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.sizeof

Method: out = sizeof (obj) ¶

Size of array in bytes.

8.2.2.3 calendarDuration.isnat

Method: out = isnat (obj) ¶

True if input elements are NaT.

Returns logical array the same size as obj.

8.2.2.4 calendarDuration.uminus

Method: out = uminus (obj) ¶

Unary minus. Negates the sign of obj.

8.2.2.5 calendarDuration.plus

Method: out = plus (A, B) ¶

Addition: add two calendarDurations.

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.

8.2.2.6 calendarDuration.times

Method: out = times (obj, B) ¶

Multiplication: Multiplies a calendarDuration by a numeric factor.

Returns a calendarDuration.

8.2.2.7 calendarDuration.minus

Method: out = times (A, B) ¶

Subtraction: Subtracts one calendarDuration from another.

Returns a calendarDuration.

8.2.2.8 calendarDuration.dispstrs

Method: out = dispstrs (obj) ¶

Get display strings for each element of obj.

Returns a cellstr the same size as obj.

8.2.2.9 calendarDuration.isnan

Method: out = isnan (obj) ¶

True if input elements are NaT. This is just an alias for isnat, provided for compatibility and polymorphic programming purposes.

Returns logical array the same size as obj.

8.2.5.1 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.

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.sizeof

Method: out = sizeof (obj) ¶

Size of array in bytes.

8.2.5.4 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.

8.2.5.5 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.

8.2.5.6 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.

8.2.5.7 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.

8.2.5.8 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.

8.2.5.9 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.

8.2.5.10 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.

8.2.5.11 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.

8.2.5.12 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.

8.2.5.13 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}.

8.2.5.14 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.

8.2.5.15 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.

8.2.5.16 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.

8.2.5.17 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.

8.2.5.18 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.

8.2.5.19 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.

8.2.5.20 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.

8.2.9.1 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.

8.2.9.2 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.

8.2.9.3 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.

8.2.9.4 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 NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

8.2.9.5 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.

8.2.9.6 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.

8.2.9.7 datetime.sizeof

Method: out = sizeof (obj) ¶

Size of array in bytes.

8.2.9.8 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.

8.2.9.9 datetime.ymd

Method: [y, m, d] = ymd (obj) ¶

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

For zoned datetimes, these will be local times in the associated time zone.

Returns double arrays the same size as obj.

8.2.9.10 datetime.hms

Method: [h, m, s] = hms (obj) ¶

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

For zoned datetimes, these will be local times in the associated time zone.

Returns double arrays the same size as obj.

8.2.9.11 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 datetimes, these will be local times in the associated time zone.

Returns double arrays the same size as obj.

8.2.9.12 datetime.timeofday

Method: out = timeofday (obj) ¶

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

For zoned datetimes, these will be local times in the associated time zone.

Returns a duration array the same size as obj.

8.2.9.13 datetime.week

Method: out = week (obj) ¶

Get the week of the year.

This method is unimplemented.

8.2.9.14 datetime.dispstrs

Method: out = dispstrs (obj) ¶

Get display strings for each element of obj.

Returns a cellstr the same size as obj.

8.2.9.15 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.

8.2.9.16 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.

8.2.9.17 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.

8.2.9.18 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.

8.2.9.19 datetime.datenum

Method: out = datenum (obj) ¶

Convert this to datenums that represent the same local time

Returns double array of same size as this.

8.2.9.20 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.

8.2.9.21 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);

8.2.9.22 datetime.isnat

Method: out = isnat (obj) ¶

True if input elements are NaT.

Returns logical array the same size as obj.

8.2.9.23 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.

8.2.9.24 datetime.lt

Method: out = lt (A, B) ¶

True if A is less than B. This defines the < operator for datetimes.

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.le

Method: out = le (A, B) ¶

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

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

Returns logical array the same size as obj.

8.2.9.26 datetime.ne

Method: out = ne (A, B) ¶

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

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

Returns logical array the same size as obj.

8.2.9.27 datetime.eq

Method: out = eq (A, B) ¶

True if A is equal to B. This defines the == operator for datetimes.

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

Returns logical array the same size as obj.

8.2.9.28 datetime.ge

Method: out = ge (A, B) ¶

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

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

Returns logical array the same size as obj.

8.2.9.29 datetime.gt

Method: out = gt (A, B) ¶

True if A is greater than B. This defines the > operator for datetimes.

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

Returns logical array the same size as obj.

8.2.9.30 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.

8.2.9.31 datetime.minus

Method: out = minus (A, B) ¶

Subtraction (- operator). Subtracts a duration, calendarDuration or numeric B from a datetime A, or subtracts two datetimes 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.

8.2.9.32 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.

8.2.9.33 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.

8.2.9.34 datetime.linspace

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

Linearly-spaced values in date/time space.

Constructs a vector of datetimes 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 datetimes.

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

Returns an n-long datetime vector.

8.2.9.35 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.

8.2.13.1 duration.ofDays

8.2.13.2 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.

8.2.13.3 duration.sizeof

Method: out = sizeof (obj) ¶

Size of array in bytes.

8.2.13.4 duration.years

8.2.13.5 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.

8.2.13.6 duration.hours

8.2.13.7 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.

8.2.13.8 duration.minutes

8.2.13.9 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.

8.2.13.10 duration.seconds

8.2.13.11 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.

8.2.13.12 duration.milliseconds

8.2.13.13 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.

8.2.13.14 duration.dispstrs

8.2.13.15 duration.dispstrs

Method: out = duration (obj) ¶

Get display strings for each element of obj.

Returns a cellstr the same size as obj.

8.2.13.16 duration.char

8.2.13.17 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.

8.2.13.18 duration.linspace

8.2.13.19 duration.linspace

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

Linearly-spaced values in time duration space.

Constructs a vector of durations 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 durations.

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

Returns an n-long datetime vector.

8.2.25.1 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.

8.2.25.2 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 NaTs. Doing arithmetic with a NaT and any other value results in a NaT.

This static method is provided because the global NaT function creates datetimes, not localdates

8.2.25.3 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.

8.2.25.4 localdate.dispstrs

Method: out = dispstrs (obj) ¶

Get display strings for each element of obj.

Returns a cellstr the same size as obj.

8.2.25.5 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.

8.2.25.6 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.

8.2.25.7 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.

8.2.25.8 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.

8.2.25.9 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.

8.2.25.10 localdate.isnat

Method: out = isnat (obj) ¶

True if input elements are NaT.

Returns logical array the same size as obj.

8.2.25.11 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.

8.2.28.1 missing.missing

Constructor: obj = missing () ¶

Constructs a scalar missing array.

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

8.2.28.2 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.

8.2.28.3 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.

8.2.28.4 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.

8.2.28.5 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.

8.2.44.1 octave.chrono.DummyClass.DummyClass

Constructor: obj = octave.chrono.DummyClass () ¶

Constructs a new scalar DummyClass with default values.

Constructor: obj = octave.chrono.DummyClass (x, y) ¶

Constructs a new DummyClass with the specified values.

8.2.44.2 octave.chrono.DummyClass.foo

Method: out = foo (obj) ¶

Computes a foo value.

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8.2.44.3 octave.chrono.DummyClass.bar

Method: out = bar (obj) ¶

Computes a bar value.

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

8.2.45.2 octave.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.

8.2.45.3 octave.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 = octave.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

8.2.45.4 octave.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 = octave.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);

8.2.45.5 octave.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.

8.2.45.6 octave.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 = octave.dataset.attitude

octave.examples.plot_pairs (t);

# TODO: Display table summary

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

8.2.45.7 octave.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 = octave.dataset.austres

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

8.2.45.8 octave.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.

8.2.45.9 octave.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

8.2.45.10 octave.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

8.2.45.11 octave.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 = octave.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

8.2.45.12 octave.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 = octave.dataset.ChickWeight

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

8.2.45.13 octave.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 = octave.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

8.2.45.14 octave.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 = octave.dataset.co2;

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

8.2.45.15 octave.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

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

8.2.45.17 octave.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 = octave.dataset.discoveries;

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

8.2.45.18 octave.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 = octave.dataset.DNase;

# TODO: Port this from R

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

8.2.45.19 octave.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.

8.2.45.20 octave.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

8.2.45.21 octave.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

8.2.45.22 octave.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 = octave.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");

8.2.45.23 octave.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 = octave.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

8.2.45.24 octave.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 = octave.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

8.2.45.25 octave.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 = octave.dataset.freeny;

summary(t)

octave.examples.plot_pairs (removevars (t, "date"))

# TODO: Create linear model and print summary

# TODO: Linear model plot

8.2.45.26 octave.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

octave.dataset.HairEyeColor

# TODO: Aggregate over sex and display a table of counts

# TODO: Port mosaic plot to Octave

8.2.45.27 octave.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

octave.dataset.Harman23cor;

# TODO: Port factanal to Octave