) within diet.

  `Diet`

    a factor with levels 1, ..., 4 indicating which experimental diet the chick received.

  #### Source ¶

  Crowder, M. and Hand, D. (1990). Analysis of Repeated Measures. London: Chapman and Hall. (example 5.3)

  Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis. London: Chapman and Hall. (table A.2)

  Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.

  #### Examples ¶

      t = tblish.dataset.ChickWeight

      tblish.examples.coplot (t, "Time", "weight", "Chick");

#### 8.2.57.13 tblish.dataset.chickwts ¶

Static Method: `out =` chickwts `()` ¶

  Chicken Weights by Feed Type

  #### Description ¶

  An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens.

  Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Their weights in grams after six weeks are given along with feed types.

  #### Format ¶

  `weight`

    Chick weight at six weeks (gm).

  `feed`

    Feed type.

  #### Source ¶

  Anonymous (1948) Biometrika, 35, 214.

  #### References ¶

  McNeil, D. R. (1977). `Interactive Data Analysis`. New York: Wiley.

  #### Examples ¶

      # This example requires the statistics package from Octave Forge

      t = tblish.dataset.chickwts

      # Boxplot by group
      figure
      g = groupby (t, "feed", {
        "weight", @(x) {x}, "weight"
      });
      boxplot (g.weight, 1);
      xlabel ("feed"); ylabel ("Weight at six weeks (gm)");
      xticklabels ([{""} cellstr(g.feed')]);

      # Linear model
      # TODO: This linear model thing and anova

#### 8.2.57.14 tblish.dataset.co2 ¶

Static Method: `out =` co2 `()` ¶

  Mauna Loa Atmospheric CO2 Concentration

  #### Description ¶

  Atmospheric concentrations of CO2 are expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale. Contains monthly observations from 1959 to 1997.

  #### Format ¶

  `date`

    Date of the month of the observation, as datetime.

  `co2`

    CO2 concentration (ppm).

  #### Details ¶

  The values for February, March and April of 1964 were missing and have been obtained by interpolating linearly between the values for January and May of 1964.

  #### Source ¶

  Keeling, C. D. and Whorf, T. P., Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0220.

  ftp://cdiac.esd.ornl.gov/pub/maunaloa-co2/maunaloa.co2.

  #### References ¶

  Cleveland, W. S. (1993). `Visualizing Data`. New Jersey: Summit Press.

  #### Examples ¶

      t = tblish.dataset.co2;

      plot (datenum (t.date), t.co2);
      datetick ("x");
      xlabel ("Time"); ylabel ("Atmospheric concentration of CO2");
      title ("co2 data set");

#### 8.2.57.15 tblish.dataset.crimtab ¶

Static Method: `out =` crimtab `()` ¶

  Student’s 3000 Criminals Data

  #### Description ¶

  Data of 3000 male criminals over 20 years old undergoing their sentences in the chief prisons of England and Wales.

  #### Format ¶

  This dataset contains three separate variables. The `finger_length` and `body_height` variables correspond to the rows and columns of the `count` matrix.

  `finger_length`

    Midpoints of intervals of finger lengths (cm).

  `body_height`

    Body heights (cm).

  `count`

    Number of prisoners in this bin.

  #### Details ¶

  Student is the pseudonym of William Sealy Gosset. In his 1908 paper he wrote (on page 13) at the beginning of section VI entitled Practical Test of the forgoing Equations:

  “Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically. The material used was a correlation table containing the height and left middle finger measurements of 3000 criminals, from a paper by W. R. MacDonell (Biometrika, Vol. I., p. 219). The measurements were written out on 3000 pieces of cardboard, which were then very thoroughly shuffled and drawn at random. As each card was drawn its numbers were written down in a book, which thus contains the measurements of 3000 criminals in a random order. Finally, each consecutive set of 4 was taken as a sample—750 in all—and the mean, standard deviation, and correlation of each sample etermined. The difference between the mean of each sample and the mean of the population was then divided by the standard deviation of the sample, giving us the z of Section III.”

  The table is in fact page 216 and not page 219 in MacDonell(1902). In the MacDonell table, the middle finger lengths were given in mm and the heights in feet/inches intervals, they are both converted into cm here. The midpoints of intervals were used, e.g., where MacDonell has “4’ 7"9/16 – 8"9/16”, we have 142.24 which is 2.54*56 = 2.54*(4’ 8").

  MacDonell credited the source of data (page 178) as follows: “The data on which the memoir is based were obtained, through the kindness of Dr Garson, from the Central Metric Office, New Scotland Yard... He pointed out on page 179 that: “The forms were drawn at random from the mass on the office shelves; we are therefore dealing with a random sampling.”

  #### Source ¶

  http://pbil.univ-lyon1.fr/R/donnees/criminals1902.txt thanks to Jean R. Lobry and Anne-Béatrice Dufour.

  #### References ¶

  Garson, J.G. (1900). The metric system of identification of criminals, as used in in Great Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and Ireland, 30, 161–198.

  MacDonell, W.R. (1902). On criminal anthropometry and the identification of criminals. Biometrika, 1(2), 177–227.

  Student (1908). The probable error of a mean. `Biometrika`, 6, 1–25.

  #### Examples ¶

      # TODO: Port this from R

#### 8.2.57.16 tblish.dataset.cupcake ¶

Static Method: `out =` cupcake `()` ¶

  Google Search popularity for "cupcake", 2004-2019

  #### Description ¶

  Monthly popularity of worldwide Google search results for "cupcake", 2004-2019.

  #### Format ¶

  `Month`

    Month when searches took place

  `Cupcake`

    An indicator of search volume, in unknown units

  #### Source ¶

  Google Trends, https://trends.google.com/trends/explore?q=%2Fm%2F03p1r4&date=all, retrieved 2019-05-04 by Andrew Janke.

  #### Examples ¶

      t = tblish.dataset.cupcake
      plot (datenum (t.Month), t.Cupcake)
      title ('“Cupcake” Google Searches'); xlabel ("Year"); ylabel ("Unknown popularity metric");

#### 8.2.57.17 tblish.dataset.discoveries ¶

Static Method: `out =` discoveries `()` ¶

  Yearly Numbers of Important Discoveries

  #### Description ¶

  The numbers of “great” inventions and scientific discoveries in each year from 1860 to 1959.

  #### Format ¶

  `year`

    Year.

  `discoveries`

    Number of “great” discoveries that year.

  #### Source ¶

  The World Almanac and Book of Facts, 1975 Edition, pages 315–318.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.discoveries;

      plot (t.year, t.discoveries);
      xlabel ("Time"); ylabel ("Number of important discoveries");
      title ("discoveries data set");

#### 8.2.57.18 tblish.dataset.DNase ¶

Static Method: `out =` DNase `()` ¶

  Elisa assay of DNase

  #### Description ¶

  Data obtained during development of an ELISA assay for the recombinant protein DNase in rat serum.

  #### Format ¶

  `Run`

    Ordered `categorical` indicating the assay run.

  `conc`

    Known concentration of the protein (ng/ml).

  `density`

    Measured optical density in the assay (dimensionless).

  #### Source ¶

  Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 134)

  Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

  #### Examples ¶

      t = tblish.dataset.DNase;

      # TODO: Port this from R

      tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @scatter);
      tblish.examples.coplot (t, "conc", "density", "Run", "PlotFcn", @loglog, ...
        "PlotArgs", {"o"});

#### 8.2.57.19 tblish.dataset.esoph ¶

Static Method: `out =` esoph `()` ¶

  Smoking, Alcohol and Esophageal Cancer

  #### Description ¶

  Data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.

  #### Format ¶

  `item`

    Age group (years).

  `alcgp`

    Alcohol consumption (gm/day).

  `tobgp`

    Tobacco consumption (gm/day).

  `ncases`

    Number of cases.

  `ncontrols`

    Number of controls

  #### Source ¶

  Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. Oxford: IARC Lyon / Oxford University Press.

  #### Examples ¶

      # TODO: Port this from R

      # TODO: Port the anova output

      # TODO: Port the fancy plot
      # This involves a "mosaic plot", which is not supported by Octave, so this will
      # take some work.

#### 8.2.57.20 tblish.dataset.euro ¶

Static Method: `out =` euro `()` ¶

  Conversion Rates of Euro Currencies

  #### Description ¶

  Conversion rates between the various Euro currencies.

  #### Format ¶

  This data comes in two separate variables.

  `euro`

    An 11-long vector of the value of 1 Euro in all participating currencies.

  `euro_cross`

    An 11-by-11 matrix of conversion rates between various Euro currencies.

  `euro_date`

    The date upon which these Euro conversion rates were fixed.

  #### Details ¶

  The data set euro contains the value of 1 Euro in all currencies participating in the European monetary union (Austrian Schilling ATS, Belgian Franc BEF, German Mark DEM, Spanish Peseta ESP, Finnish Markka FIM, French Franc FRF, Irish Punt IEP, Italian Lira ITL, Luxembourg Franc LUF, Dutch Guilder NLG and Portuguese Escudo PTE). These conversion rates were fixed by the European Union on December 31, 1998. To convert old prices to Euro prices, divide by the respective rate and round to 2 digits.

  #### Source ¶

  Unknown.

  This example data set was derived from the R 3.6.0 example datasets, and they do not specify a source.

  #### Examples ¶

      # TODO: Port this from R

      # TODO: Example conversion

      # TODO: "dot chart" showing euro-to-whatever conversion rates and vice versa

#### 8.2.57.21 tblish.dataset.eurodist ¶

Static Method: `out =` eurodist `()` ¶

  Distances Between European Cities and Between US Cities

  #### Description ¶

  `eurodist` gives road distances (in km) between 21 cities in Europe. The data are taken from a table in The Cambridge Encyclopaedia.

  `UScitiesD` gives “straight line” distances between 10 cities in the US.

  #### Format ¶

  `eurodist`

    ?????

  TODO: Finish this.

  #### Source ¶

  Crystal, D. Ed. (1990). The Cambridge Encyclopaedia. Cambridge: Cambridge University Press.

  The US cities distances were provided by Pierre Legendre.

  #### Examples ¶

#### 8.2.57.22 tblish.dataset.EuStockMarkets ¶

Static Method: `out =` EuStockMarkets `()` ¶

  Daily Closing Prices of Major European Stock Indices

  #### Description ¶

  Contains the daily closing prices of major European stock indices: Germany DAX (Ibis), Switzerland SMI, France CAC, and UK FTSE. The data are sampled in business time, i.e., weekends and holidays are omitted.

  #### Format ¶

  A multivariate time series with 1860 observations on 4 variables.

  The starting date is the 130th day of 1991, with a frequency of 260 observations per year.

  #### Source ¶

  The data were kindly provided by Erste Bank AG, Vienna, Austria.

  #### Examples ¶

      t = tblish.dataset.EuStockMarkets;

      # The fact that we're doing this munging means that table might have
      # been the wrong structure for this data in the first place

      t2 = removevars (t, "day");
      index_names = t2.Properties.VariableNames;
      day = 1:height (t2);
      price = table2array (t2);

      price0 = price(1,:);

      rel_price = price ./ repmat (price0, [size(price, 1) 1]);

      figure;
      plot (day, rel_price);
      legend (index_names);
      xlabel ("Business day");
      ylabel ("Relative price");

#### 8.2.57.23 tblish.dataset.faithful ¶

Static Method: `out =` faithful `()` ¶

  Old Faithful Geyser Data

  #### Description ¶

  Waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.

  #### Format ¶

  `eruptions`

    Eruption time (mins).

  `waiting`

    Waiting time to next eruption (mins).

  #### Source ¶

  W. Härdle.

  #### References ¶

  Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.

  Azzalini, A. and Bowman, A. W. (1990). A look at some data on the Old Faithful geyser. Applied Statistics, 39, 357–365.

  #### Examples ¶

      t = tblish.dataset.faithful;

      # Munge the data, rounding eruption time to the second
      e60 = 60 * t.eruptions;
      ne60 = round (e60);
      # TODO: Port zapsmall to Octave
      eruptions = ne60 / 60;
      # TODO: Display mean relative difference and bins summary

      # Histogram of rounded eruption times
      figure
      hist (ne60, max (ne60))
      xlabel ("Eruption time (sec)")
      ylabel ("n")
      title ("faithful data: Eruptions of Old Faithful")

      # Scatter plot of eruption time vs waiting time
      figure
      scatter (t.eruptions, t.waiting)
      xlabel ("Eruption time (min)")
      ylabel ("Waiting time to next eruption (min)")
      title ("faithful data: Eruptions of Old Faithful")
      # TODO: Port Lowess smoothing to Octave

#### 8.2.57.24 tblish.dataset.Formaldehyde ¶

Static Method: `out =` Formaldehyde `()` ¶

  Determination of Formaldehyde

  #### Description ¶

  These data are from a chemical experiment to prepare a standard curve for the determination of formaldehyde by the addition of chromatropic acid and concentrated sulphuric acid and the reading of the resulting purple color on a spectrophotometer.

  #### Format ¶

  `record`

    Observation record number.

  `carb`

    Carbohydrate (ml).

  `optden`

    Optical Density

  #### Source ¶

  Bennett, N. A. and N. L. Franklin (1954). Statistical Analysis in Chemistry and the Chemical Industry. New York: Wiley.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.Formaldehyde;

      figure
      scatter (t.carb, t.optden)
      # TODO: Add a linear model line
      xlabel ("Carbohydrate (ml)")
      ylabel ("Optical Density")
      title ("Formaldehyde data")

      # TODO: Add linear model summary output
      # TOD: Add linear model summary plot

#### 8.2.57.25 tblish.dataset.freeny ¶

Static Method: `out =` freeny `()` ¶

  Freeny’s Revenue Data

  #### Description ¶

  Freeny’s data on quarterly revenue and explanatory variables.

  #### Format ¶

  Freeny’s dataset consists of one observed dependent variable (revenue) and four explanatory variables (lagged quartery revenue, price index, income level, and market potential).

  `date`

    Start date of the quarter for the observation.

  `y`

    Observed quarterly revenue. TODO: Determine units (probably millions of USD?)

  `lag_quarterly_revenue`

    Quarterly revenue (`y`), lagged 1 quarter.

  `price_index`

    A price index

  `income_level`

    ??? TODO: Fill this in

  `market_potential`

    ??? TODO: Fill this in

  #### Source ¶

  Freeny, A. E. (1977). A Portable Linear Regression Package with Test Programs. Bell Laboratories memorandum.

  #### References ¶

  Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

  #### Examples ¶

      t = tblish.dataset.freeny;

      summary (t)

      tblish.examples.plot_pairs (removevars (t, "date"))

      # TODO: Create linear model and print summary

      # TODO: Linear model plot

#### 8.2.57.26 tblish.dataset.HairEyeColor ¶

Static Method: `out =` HairEyeColor `()` ¶

  Hair and Eye Color of Statistics Students

  #### Description ¶

  Distribution of hair and eye color and sex in 592 statistics students.

  #### Format ¶

  This data set comes in multiple variables

  `n`

    A 3-dimensional array containing the counts of students in each bucket. It is arranged as hair-by-eye-by-sex.

  `hair`

    Hair colors for the indexes along dimension 1.

  `eye`

    Eye colors for the indexes along dimension 2.

  `sex`

    Sexes for the indexes along dimension 3.

  #### Details ¶

  The Hair x Eye table comes rom a survey of students at the University of Delaware reported by Snee (1974). The split by Sex was added by Friendly (1992a) for didactic purposes.

  This data set is useful for illustrating various techniques for the analysis of contingency tables, such as the standard chi-squared test or, more generally, log-linear modelling, and graphical methods such as mosaic plots, sieve diagrams or association plots.

  #### Source ¶

  http://euclid.psych.yorku.ca/ftp/sas/vcd/catdata/haireye.sas

  Snee (1974) gives the two-way table aggregated over Sex. The Sex split of the ‘Brown hair, Brown eye’ cell was changed to agree with that used by Friendly (2000).

  #### References ¶

  Snee, R. D. (1974). Graphical display of two-way contingency tables. The American Statistician, 28, 9–12.

  Friendly, M. (1992a). Graphical methods for categorical data. SAS User Group International Conference Proceedings, 17, 190–200. http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

  Friendly, M. (1992b). Mosaic displays for loglinear models. Proceedings of the Statistical Graphics Section, American Statistical Association, pp. 61–68. http://www.math.yorku.ca/SCS/Papers/asa92.html

  Friendly, M. (2000). Visualizing Categorical Data. SAS Institute, ISBN 1-58025-660-0.

  #### Examples ¶

      tblish.dataset.HairEyeColor

      # TODO: Aggregate over sex and display a table of counts

      # TODO: Port mosaic plot to Octave

#### 8.2.57.27 tblish.dataset.Harman23cor ¶

Static Method: `out =` Harman23cor `()` ¶

  Harman Example 2.3

  #### Description ¶

  A correlation matrix of eight physical measurements on 305 girls between ages seven and seventeen.

  #### Format ¶

  `cov`

    An 8-by-8 correlation matrix.

  `names`

    Names of the variables corresponding to the indexes of the correlation matrix’s dimensions.

  #### Source ¶

  Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 2.3.

  #### Examples ¶

      tblish.dataset.Harman23cor;

      # TODO: Port factanal to Octave

#### 8.2.57.28 tblish.dataset.Harman74cor ¶

Static Method: `out =` Harman74cor `()` ¶

  Harman Example 7.4

  #### Description ¶

  A correlation matrix of 24 psychological tests given to 145 seventh and eighth-grade children in a Chicago suburb by Holzinger and Swineford.

  #### Format ¶

  `cov`

    A 2-dimensional correlation matrix.

  `vars`

    Names of the variables corresponding to the indexes along the dimensions of `cov`.

  #### Source ¶

  Harman, H. H. (1976). Modern Factor Analysis, Third Edition Revised. Chicago: University of Chicago Press. Table 7.4.

  #### Examples ¶

      tblish.dataset.Harman74cor;

      # TODO: Port factanal to Octave

#### 8.2.57.29 tblish.dataset.Indometh ¶

Static Method: `out =` Indometh `()` ¶

  Pharmacokinetics of Indomethacin

  #### Description ¶

  Data on the pharmacokinetics of indometacin (or, older spelling, ‘indomethacin’).

  #### Format ¶

  `Subject`

    Subject identifier.

  `time`

    Time since drug administration at which samples were drawn (hours).

  `conc`

    Plasma concentration of indomethacin (mcg/ml).

  #### Details ¶

  Each of the six subjects were given an intravenous injection of indometacin.

  #### Source ¶

  Kwan, Breault, Umbenhauer, McMahon and Duggan (1976). Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Journal of Pharmacokinetics and Biopharmaceutics 4, 255–280.

  Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.2.4, p. 129)

  Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

#### 8.2.57.30 tblish.dataset.infert ¶

Static Method: `out =` infert `()` ¶

  Infertility after Spontaneous and Induced Abortion

  #### Description ¶

  This is a matched case-control study dating from before the availability of conditional logistic regression.

  #### Format ¶

  `education`

    Index of the record.

  `age`

    Age in years of case.

  `parity`

    Count.

  `induced`

    Number of prior induced abortions, grouped into “0”, “1”, or “2 or more”.

  `case_status`

    0 = control, 1 = case.

  `spontaneous`

    Number of prior spontaneous abortions, grouped into “0”, “1”, or “2 or more”.

  `stratum`

    Matched set number.

  `pooled_stratum`

    Stratum number.

  #### Note ¶

  One case with two prior spontaneous abortions and two prior induced abortions is omitted.

  #### Source ¶

  Trichopoulos et al (1976). Br. J. of Obst. and Gynaec. 83, 645–650.

  #### Examples ¶

      t = tblish.dataset.infert;

      # TODO: Port glm() (generalized linear model) stuff to Octave

#### 8.2.57.31 tblish.dataset.InsectSprays ¶

Static Method: `out =` InsectSprays `()` ¶

  Effectiveness of Insect Sprays

  #### Description ¶

  The counts of insects in agricultural experimental units treated with different insecticides.

  #### Format ¶

  `spray`

    The type of spray.

  `count`

    Insect count.

  #### Source ¶

  Beall, G., (1942). The Transformation of data from entomological field experiments. Biometrika, 29, 243–262.

  #### References ¶

  McNeil, D. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.InsectSprays;

      # TODO: boxplot

      # TODO: AOV plots

#### 8.2.57.32 tblish.dataset.iris ¶

Static Method: `out =` iris `()` ¶

  The Fisher Iris dataset: measurements of various flowers

  #### Description ¶

  This is the classic Fisher Iris dataset.

  #### Format ¶

  `Species`

    The species of flower being measured.

  `SepalLength`

    Length of sepals, in centimeters.

  `SepalWidth`

    Width of sepals, in centimeters.

  `PetalLength`

    Length of petals, in centimeters.

  `PetalWidth`

    Width of petals, in centimeters.

  #### Source ¶

  http://archive.ics.uci.edu/ml/datasets/Iris

  #### References ¶

  https://en.wikipedia.org/wiki/Iris_flower_data_set

  Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188. also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).

  Duda, R.O., & Hart, P.E. (1973). Pattern Classification and Scene Analysis. (Q327.D83) New York: John Wiley & Sons. ISBN 0-471-22361-1. See page 218.

  The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 2–5.

  #### Examples ¶

      # TODO: Port this example from R

#### 8.2.57.33 tblish.dataset.islands ¶

Static Method: `out =` islands `()` ¶

  Areas of the World’s Major Landmasses

  #### Description ¶

  The areas in thousands of square miles of the landmasses which exceed 10,000 square miles.

  #### Format ¶

  `name`

    The name of the island.

  `area`

    The area, in thousands of square miles.

  #### Source ¶

  The World Almanac and Book of Facts, 1975, page 406.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.islands;

      # TODO: Port dot chart to Octave

#### 8.2.57.34 tblish.dataset.JohnsonJohnson ¶

Static Method: `out =` JohnsonJohnson `()` ¶

  Quarterly Earnings per Johnson & Johnson Share

  #### Description ¶

  Quarterly earnings (dollars) per Johnson & Johnson share 1960–80.

  #### Format ¶

  `date`

    Start date of the quarter.

  `earnings`

    Earnings per share (USD).

  #### Source ¶

  Shumway, R. H. and Stoffer, D. S. (2000). Time Series Analysis and its Applications. Second Edition. New York: Springer. Example 1.1.

  #### Examples ¶

      t = tblish.dataset.JohnsonJohnson

      # TODO: Yikes, look at all those plots. Port them to Octave.

#### 8.2.57.35 tblish.dataset.LakeHuron ¶

Static Method: `out =` LakeHuron `()` ¶

  Level of Lake Huron 1875-1972

  #### Description ¶

  Annual measurements of the level, in feet, of Lake Huron 1875–1972.

  #### Format ¶

  `year`

    Year of the measurement

  `level`

    Lake level (ft).

  #### Source ¶

  Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series A, page 555.

  Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. New York: Springer. Sections 5.1 and 7.6.

  #### Examples ¶

      t = tblish.dataset.LakeHuron;

      plot (t.year, t.level)
      xlabel ("Year")
      ylabel ("Lake level (ft)")
      title ("Level of Lake Huron")

#### 8.2.57.36 tblish.dataset.lh ¶

Static Method: `out =` lh `()` ¶

  Luteinizing Hormone in Blood Samples

  #### Description ¶

  A regular time series giving the luteinizing hormone in blood samples at 10 minute intervals from a human female, 48 samples.

  #### Format ¶

  `sample`

    The number of the observation.

  `lh`

    Level of luteinizing hormone.

  #### Source ¶

  P.J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. Table A.1, series 3.

  #### Examples ¶

      t = tblish.dataset.lh;

      plot (t.sample, t.lh);
      xlabel ("Sample Number");
      ylabel ("lh level");

#### 8.2.57.37 tblish.dataset.LifeCycleSavings ¶

Static Method: `out =` LifeCycleSavings `()` ¶

  Intercountry Life-Cycle Savings Data

  #### Description ¶

  Data on the savings ratio 1960–1970.

  #### Format ¶

  `country`

    Name of the country.

  `sr`

    Aggregate personal savings.

  `pop15`

    Percentage of population under 15.

  `pop75`

    Percentage of population over 75.

  `dpi`

    Real per-capita disposable income.

  `ddpi`

    Percent growth rate of dpi.

  #### Details ¶

  Under the life-cycle savings hypothesis as developed by Franco Modigliani, the savings ratio (aggregate personal saving divided by disposable income) is explained by per-capita disposable income, the percentage rate of change in per-capita disposable income, and two demographic variables: the percentage of population less than 15 years old and the percentage of the population over 75 years old. The data are averaged over the decade 1960–1970 to remove the business cycle or other short-term fluctuations.

  #### Source ¶

  The data were obtained from Belsley, Kuh and Welsch (1980). They in turn obtained the data from Sterling (1977).

  #### References ¶

  Sterling, Arnie (1977). Unpublished BS Thesis. Massachusetts Institute of Technology.

  Belsley, D. A., Kuh. E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.LifeCycleSavings;

      # TODO: linear model

      # TODO: pairs plot with Lowess smoothed line

#### 8.2.57.38 tblish.dataset.Loblolly ¶

Static Method: `out =` Loblolly `()` ¶

  Growth of Loblolly pine trees

  #### Description ¶

  Records of the growth of Loblolly pine trees.

  #### Format ¶

  `height`

    Tree height (ft).

  `age`

    Tree age (years).

  `Seed`

    Seed source for the tree. Ordering is according to increasing maximum height.

  #### Source ¶

  Kung, F. H. (1986). Fitting logistic growth curve with predetermined carrying capacity. Proceedings of the Statistical Computing Section, American Statistical Association, 340–343.

  Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

  #### Examples ¶

      t = tblish.dataset.Loblolly;

      t2 = t(t.Seed == "329",:);
      scatter (t2.age, t2.height)
      xlabel ("Tree age (yr)");
      ylabel ("Tree height (ft)");
      title ("Loblolly data and fitted curve (Seed 329 only)")

      # TODO: Compute and plot fitted curve

#### 8.2.57.39 tblish.dataset.longley ¶

Static Method: `out =` longley `()` ¶

  Longley’s Economic Regression Data

  #### Description ¶

  A macroeconomic data set which provides a well-known example for a highly collinear regression.

  #### Format ¶

  `Year`

    The year.

  `GNP_deflator`

    GNP implicit price deflator (1954=100).

  `GNP`

    Gross National Product.

  `Unemployed`

    Number of unemployed.

  `Armed_Forces`

    Number of people in the armed forces.

  `Population`

    “Noninstitutionalized” population ≥ 14 years of age.

  `Employed`

    Number of people employed.

  #### Source ¶

  J. W. Longley (1967). An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association, 62, 819–841.

  #### References ¶

  Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

  #### Examples ¶

      t = tblish.dataset.longley;

      # TODO: Linear model
      # TODO: opar plot

#### 8.2.57.40 tblish.dataset.lynx ¶

Static Method: `out =` lynx `()` ¶

  Annual Canadian Lynx trappings 1821-1934

  #### Description ¶

  Annual numbers of lynx trappings for 1821–1934 in Canada. Taken from Brockwell & Davis (1991), this appears to be the series considered by Campbell & Walker (1977).

  #### Format ¶

  `year`

    Year of the record.

  `lynx`

    Number of lynx trapped.

  #### Source ¶

  Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. New York: Springer. Series G (page 557).

  #### References ¶

  Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

  Campbell, M. J. and Walker, A. M. (1977). A Survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis. Journal of the Royal Statistical Society series A, 140, 411–431.

  #### Examples ¶

      t = tblish.dataset.lynx;

      plot (t.year, t.lynx);
      xlabel ("Year");
      ylabel ("Lynx Trapped");

#### 8.2.57.41 tblish.dataset.morley ¶

Static Method: `out =` morley `()` ¶

  Michelson Speed of Light Data

  #### Description ¶

  A classical data of Michelson (but not this one with Morley) on measurements done in 1879 on the speed of light. The data consists of five experiments, each consisting of 20 consecutive ‘runs’. The response is the speed of light measurement, suitably coded (km/sec, with 299000 subtracted).

  #### Format ¶

  `Expt`

    The experiment number, from 1 to 5.

  `Run`

    The run number within each experiment.

  `Speed`

    Speed-of-light measurement.

  #### Details ¶

  The data is here viewed as a randomized block experiment with `experiment` and `run` as the factors. `run` may also be considered a quantitative variate to account for linear (or polynomial) changes in the measurement over the course of a single experiment.

  #### Source ¶

  A. J. Weekes (1986). A Genstat Primer. London: Edward Arnold.

  S. M. Stigler (1977). Do robust estimators work with real data? Annals of Statistics 5, 1055–1098. (See Table 6.)

  A. A. Michelson (1882). Experimental determination of the velocity of light made at the United States Naval Academy, Annapolis. Astronomic Papers, 1, 135–8. U.S. Nautical Almanac Office. (See Table 24.).

  #### Examples ¶

      t = tblish.dataset.morley;

      # TODO: Port to Octave

#### 8.2.57.42 tblish.dataset.mtcars ¶

Static Method: `out =` mtcars `()` ¶

  Motor Trend 1974 Car Road Tests

  #### Description ¶

  The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

  #### Format ¶

  `mpg`

    Fuel efficiency in miles/gallon

  `cyl`

    Number of cylinders

  `disp`

    Displacement (cu. in.)

  `hp`

    Gross horsepower

  `drat`

    Rear axle ratio

  `wt`

    Weight (1,000 lbs)

  `qsec`

    1/4 mile time

  `vs`

    Engine type (0 = V-shaped, 1 = straight)

  `am`

    Transmission type (0 = automatic, 1 = manual)

  `gear`

    Number of forward gears

  `carb`

    Number of carburetors

  #### Note ¶

  Henderson and Velleman (1981) comment in a footnote to Table 1: “Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.”

  #### Source ¶

  Henderson and Velleman (1981). Building multiple regression models interactively. Biometrics, 37, 391–411.

  #### Examples ¶

      # TODO: Port this example from R

#### 8.2.57.43 tblish.dataset.nhtemp ¶

Static Method: `out =` nhtemp `()` ¶

  Average Yearly Temperatures in New Haven

  #### Description ¶

  The mean annual temperature in degrees Fahrenheit in New Haven, Connecticut, from 1912 to 1971.

  #### Format ¶

  `year`

    Year of the observation.

  `temp`

    Mean annual temperature (degrees F).

  #### Source ¶

  Vaux, J. E. and Brinker, N. B. (1972) Cycles, 1972, 117–121.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.nhtemp;

      plot (t.year, t.temp);
      title ("nhtemp data");
      xlabel ("Mean annual temperature in New Haven, CT (deg. F)");

#### 8.2.57.44 tblish.dataset.Nile ¶

Static Method: `out =` Nile `()` ¶

  Flow of the River Nile

  #### Description ¶

  Measurements of the annual flow of the river Nile at Aswan (formerly Assuan), 1871–1970, in m^3, “with apparent changepoint near 1898” (Cobb(1978), Table 1, p.249).

  #### Format ¶

  `year`

    Year of the record.

  `flow`

    Annual flow (cubic meters).

  #### Source ¶

  Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/DKbook.html

  #### References ¶

  Balke, N. S. (1993). Detecting level shifts in time series. Journal of Business and Economic Statistics, 11, 81–92.

  Cobb, G. W. (1978). The problem of the Nile: conditional solution to a change-point problem. Biometrika 65, 243–51.

  #### Examples ¶

      t = tblish.dataset.Nile;

      figure
      plot (t.year, t.flow);

      # TODO: Port the rest of the example to Octave

#### 8.2.57.45 tblish.dataset.nottem ¶

Static Method: `out =` nottem `()` ¶

  Average Monthly Temperatures at Nottingham, 1920-1939

  #### Description ¶

  A time series object containing average air temperatures at Nottingham Castle in degrees Fahrenheit for 20 years.

  #### Format ¶

  `record`

    Index of the record.

  `lead`

    Leading indicator.

  `sales`

    Sales volume.

  #### Source ¶

  Anderson, O. D. (1976). Time Series Analysis and Forecasting: The Box-Jenkins approach. London: Butterworths. Series R.

  #### Examples ¶

      # TODO: Come up with example code here

#### 8.2.57.46 tblish.dataset.npk ¶

Static Method: `out =` npk `()` ¶

  Classical N, P, K Factorial Experiment

  #### Description ¶

  A classical N, P, K (nitrogen, phosphate, potassium) factorial experiment on the growth of peas conducted on 6 blocks. Each half of a fractional factorial design confounding the NPK interaction was used on 3 of the plots.

  #### Format ¶

  `block`

    Which block (1 to 6).

  `N`

    Indicator (0/1) for the application of nitrogen.

  `P`

    Indicator (0/1) for the application of phosphate.

  `K`

    Indicator (0/1) for the application of potassium.

  `yield`

    Yield of peas, in pounds/plot. Plots were 1/70 acre.

  #### Source ¶

  Imperial College, London, M.Sc. exercise sheet.

  #### References ¶

  Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth edition. New York: Springer.

  #### Examples ¶

      t = tblish.dataset.npk;

      # TODO: Port aov() and LM to Octave

#### 8.2.57.47 tblish.dataset.occupationalStatus ¶

Static Method: `out =` occupationalStatus `()` ¶

  Occupational Status of Fathers and their Sons

  #### Description ¶

  Cross-classification of a sample of British males according to each subject’s occupational status and his father’s occupational status.

  #### Format ¶

  An 8-by-8 matrix of counts, with classifying fators `origin` (father’s occupational status, levels 1:8) and `destination` (son’s occupational status, levels 1:8).

  #### Source ¶

  Goodman, L. A. (1979). Simple Models for the Analysis of Association in Cross-Classifications having Ordered Categories. J. Am. Stat. Assoc., 74 (367), 537–552.

  #### Examples ¶

      # TODO: Come up with example code here

#### 8.2.57.48 tblish.dataset.Orange ¶

Static Method: `out =` Orange `()` ¶

  Growth of Orange Trees

  #### Description ¶

  Records of the growth of orange trees.

  #### Format ¶

  `Tree`

    A categorical indicating on which tree the measurement is made. Ordering is according to increasing maximum diameter.

  `age`

    Age of the tree (days since 1968-12-31).

  `circumference`

    Trunk circumference (mm). This is probably “circumference at breast height”, a standard measurement in forestry.

  #### Source ¶

  The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

  #### References ¶

  Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed). New York: Wiley. (exercise 24.N).

  Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

  #### Examples ¶

      t = tblish.dataset.Orange;

      # TODO: Port coplot to Octave

      # TODO: Linear model

#### 8.2.57.49 tblish.dataset.OrchardSprays ¶

Static Method: `out =` OrchardSprays `()` ¶

  Potency of Orchard Sprays

  #### Description ¶

  An experiment was conducted to assess the potency of various constituents of orchard sprays in repelling honeybees, using a Latin square design.

  #### Format ¶

  `rowpos`

    Row of the design.

  `colpos`

    Column of the design

  `treatment`

    Treatment level.

  `decrease`

    Response.

  #### Details ¶

  Individual cells of dry comb were filled with measured amounts of lime sulphur emulsion in sucrose solution. Seven different concentrations of lime sulphur ranging from a concentration of 1/100 to 1/1,562,500 in successive factors of 1/5 were used as well as a solution containing no lime sulphur.

  The responses for the different solutions were obtained by releasing 100 bees into the chamber for two hours, and then measuring the decrease in volume of the solutions in the various cells.

  An 8 x 8 Latin square design was used and the treatments were coded as follows:

  A – highest level of lime sulphur B – next highest level of lime sulphur … G – lowest level of lime sulphur H – no lime sulphur

  #### Source ¶

  Finney, D. J. (1947). Probit Analysis. Cambridge.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.OrchardSprays;

      tblish.examples.plot_pairs (t);

#### 8.2.57.50 tblish.dataset.PlantGrowth ¶

Static Method: `out =` PlantGrowth `()` ¶

  Results from an Experiment on Plant Growth

  #### Description ¶

  Results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.

  #### Format ¶

  `group`

    Treatment condition group.

  `weight`

    Weight of plants.

  #### Source ¶

  Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.

  #### Examples ¶

      t = tblish.dataset.PlantGrowth;

      # TODO: Port anova to Octave

#### 8.2.57.51 tblish.dataset.precip ¶

Static Method: `out =` precip `()` ¶

  Annual Precipitation in US Cities

  #### Description ¶

  The average amount of precipitation (rainfall) in inches for each of 70 United States (and Puerto Rico) cities.

  #### Format ¶

  `city`

    City observed.

  `precip`

    Annual precipitation (in).

  #### Source ¶

  Statistical Abstracts of the United States, 1975.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.precip;

      # TODO: Port dot plot to Octave

#### 8.2.57.52 tblish.dataset.presidents ¶

Static Method: `out =` presidents `()` ¶

  Quarterly Approval Ratings of US Presidents

  #### Description ¶

  The (approximately) quarterly approval rating for the President of the United States from the first quarter of 1945 to the last quarter of 1974.

  #### Format ¶

  `date`

    Approximate date of the observation.

  `approval`

    Approval rating (%).

  #### Details ¶

  The data are actually a fudged version of the approval ratings. See McNeil’s book for details.

  #### Source ¶

  The Gallup Organisation.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.presidents;

      figure
      plot (datenum (t.date), t.approval)
      datetick ("x")
      xlabel ("Date")
      ylabel ("Approval rating (%)")
      title ("presidents data")

#### 8.2.57.53 tblish.dataset.pressure ¶

Static Method: `out =` pressure `()` ¶

  Vapor Pressure of Mercury as a Function of Temperature

  #### Description ¶

  Data on the relation between temperature in degrees Celsius and vapor pressure of mercury in millimeters (of mercury).

  #### Format ¶

  `temperature`

    Temperature (deg C).

  `pressure`

    Pressure (mm Hg).

  #### Source ¶

  Weast, R. C., ed. (1973). Handbook of Chemistry and Physics. Cleveland: CRC Press.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.pressure;

      figure
      plot (t.temperature, t.pressure)
      xlabel ("Temperature (deg C)")
      ylabel ("Pressure (mm of Hg)")
      title ("pressure data: Vapor Pressure of Mercury")

      figure
      semilogy (t.temperature, t.pressure)
      xlabel ("Temperature (deg C)")
      ylabel ("Pressure (mm of Hg)")
      title ("pressure data: Vapor Pressure of Mercury")

#### 8.2.57.54 tblish.dataset.Puromycin ¶

Static Method: `out =` Puromycin `()` ¶

  Reaction Velocity of an Enzymatic Reaction

  #### Description ¶

  Reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.

  #### Format ¶

  `state`

    Whether the cell was treated.

  `conc`

    Substrate concentrations (ppm).

  `rate`

    Instantaneous reaction rates (counts/min/min).

  #### Details ¶

  Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.

  #### Source ¶

  The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

  #### References ¶

  Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Appendix A1.3.

  Treloar, M. A. (1974). Effects of Puromycin on Galactosyltransferase in Golgi Membranes. M.Sc. Thesis, U. of Toronto.

  #### Examples ¶

      t = tblish.dataset.Puromycin;

      # TODO: Port example to Octave

#### 8.2.57.55 tblish.dataset.quakes ¶

Static Method: `out =` quakes `()` ¶

  Locations of Earthquakes off Fiji

  #### Description ¶

  The data set give the locations of 1000 seismic events of MB > 4.0. The events occurred in a cube near Fiji since 1964.

  #### Format ¶

  `lat`

    Latitude of event.

  `long`

    Longitude of event.

  `depth`

    Depth (km).

  `mag`

    Richter magnitude.

  `stations`

    Number of stations reporting.

  #### Details ¶

  There are two clear planes of seismic activity. One is a major plate junction; the other is the Tonga trench off New Zealand. These data constitute a subsample from a larger dataset of containing 5000 observations.

  #### Source ¶

  This is one of the Harvard PRIM-H project data sets. They in turn obtained it from Dr. John Woodhouse, Dept. of Geophysics, Harvard University.

  #### References ¶

  G. E. P. Box and G. M. Jenkins (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

  P. J. Brockwell and R. A. Davis (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

  #### Examples ¶

      # TODO: Come up with example code here

#### 8.2.57.56 tblish.dataset.randu ¶

Static Method: `out =` randu `()` ¶

  Random Numbers from Congruential Generator RANDU

  #### Description ¶

  400 triples of successive random numbers were taken from the VAX FORTRAN function RANDU running under VMS 1.5.

  #### Format ¶

  `record`

    Index of the record.

  `x`

    X value of the triple.

  `y`

    Y value of the triple.

  `z`

    Z value of the triple.

  #### Details ¶

  In three dimensional displays it is evident that the triples fall on 15 parallel planes in 3-space. This can be shown theoretically to be true for all triples from the RANDU generator.

  These particular 400 triples start 5 apart in the sequence, that is they are ((U[5i+1], U[5i+2], U[5i+3]), i= 0, ..., 399), and they are rounded to 6 decimal places.

  Under VMS versions 2.0 and higher, this problem has been fixed.

  #### Source ¶

  David Donoho

  #### Examples ¶

      t = tblish.dataset.randu;

#### 8.2.57.57 tblish.dataset.rivers ¶

Static Method: `out =` rivers `()` ¶

  Lengths of Major North American Rivers

  #### Description ¶

  This data set gives the lengths (in miles) of 141 “major” rivers in North America, as compiled by the US Geological Survey.

  #### Format ¶

  `rivers`

    A vector containing 141 observations.

  #### Source ¶

  World Almanac and Book of Facts, 1975, page 406.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      tblish.dataset.rivers;

      longest_river = max (rivers)
      shortest_river = min (rivers)

#### 8.2.57.58 tblish.dataset.rock ¶

Static Method: `out =` rock `()` ¶

  Measurements on Petroleum Rock Samples

  #### Description ¶

  Measurements on 48 rock samples from a petroleum reservoir.

  #### Format ¶

  `area`

    Area of pores space, in pixels out of 256 by 256.

  `peri`

    Perimeter in pixels.

  `shape`

    Perimeter/sqrt(area).

  `perm`

    Permeability in milli-Darcies.

  #### Details ¶

  Twelve core samples from petroleum reservoirs were sampled by 4 cross-sections. Each core sample was measured for permeability, and each cross-section has total area of pores, total perimeter of pores, and shape.

  #### Source ¶

  Data from BP Research, image analysis by Ronit Katz, U. Oxford.

  #### Examples ¶

      t = tblish.dataset.rock;

      figure
      scatter (t.area, t.perm)
      xlabel ("Area of pores space (pixels out of 256x256)")
      ylabel ("Permeability (milli-Darcies)")

#### 8.2.57.59 tblish.dataset.sleep ¶

Static Method: `out =` sleep `()` ¶

  Student’s Sleep Data

  #### Description ¶

  Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.

  #### Format ¶

  `id`

    Patient ID.

  `group`

    Drug given.

  `extra`

    Increase in hours of sleep.

  #### Details ¶

  The `group` variable name may be misleading about the data: They represent measurements on 10 persons, not in groups.

  #### Source ¶

  Cushny, A. R. and Peebles, A. R. (1905). The action of optical isomers: II hyoscines. The Journal of Physiology, 32, 501–510.

  Student (1908). The probable error of the mean. Biometrika, 6, 20.

  #### References ¶

  Scheffé, Henry (1959). The Analysis of Variance. New York, NY: Wiley.

  #### Examples ¶

      t = tblish.dataset.sleep;

      # TODO: Port to Octave

#### 8.2.57.60 tblish.dataset.stackloss ¶

Static Method: `out =` stackloss `()` ¶

  Brownlee’s Stack Loss Plant Data

  #### Description ¶

  Operational data of a plant for the oxidation of ammonia to nitric acid.

  #### Format ¶

  `AirFlow`

    Flow of cooling air.

  `WaterTemp`

    Cooling Water Inlet temperature.

  `AcidConc`

    Concentration of acid (per 1000, minus 500).

  `StackLoss`

    Stack loss

  #### Details ¶

  “Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)

  `AirFlow` represents the rate of operation of the plant. `WaterTemp` is the temperature of cooling water circulated through coils in the absorption tower. `AcidConc` is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid. `StackLoss` (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.

  #### Source ¶

  Brownlee, K. A. (1960, 2nd ed. 1965). Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.

  #### References ¶

  Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

  Dodge, Y. (1996). The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber’s 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.

  #### Examples ¶

      t = tblish.dataset.stackloss;

      # TODO: Create linear model and print summary

#### 8.2.57.61 tblish.dataset.state ¶

Static Method: `out =` state `()` ¶

  US State Facts and Figures

  #### Description ¶

  Data related to the 50 states of the United States of America.

  #### Format ¶

  `abb`

    State abbreviation.

  `name`

    State name.

  `area`

    Area (sq mi).

  `lat`

    Approximate center (latitude).

  `lon`

    Approximate center (longitude).

  `division`

    State division.

  `revion`

    State region.

  `Population`

    Population estimate as of July 1, 1975.

  `Income`

    Per capita income (1974).

  `Illiteracy`

    Illiteracy as of 1970 (percent of population).

  `LifeExp`

    Lfe expectancy in years (1969-71).

  `Murder`

    Murder and non-negligent manslaughter rate per 100,000 population (1976).

  `HSGrad`

    Percent high-school graduates (1970).

  `Frost`

    Mean number of days with minimum temperature below freezing (1931-1960) in capital or large city.

  #### Source ¶

  U.S. Department of Commerce, Bureau of the Census (1977) Statistical Abstract of the United States.

  U.S. Department of Commerce, Bureau of the Census (1977) County and City Data Book.

  #### References ¶

  Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

  #### Examples ¶

      t = tblish.dataset.state;

#### 8.2.57.62 tblish.dataset.sunspot_month ¶

Static Method: `out =` sunspot_month `()` ¶

  Monthly Sunspot Data, from 1749 to “Present”

  #### Description ¶

  Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that may occasionally be updated when new counts become available.

  #### Format ¶

  `month`

    Month of the observation.

  `sunspots`

    Number of sunspots.

  #### Source ¶

  WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS. Currently at http://www.sidc.be/silso/datafiles.

  #### Examples ¶

      t = tblish.dataset.sunspot_month;

#### 8.2.57.63 tblish.dataset.sunspot_year ¶

Static Method: `out =` sunspot_year `()` ¶

  Yearly Sunspot Data, 1700-1988

  #### Description ¶

  Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

  #### Format ¶

  `year`

    Year of the observation.

  `sunspots`

    Number of sunspots.

  #### Source ¶

  H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

  #### Examples ¶

      t = tblish.dataset.sunspot_year;

      figure
      plot (t.year, t.sunspots)
      xlabel ("Year")
      ylabel ("Sunspots")

#### 8.2.57.64 tblish.dataset.sunspots ¶

Static Method: `out =` sunspots `()` ¶

  Monthly Sunspot Numbers, 1749-1983

  #### Description ¶

  Monthly mean relative sunspot numbers from 1749 to 1983. Collected at Swiss Federal Observatory, Zurich until 1960, then Tokyo Astronomical Observatory.

  #### Format ¶

  `month`

    Month of the observation.

  `sunspots`

    Number of observed sunspots.

  #### Source ¶

  Andrews, D. F. and Herzberg, A. M. (1985) Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.

  #### Examples ¶

      t = tblish.dataset.sunspots;

      figure
      plot (datenum (t.month), t.sunspots)
      datetick ("x")
      xlabel ("Date")
      ylabel ("Monthly sunspot numbers")
      title ("sunspots data")

#### 8.2.57.65 tblish.dataset.swiss ¶

Static Method: `out =` swiss `()` ¶

  Swiss Fertility and Socioeconomic Indicators (1888) Data

  #### Description ¶

  Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.

  #### Format ¶

  `Fertility`

    Ig, ‘common standardized fertility measure’.

  `Agriculture`

    % of males involved in agriculture as occupation.

  `Examination`

    % draftees receiving highest mark on army examination.

  `Education`

    % education beyond primary school for draftees.

  `Catholic`

    % ‘Catholic’ (as opposed to ‘Protestant’).

  `InfantMortality`

    Live births who live less than 1 year.

  All variables but ‘Fertility’ give proportions of the population.

  #### Source ¶

  (paraphrasing Mosteller and Tukey):

  Switzerland, in 1888, was entering a period known as the demographic transition; i.e., its fertility was beginning to fall from the high level typical of underdeveloped countries.

  The data collected are for 47 French-speaking “provinces” at about 1888.

  Here, all variables are scaled to [0, 100], where in the original, all but `Catholic` were scaled to [0, 1].

  #### Note ¶

  Files for all 182 districts in 1888 and other years have been available at https://opr.princeton.edu/archive/pefp/switz.aspx.

  They state that variables `Examination` and `Education` are averages for 1887, 1888 and 1889.

  #### References ¶

  Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Monterey: Wadsworth & Brooks/Cole.

  #### Examples ¶

      t = tblish.dataset.swiss;

      # TODO: Port linear model to Octave

#### 8.2.57.66 tblish.dataset.Theoph ¶

Static Method: `out =` Theoph `()` ¶

  Pharmacokinetics of Theophylline

  #### Description ¶

  An experiment on the pharmacokinetics of theophylline.

  #### Format ¶

  `Subject`

    Categorical identifying the subject on whom the observation was made. The ordering is by increasing maximum concentration of theophylline observed.

  `Wt`

    Weight of the subject (kg).

  `Dose`

    Dose of theophylline administerred orally to the subject (mg/kg).

  `Time`

    Time since drug administration when the sample was drawn (hr).

  `conc`

    Theophylline concentration in the sample (mg/L).

  #### Details ¶

  Boeckmann, Sheiner and Beal (1994) report data from a study by Dr. Robert Upton of the kinetics of the anti-asthmatic drug theophylline. Twelve subjects were given oral doses of theophylline then serum concentrations were measured at 11 time points over the next 25 hours.

  These data are analyzed in Davidian and Giltinan (1995) and Pinheiro and Bates (2000) using a two-compartment open pharmacokinetic model, for which a self-starting model function, SSfol, is available.

  #### Source ¶

  The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

  #### References ¶

  Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994). NONMEM Users Guide: Part V. NONMEM Project Group, University of California, San Francisco.

  Davidian, M. and Giltinan, D. M. (1995). Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall. (section 5.5, p. 145 and section 6.6, p. 176)

  Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer. (Appendix A.29)

  #### Examples ¶

      t = tblish.dataset.Theoph;

      # TODO: Coplot
      # TODO: Yet another linear model to port to Octave

#### 8.2.57.67 tblish.dataset.Titanic ¶

Static Method: `out =` Titanic `()` ¶

  Survival of passengers on the Titanic

  #### Description ¶

  This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner ‘Titanic’, summarized according to economic status (class), sex, age and survival.

  #### Format ¶

  `n` is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables. The dimensions of the array correspond to the following variables:

  `Class`

    1st, 2nd, 3rd, Cre.

  `Sex`

    Male, Female.

  `Age`

    Child, Adult.

  `Survived`

    No, Yes.

  #### Details ¶

  The sinking of the Titanic is a famous event, and new books are still being published about it. Many well-known facts—from the proportions of first-class passengers to the ‘women and children first’ policy, and the fact that that policy was not entirely successful in saving the women and children in the third class—are reflected in the survival rates for various classes of passenger.

  These data were originally collected by the British Board of Trade in their investigation of the sinking. Note that there is not complete agreement among primary sources as to the exact numbers on board, rescued, or lost.

  Due in particular to the very successful film ‘Titanic’, the last years saw a rise in public interest in the Titanic. Very detailed data about the passengers is now available on the Internet, at sites such as Encyclopedia Titanica (https://www.encyclopedia-titanica.org/).

  #### Source ¶

  Dawson, Robert J. MacG. (1995). The ‘Unusual Episode’ Data Revisited. Journal of Statistics Education, 3.

  The source provides a data set recording class, sex, age, and survival status for each person on board of the Titanic, and is based on data originally collected by the British Board of Trade and reprinted in:

  British Board of Trade (1990). Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

  #### Examples ¶

      tblish.dataset.Titanic;

      # TODO: Port mosaic plot to Octave

      # TODO: Check for higher survival rates in children and females

#### 8.2.57.68 tblish.dataset.ToothGrowth ¶

Static Method: `out =` ToothGrowth `()` ¶

  The Effect of Vitamin C on Tooth Growth in Guinea Pigs

  #### Description ¶

  The response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice or ascorbic acid (a form of vitamin C and coded as `VC`).

  #### Format ¶

  `supp`

    Supplement type.

  `dose`

    Dose (mg/day).

  `len`

    Tooth length.

  #### Source ¶

  C. I. Bliss (1952). The Statistics of Bioassay. Academic Press.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. The Journal of Nutrition, 33(5), 491–504.

  #### Examples ¶

      t = tblish.dataset.ToothGrowth;

      tblish.examples.coplot (t, "dose", "len", "supp");

      # TODO: Port Lowess smoothing to Octave

#### 8.2.57.69 tblish.dataset.treering ¶

Static Method: `out =` treering `()` ¶

  Yearly Treering Data, -6000-1979

  #### Description ¶

  Contains normalized tree-ring widths in dimensionless units.

  #### Format ¶

  A univariate time series with 7981 observations.

  Each tree ring corresponds to one year.

  #### Details ¶

  The data were recorded by Donald A. Graybill, 1980, from Gt Basin Bristlecone Pine 2805M, 3726-11810 in Methuselah Walk, California.

  #### Source ¶

  Time Series Data Library: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/, series ‘CA535.DAT’.

  #### References ¶

  For some photos of Methuselah Walk see https://web.archive.org/web/20110523225828/http://www.ltrr.arizona.edu/~hallman/sitephotos/meth.html.

  #### Examples ¶

      t = tblish.dataset.treering;

#### 8.2.57.70 tblish.dataset.trees ¶

Static Method: `out =` trees `()` ¶

  Diameter, Height and Volume for Black Cherry Trees

  #### Description ¶

  This data set provides measurements of the diameter, height and volume of timber in 31 felled black cherry trees. Note that the diameter (in inches) is erroneously labelled Girth in the data. It is measured at 4 ft 6 in above the ground.

  #### Format ¶

  `Girth`

    Tree diameter (rather than girth, actually) in inches.

  `Height`

    Height in ft.

  `Volume`

    Volume of timber in cubic feet.

  #### Source ¶

  Ryan, T. A., Joiner, B. L. and Ryan, B. F. (1976). The Minitab Student Handbook. Duxbury Press.

  #### References ¶

  Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford: Oxford University Press.

  #### Examples ¶

      t = tblish.dataset.trees;

      figure
      tblish.examples.plot_pairs (t);

      figure
      loglog (t.Girth, t.Volume)
      xlabel ("Girth")
      ylabel ("Volume")

      # TODO: Transform to log space for the coplot

      # TODO: Linear model

#### 8.2.57.71 tblish.dataset.UCBAdmissions ¶

Static Method: `out =` UCBAdmissions `()` ¶

  Student Admissions at UC Berkeley

  #### Description ¶

  Aggregate data on applicants to graduate school at Berkeley for the six largest departments in 1973 classified by admission and sex.

  #### Format ¶

  A 3-dimensional array resulting from cross-tabulating 4526 observations on 3 variables. The variables and their levels are as follows:

  `Admit`

    Admitted, Rejected.

  `Gender`

    Male, Female.

  `Dept`

    A, B, C, D, E, F.

  #### Details ¶

  This data set is frequently used for illustrating Simpson’s paradox, see Bickel et al (1975). At issue is whether the data show evidence of sex bias in admission practices. There were 2691 male applicants, of whom 1198 (44.5%) were admitted, compared with 1835 female applicants of whom 557 (30.4%) were admitted. This gives a sample odds ratio of 1.83, indicating that males were almost twice as likely to be admitted. In fact, graphical methods (as in the example below) or log-linear modelling show that the apparent association between admission and sex stems from differences in the tendency of males and females to apply to the individual departments (females used to apply more to departments with higher rejection rates).

  #### Source ¶

  The data are given in Box & Jenkins (1976). Obtained from the Time Series Data Library at http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.

  #### References ¶

  Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–403. http://www.jstor.org/stable/1739581.

  #### Examples ¶

      tblish.dataset.UCBAdmissions;

      # TODO: Port mosaic plot to Octave

#### 8.2.57.72 tblish.dataset.UKDriverDeaths ¶

Static Method: `out =` UKDriverDeaths `()` ¶

  Road Casualties in Great Britain 1969-84

  #### Description ¶

  `UKDriverDeaths` is a time series giving the monthly totals of car drivers in Great Britain killed or seriously injured Jan 1969 to Dec 1984. Compulsory wearing of seat belts was introduced on 31 Jan 1983.

  `Seatbelts` is more information on the same problem.

  #### Format ¶

  `UKDriverDeaths` is a table with the following variables:

  `month`

    Month of the observation.

  `deaths`

    Number of deaths.

  `Seatbelts` is a table with the following variables:

  `month`

    Month of the observation.

  `DriversKilled`

    Car drivers killed.

  `drivers`

    Same as `UKDriverDeaths` `deaths` count.

  `front`

    Front-seat passengers killed or seriously injured.

  `rear`

    Rear-seat passengers killed or seriously injured.

  `kms`

    Distance driven.

  `PetrolPrice`

    Petrol price.

  `VanKilled`

    Number of van (“light goods vehicle”) drivers killed.

  `law`

    0/1: was the seatbelt law in effect that month?

  #### Source ¶

  Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press. pp. 519–523.

  Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

  #### References ¶

  Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

  #### Examples ¶

      tblish.dataset.UKDriverDeaths;
      d = UKDriverDeaths;
      s = Seatbelts;

      # TODO: Port the model and plots to Octave

#### 8.2.57.73 tblish.dataset.UKgas ¶

Static Method: `out =` UKgas `()` ¶

  UK Quarterly Gas Consumption

  #### Description ¶

  Quarterly UK gas consumption from 1960Q1 to 1986Q4, in millions of therms.

  #### Format ¶

  `date`

    Quarter of the observation

  `gas`

    Gas consumption (MM therms).

  #### Source ¶

  Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/.

  #### Examples ¶

      t = tblish.dataset.UKgas;

      plot (datenum (t.date), t.gas);
      datetick ("x")
      xlabel ("Month")
      ylabel ("Gas consumption (MM therms)")

#### 8.2.57.74 tblish.dataset.UKLungDeaths ¶

Static Method: `out =` UKLungDeaths `()` ¶

  Monthly Deaths from Lung Diseases in the UK

  #### Description ¶

  Three time series giving the monthly deaths from bronchitis, emphysema and asthma in the UK, 1974–1979.

  #### Format ¶

  `date`

    Month of the observation.

  `ldeaths`

    Total lung deaths.

  `fdeaths`

    Lung deaths among females.

  `mdeaths`

    Lung deaths among males.

  #### Source ¶

  P. J. Diggle (1990). Time Series: A Biostatistical Introduction. Oxford. table A.3

  #### Examples ¶

      t = tblish.dataset.UKLungDeaths;

      figure
      plot (datenum (t.date), t.ldeaths);
      title ("Total UK Lung Deaths")
      xlabel ("Month")
      ylabel ("Deaths")

      figure
      plot (datenum (t.date), [t.fdeaths t.mdeaths]);
      title ("UK Lung Deaths buy sex")
      legend ({"Female", "Male"})
      xlabel ("Month")
      ylabel ("Deaths")

#### 8.2.57.75 tblish.dataset.USAccDeaths ¶

Static Method: `out =` USAccDeaths `()` ¶

  Accidental Deaths in the US 1973-1978

  #### Description ¶

  A time series giving the monthly totals of accidental deaths in the USA.

  #### Format ¶

  `month`

    Month of the observation.

  `deaths`

    Accidental deaths.

  #### Source ¶

  Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. New York: Springer.

  #### Examples ¶

      t = tblish.dataset.USAccDeaths;

#### 8.2.57.76 tblish.dataset.USArrests ¶

Static Method: `out =` USArrests `()` ¶

  Violent Crime Rates by US State

  #### Description ¶

  This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

  #### Format ¶

  `State`

    State name.

  `Murder`

    Murder arrests (per 100,000).

  `Assault`

    Assault arrests (per 100,000).

  `UrbanPop`

    Percent urban population.

  `Rape`

    Rape arrests (per 100,000).

  #### Note ¶

  `USArrests` contains the data as in McNeil’s monograph. For the `UrbanPop` percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule), as found by Daniel S Coven (Arizona).

  See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

  #### Source ¶

  World Almanac and Book of Facts 1975. (Crime rates).

  Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.USArrests;

      summary (t);

      tblish.examples.plot_pairs (t(:,2:end));

      # TODO: Difference between USArrests and its correction

      # TODO: +/- 0.5 to restore the original <n>.5 percentages

#### 8.2.57.77 tblish.dataset.USJudgeRatings ¶

Static Method: `out =` USJudgeRatings `()` ¶

  Lawyers’ Ratings of State Judges in the US Superior Court

  #### Description ¶

  Lawyers’ ratings of state judges in the US Superior Court.

  #### Format ¶

  `CONT`

    Number of contacts of lawyer with judge.

  `INTG`

    Judicial integrity.

  `DMNR`

    Demeanor.

  `DILG`

    Diligence.

  `CFMG`

    Case flow managing.

  `DECI`

    Prompt decisions.

  `PREP`

    Preparation for trial.

  `FAMI`

    Familiarity with law.

  `ORAL`

    Sound oral rulings.

  `WRIT`

    Sound written rulings.

  `PHYS`

    Physical ability.

  `RTEN`

    Worthy of retention.

  #### Source ¶

  New Haven Register, 14 January, 1977 (from John Hartigan).

  #### Examples ¶

      t = tblish.dataset.USJudgeRatings;

      figure
      tblish.examples.plot_pairs (t(:,2:end));
      title ("USJudgeRatings data")

#### 8.2.57.78 tblish.dataset.USPersonalExpenditure ¶

Static Method: `out =` USPersonalExpenditure `()` ¶

  Personal Expenditure Data

  #### Description ¶

  This data set consists of United States personal expenditures (in billions of dollars) in the categories: food and tobacco, household operation, medical and health, personal care, and private education for the years 1940, 1945, 1950, 1955 and 1960.

  #### Format ¶

  A 2-dimensional matrix `x` with Category along dimension 1 and Year along dimension 2.

  #### Source ¶

  The World Almanac and Book of Facts, 1962, page 756.

  #### References ¶

  Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      tblish.dataset.USPersonalExpenditure;

      # TODO: Port medpolish() from R, whatever that is.

#### 8.2.57.79 tblish.dataset.uspop ¶

Static Method: `out =` uspop `()` ¶

  Populations Recorded by the US Census

  #### Description ¶

  This data set gives the population of the United States (in millions) as recorded by the decennial census for the period 1790–1970.

  #### Format ¶

  `year`

    Year of the census.

  `population`

    Population, in millions.

  #### Source ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.uspop;

      figure
      semilogy (t.year, t.population)
      xlabel ("Year")
      ylabel ("U.S. Population (millions)")

#### 8.2.57.80 tblish.dataset.VADeaths ¶

Static Method: `out =` VADeaths `()` ¶

  Death Rates in Virginia (1940)

  #### Description ¶

  Death rates per 1000 in Virginia in 1940.

  #### Format ¶

  A 2-dimensional matrix `deaths`, with age group along dimension 1 and demographic group along dimension 2.

  #### Details ¶

  The death rates are measured per 1000 population per year. They are cross-classified by age group (rows) and population group (columns). The age groups are: 50–54, 55–59, 60–64, 65–69, 70–74 and the population groups are Rural/Male, Rural/Female, Urban/Male and Urban/Female.

  This provides a rather nice 3-way analysis of variance example.

  #### Source ¶

  Molyneaux, L., Gilliam, S. K., and Florant, L. C.(1947) Differences in Virginia death rates by color, sex, age, and rural or urban residence. American Sociological Review, 12, 525–535.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      tblish.dataset.VADeaths;

      # TODO: Port to Octave

#### 8.2.57.81 tblish.dataset.volcano ¶

Static Method: `out =` volcano `()` ¶

  Topographic Information on Auckland’s Maunga Whau Volcano

  #### Description ¶

  Maunga Whau (Mt Eden) is one of about 50 volcanos in the Auckland volcanic field. This data set gives topographic information for Maunga Whau on a 10m by 10m grid.

  #### Format ¶

  A matrix `volcano` with 87 rows and 61 columns, rows corresponding to grid lines running east to west and columns to grid lines running south to north.

  #### Source ¶

  Digitized from a topographic map by Ross Ihaka. These data should not be regarded as accurate.

  #### References ¶

  Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. p. 537.

  Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods. Second edition. New York: Springer-Verlag. p. 414.

  #### Examples ¶

      tblish.dataset.volcano;

      # TODO: Figure out how to do a topo map in Octave. Just a gridded color plot
      # should be fine. And then maybe do a 3-d mesh plot.

#### 8.2.57.82 tblish.dataset.warpbreaks ¶

Static Method: `out =` warpbreaks `()` ¶

  The Number of Breaks in Yarn during Weaving

  #### Description ¶

  This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn.

  #### Format ¶

  `wool`

    Type of wool (A or B).

  `tension`

    The level of tension (L, M, H).

  `breaks`

    Number of breaks.

  There are measurements on 9 looms for each of the six types of warp (AL, AM, AH, BL, BM, BH).

  #### Source ¶

  Tippett, L. H. C. (1950). Technological Applications of Statistics. New York: Wiley. Page 106.

  #### References ¶

  Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Mass: Addison-Wesley.

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.warpbreaks;

      summary (t)

      # TODO: Port the plotting code and OPAR to Octave

#### 8.2.57.83 tblish.dataset.women ¶

Static Method: `out =` women `()` ¶

  Average Heights and Weights for American Women

  #### Description ¶

  This data set gives the average heights and weights for American women aged 30–39.

  #### Format ¶

  `height`

    Height (in).

  `weight`

    Weight (lbs).

  #### Details ¶

  The data set appears to have been taken from the American Society of Actuaries Build and Blood Pressure Study for some (unknown to us) earlier year.

  The World Almanac notes: “The figures represent weights in ordinary indoor clothing and shoes, and heights with shoes”.

  #### Source ¶

  The World Almanac and Book of Facts, 1975.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      t = tblish.dataset.women;

      figure
      scatter (t.height, t.weight)
      xlabel ("Height (in)")
      ylabel ("Weight (lb")
      title ("women data: American women aged 30-39")

#### 8.2.57.84 tblish.dataset.WorldPhones ¶

Static Method: `out =` WorldPhones `()` ¶

  The World’s Telephones

  #### Description ¶

  The number of telephones in various regions of the world (in thousands).

  #### Format ¶

  A matrix with 7 rows and 8 columns. The columns of the matrix give the figures for a given region, and the rows the figures for a year.

  The regions are: North America, Europe, Asia, South America, Oceania, Africa, Central America.

  The years are: 1951, 1956, 1957, 1958, 1959, 1960, 1961.

  #### Source ¶

  AT&T (1961) The World’s Telephones.

  #### References ¶

  McNeil, D. R. (1977). Interactive Data Analysis. New York: Wiley.

  #### Examples ¶

      tblish.dataset.WorldPhones;

      # TODO: Port matplot() to Octave

#### 8.2.57.85 tblish.dataset.WWWusage ¶

Static Method: `out =` WWWusage `()` ¶

  WWWusage

  #### Description ¶

  A time series of the numbers of users connected to the Internet through a server every minute.

  #### Format ¶

  A time series of length 100.

  #### Source ¶

  Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press. http://www.ssfpack.com/dkbook/

  #### References ¶

  Makridakis, S., Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting: Methods and Applications. New York: Wiley.

  #### Examples ¶

      # TODO: Come up with example code here

#### 8.2.57.86 tblish.dataset.zCO2 ¶

Static Method: `out =` zCO2 `()` ¶

  Carbon Dioxide Uptake in Grass Plants

  #### Description ¶

  The `CO2` data set has 84 rows and 5 columns of data from an experiment on the cold tolerance of the grass species Echinochloa crus-galli.

  #### Format ¶

  #### Details ¶

  The CO2 uptake of six plants from Quebec and six plants from Mississippi was measured at several levels of ambient CO2 concentration. Half the plants of each type were chilled overnight before the experiment was conducted.

  #### Source ¶

  Potvin, C., Lechowicz, M. J. and Tardif, S. (1990). The statistical analysis of ecophysiological response curves obtained from experiments involving repeated measures. Ecology, 71, 1389–1400.

  Pinheiro, J. C. and Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. New York: Springer.

  #### Examples ¶

      t = tblish.dataset.zCO2;

      # TODO: Coplot
      # TODO: Port the linear model to Octave

#### 8.2.58 tblish.datasets ¶

Class: tblish.datasets ¶

  Example dataset collection.

  `tblish.datasets` is a collection of example datasets to go with the Tablicious package.

  The `tblish.datasets` class provides methods for listing and loading the example datasets.

#### 8.2.58.1 datasets.description ¶

Static Method: `description` (datasetName) ¶
Static Method: `out =` description `(datasetName)` ¶

  Get or display the description for a dataset.

  Gets the description for the named dataset. If the output is captured, it is returned as a charvec containing plain text suitable for human display. If the output is not captured, displays the description to the console.

#### 8.2.58.2 datasets.list ¶

Static Method: list `()` ¶
Static Method: `out =` list `()` ¶

  List all datasets.

  Lists all the example datasets known to this class. If the output is captured, returns the list as a table. If the output is not captured, displays the list.

  Returns a table with variables Name, Description, and possibly more.

#### 8.2.58.3 datasets.load ¶

Static Method: `load` (datasetName) ¶
Static Method: `out =` load `(datasetName)` ¶

  Load a specified dataset.

  datasetName is the name of the dataset to load, as found in the `Name` column of the dataset list.

#### 8.2.59 tblish.evalWithTableVars ¶

Function: `out =` tblish.evalWithTableVars `(tbl, expr)` ¶

  Evaluate an expression against a table array’s variables.

  Evaluates the M-code expression expr in a workspace where all of tbl’s variables have been assigned to workspace variables.

  expr is a charvec containing an Octave expression.

  As an implementation detail, the workspace will also contain some variables that are prefixed and suffixed with "__". So try to avoid those in your table variable names.

  Returns the result of the evaluation.

  Examples:

      [s,p,sp] = tblish.examples.SpDb
      tmp = join (sp, p);
      shipment_weight = tblish.evalWithTableVars (tmp, "Qty .* Weight")

  See also: table.restrict

#### 8.2.60 tblish.examples.coplot ¶

Function: `[fig, hax] =` tblish.examples.coplot `(tbl, xvar, yvar, gvar)` ¶
Function: `[fig, hax] =` tblish.examples.coplot `(fig, tbl, xvar, yvar, gvar)` ¶
Function: `[fig, hax] =` tblish.examples.coplot `(…, OptionName, OptionValue, …)` ¶

  Conditioning plot.

  `tblish.examples.coplot` produces conditioning plots. This is a kind of plot that breaks up the data into groups based on one or two grouping variables, and plots each group of data in a separate subplot.

  tbl is a `table` containing the data to plot.

  xvar is the name of the table variable within tbl to use as the X values. May be a variable name or index.

  yvar is the name of the table variable within tbl to use as the Y values. May be a variable name or index.

  gvar is the name of the table variable or variables within tbl to use as the grouping variable(s). The grouping variables split the data into groups based on the distinct values in those variables. gvar may specify either one or two grouping variables (but not more). It can be provided as a charvec, cellstr, or index array. Records with a missing value for their grouping variable(s) are ignored.

  fig is the figure handle to plot into. If fig is not provided, a new figure is created.

  Name/Value options:

  `PlotFcn`

    The plotting function to use, supplied as a function handle. Defaults to `@plot`. It must be a function that provides the signature `fcn(hax, X, Y, …)`.

  `PlotArgs`

    A cell array of arguments to pass in to the plotting function, following the hax, x, and y arguments.

  Returns: fig – the figure handle it plotted into hax – array of axes handles to all the axes for the subplots

#### 8.2.61 tblish.examples.plot_pairs ¶

Function: `out =` tblish.examples.plot_pairs `(data)` ¶
Function: `out =` tblish.examples.plot_pairs `(data, plot_type)` ¶
Function: `out =` tblish.examples.plot_pairs `(fig, …)` ¶

  Plot pairs of variables against each other.

  data is the data holding the variables to plot. It may be either a `table` or a struct. Each variable or field in the `table` or struct is considered to be one variable. Each must hold a vector, and all the vectors of all the variables must be the same size.

  plot_type is a charvec indicating what plot type to do in each subplot. (`"scatter"` is the default.) Valid plot_type values are:

  `"scatter"`

    A plain scatter plot.

  `"smooth"`

    A scatter plot + fitted line, like R’s `panel.smooth` does.

  fig is an optional figure handle to plot into. If omitted, a new figure is created.

  Returns the created figure, if the output is captured.

#### 8.2.62 tblish.examples.SpDb ¶

Function: `spdb =` tblish.examples.SpDb `()` ¶
Function: `[s, p, sp] =` tblish.examples.SpDb `()` ¶

  The classic Suppliers-Parts example database.

  Constructs the classic C. J. Date Suppliers-Parts ("SP") example database as tables. This database is the one used as an example throughout Date’s "An Introduction to Database Systems" textbook.

  Returns the database as a set of three table arrays. If one argout is captured, the tables are returned in the fields of a single struct. If multiple argouts are captured, the tables are returned as three argouts with a single table in each, in the order (s, p, sp).

#### 8.2.63 tblish.sizeof2 ¶

Function: `out =` tblish.sizeof2 `(x)` ¶

  Approximate size of an array in bytes, with object support.

  This is an alternative to Octave’s `sizeof` function that tries to provide meaningful support for objects, including the classes defined in Tablicious. It is named "sizeof2" instead of "sizeof" to avoid a "shadowing core function" warning when loading Tablicious, because it seems that Octave does not consider packages (namespaces) when detecting shadowed functions.

  This may be supplemented or replaced by `sizeof` override methods on Tablicious’s classes. I’m not sure whether Octave’s `sizeof` supports extension by method overrides, so I’m not doing that yet. If that happens, this `sizeof2` function will stick around in a deprecated state for a while, and it will respect those override methods.

  For tables, this returns the sum of `sizeof` for all of its variables’ arrays, plus the size of the VariableNames and any other metadata stored in obj.

  This is currently broken for some types, because its implementation is in transition from overridden methods on Tablicious’s objects to a separate function.

  This is not supported, fully or at all, for all input types, but it has support for the types defined in Tablicious, plus some Octave built-in types, and makes a best effort at figuring out user-defined classdef objects. It currently does not have extensibility support for customization by classdef classes, but that may be added in the future, in which case its output may change significantly for classdef objects in future releases.

  x is an array of any type.

  Returns a scalar numeric. Returns NaN for types that are known to not be supported, instead of raising an error. Raises an error if it fails to determine the size of an input of a type that it thought was supported.

  See also: sizeof

#### 8.2.64 tblish.table.grpstats ¶

Function: `[out] =` tblish.table.grpstats `(tbl, groupvar)` ¶
Function: `[out] =` tblish.table.grpstats `(…, `'DataVars'`, DataVars)` ¶

  Statistics by group for a table array.

  This is a table-specific implementation of `grpstats` that works on table arrays. It is supplied as a function in the +tblish package to avoid colliding with the global `grpstats` function supplied by the Statistics Octave Forge package. Depending on which version of the Statistics OF package you are using, it may or may not support table inputs to its `grpstats` function. This function is supplied as an alternative you can use in an environment where `table` arrays are not supported by the `grpstats` that you have, though you need to make code changes and call it as `tblish.table.grpstats(tbl)` instead of with a plain `grpstats(tbl)`.

  See also: table.groupby, table.findgroups, table.splitapply

#### 8.2.65 timezones ¶

Function: `out =` timezones `()` ¶
Function: `out =` timezones `(area)` ¶

  List all the time zones defined on this system.

  This lists all the time zones that are defined in the IANA time zone database used by this Octave. (On Linux and macOS, that will generally be the system time zone database from `/usr/share/zoneinfo`. On Windows, it will be the database redistributed with the Tablicious package.

  If the return is captured, the output is returned as a table if your Octave has table support, or a struct if it does not. It will have fields/variables containing column vectors:

  `Name`

    The IANA zone name, as cellstr.

  `Area`

    The geographical area the zone is in, as cellstr.

  Compatibility note: Matlab also includes UTCOffset and DSTOffset fields in the output; these are currently unimplemented.

#### 8.2.66 todatetime ¶

Function: `out =` todatetime `(x)` ¶

  Convert input to a Tablicious datetime array, with convenient interface.

  This is an alternative to the regular datetime constructor, with a signature and conversion logic that Tablicious’s author likes better.

  This mainly exists because datetime’s constructor signature does not accept datenums, and instead treats one-arg numeric inputs as datevecs. (For compatibility with Matlab’s interface.) I think that’s less convenient: datenums seem to be more common than datevecs in M-code, and it returns an object array that’s not the same size as the input.

  Returns a datetime array whose size depends on the size and type of the input array, but will generally be the same size as the array of strings or numerics the input array "represents".

#### 8.2.67 vartype ¶

Function: `out =` vartype `(type)` ¶

  Filter by variable type for use in suscripting.

  Creates an object that can be used for subscripting into the variables dimension of a table and filtering on variable type.

  type is the name of a type as charvec. This may be anything that the `isa` function accepts, or `'cellstr'` to select cellstrs, as determined by `iscellstr`.

  Returns an object of an opaque type. Don’t worry about what type it is; just pass it into the second argument of a subscript into a `table` object.

#### 8.2.68 vecfun ¶

Function: `out =` vecfun `(fcn, x, dim)` ¶

  Apply function to vectors in array along arbitrary dimension.

  This function is not implemented yet.

  Applies a given function to the vector slices of an N-dimensional array, where those slices are along a given dimension.

  fcn is a function handle to apply.

  x is an array of arbitrary type which is to be sliced and passed in to fcn.

  dim is the dimension along which the vector slices lay.

  Returns the collected output of the fcn calls, which will be the same size as x, but not necessarily the same type.

#### 8.2.69 years ¶

Function File: `out =` years `(x)` ¶

  Create a `duration` x years long, or get the years in a `duration` x.

  If input is numeric, returns a `duration` array in units of fixed-length years of 365.2425 days each.

  If input is a `duration`, converts the `duration` to a number of fixed-length years as double.

  Note: `years` creates fixed-length years, which may not be what you want. To create a duration of calendar years (which account for actual leap days), use `calyears`.

  See calyears.

## 9 Copying ¶

### 9.1 Package Copyright ¶

Tablicious for GNU Octave is covered by the GNU GPLv3 and other Free and Open Source Software licenses.

The main code of Tablicious is licensed under the GNU GPL version 3.

The date/time portion of Tablicious includes some Unicode data files licensed under the Unicode License Agreement - Data Files and Software license.

The Tablicious test suite contains some files, specifically some table-related tests using MP-Test like `t/t_01_table.m`, which are BSD 3-Clause licensed, and are adapted from MATPOWER written by Ray Zimmerman.

The Fisher Iris dataset is Public Domain.

### 9.2 Manual Copyright ¶

This manual is for Tablicious, version 0.4.3.

Copyright © 2019, 2023, 2024 Andrew Janke

> Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies.

> Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one.

> Permission is granted to copy and distribute translations of this manual into another language, under the same conditions as for modified versions.