Title: Discrete Multivariate Probability Distributions
Version: 0.0.10
Description: Provides an object class for dealing with many multivariate probability distributions at once, useful for simulation.
Depends: R (≥ 3.5.0), rje
License: GPL-2
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
RoxygenNote: 7.1.2
URL: https://github.com/rje42/contingency
BugReports: https://github.com/rje42/contingency/issues
NeedsCompilation: no
Packaged: 2022-11-18 15:35:09 UTC; evans
Author: Robin Evans [aut, cre]
Maintainer: Robin Evans <evans@stats.ox.ac.uk>
Repository: CRAN
Date/Publication: 2022-11-18 16:20:02 UTC

Subset object of class tables

Description

Take subset of tables class.

Usage

## S3 method for class 'tables'
x[i, j, ..., drop = TRUE, keep = FALSE]

Arguments

x

object of class tables

i

indicies of which tables to retain

j

which rows of each table to retain (or if ... not specified, entries )

...

additional indices up to the dimension of the table

drop

usual logical indicating whether to consolidate margins of the table (doesn't apply to i)

keep

if only one table is specified with i, should the object output be an object of class tables? If not becomes a suitable array.

Details

There are two main ways to subset these tables. In both cases the first index refers to the tables being selected; one of the methods is to additionally specify all the indices corresponding to the tables, the other is to only specify a single entry. For example, x[,1,2,2] specifies the (1,2,2)th entry of each table; x[,7] will have the same effect for 2x2x2 tables.

If only one index is specified, then the function behaves just as ordinary subsetting on an array.

Value

A tables object over the specific entries and values selected.

Examples

x <- rprobMat(n=10, rep(2,3))
x[1,]
x[,1,1:2,1]
x[,1,1:2,1,drop=FALSE]

Permute dimensions of tables

Description

Method for permuting indices of tables object.

Usage

## S3 method for class 'tables'
aperm(a, perm, ...)

Arguments

a

object of class tables

perm

permutation of 1,...,k, where each table has k dimensions

...

other arguments to methods

Value

A permuted tables object.

Convert tables into array

Description

Convert tables into array

Usage

## S3 method for class 'tables'
as.array(x, ...)

Arguments

x

tables object

...

other arguments

Value

An array object

Convert tables into matrix

Description

Convert tables into matrix

Usage

## S3 method for class 'tables'
as.matrix(x, ...)

Arguments

x

tables object

...

other arguments

Value

A matrix object

As tables

Description

As tables

Usage

as_tables(x, tdim, ...)

Arguments

x

array or matrix object

tdim

dimensions for each table

...

other arguments for methods

Value

A tables object.

Apply function over tables

Description

Apply a function to each contingency table in a tables object.

Usage

capply(x, f, ...)

Arguments

x

object of class tables

f

function to apply to each table

...

additional arguments to f

Value

a vector, matrix or list of outputs from the function f.

Check conditional independence

Description

Gives a numerical check that a (conditional) independence holds in a probability distribution.

Usage

checkCI(x, A, B, C = integer(0), eps = .Machine$double.eps, ...)

## S3 method for class 'array'
checkCI(x, A, B, C = integer(0), eps = .Machine$double.eps, ...)

## S3 method for class 'tables'
checkCI(x, A, B, C = integer(0), eps = .Machine$double.eps, ...)

Arguments

x

an array or object of class tables

A, B

the sets of variables whose independence is to be tested

C

conditioning set (possibly empty)

eps

tolerance parameter

...

other arguments to methods

Details

just tests to an appropriate numerical precision that a conditional independence holds: this is *not* a statistical test for conditional independence. If A and B overlap with C then these vertices are ignored. If A and B intersect with one another (but not C) then the solution is always false.

Value

A logical, or a vector of logicals of the same length as the number of tables provided, indicating whether the conditional independence seems to hold numerically.

Methods (by class)

Calculate entropy of discrete distribution

Description

Calculate entropy of discrete distribution

Usage

entropy(p, ...)

## Default S3 method:
entropy(p, ...)

## S3 method for class 'array'
entropy(p, margin, ...)

## S3 method for class 'tables'
entropy(p, margin, ...)

Arguments

p

non-negative numeric vector

...

other arguments to methods

margin

margin to consider

Value

A numeric value of the entopy, or vector of entropies.

Methods (by class)

Interaction information

Description

Interaction information

Usage

interactionInf(p, ...)

## Default S3 method:
interactionInf(p, ..., condition)

Arguments

p

object to find interaction information for

...

other arguments to methods

condition

variables on which to condition

Value

Numeric value for interaction information, or a vector of interaction information values.

Methods (by class)

Kullback-Leibler Divergence

Description

Get the KL Divergence between two discrete distributions

Usage

kl(x, y, ...)

## Default S3 method:
kl(x, y, ...)

## S3 method for class 'tables'
kl(x, y, ...)

Arguments

x, y

vectors (of probabilities)

...

other arguments to methods

Value

a numberic value, vector or matrix of KL-divergences.

Methods (by class)

Get margin of a table or tables

Description

Get margin of a table or tables

Usage

margin(x, ...)

margin2(x, ...)

conditional(x, ...)

conditional2(x, ...)

intervention(x, ...)

Arguments

x

a contingency table or tables object

...

a contingency table or tables object

Details

margin2 keeps all dimensions, and hence results will sum to the number of cells summed over.

Value

an object of the same class as x. The resulting array, or collection of tables, will contain a marginal, conditional or interventional distribution.

Functions

Get the marginal distributions

Description

Get the marginal distributions

Usage

## S3 method for class 'tables'
margin(x, margin = NULL, order = TRUE, ...)

Arguments

x

an object of class tables

margin

integer vector giving margin to be calculated (1 for rows, etc.)

order

logical indicating whether resulting indices should be in the same order as stated in margin

...

other arguments to function

Details

Calculates marginal distributions for each entry in a probMat.

Value

An object of class tables consisting of the required marginal distribution.

Multiinformation

Description

Get the multiinformation for a discrete distribution

Usage

multiInf(x, ...)

## Default S3 method:
multiInf(x, margin = NULL, ...)

## S3 method for class 'tables'
multiInf(x, margin = NULL, ...)

Arguments

x

vectors (of probabilities)

...

other arguments to methods

margin

margin to find multiinformation for

Value

a numberic value, vector or matrix of required multiinformation.

Methods (by class)

(Conditional) mutual information

Description

(Conditional) mutual information

Usage

mutualInf(p, m1, m2, condition, ...)

## Default S3 method:
mutualInf(p, m1, m2, condition, ...)

## S3 method for class 'tables'
mutualInf(p, m1, m2, condition, ...)

Arguments

p

numeric array or tables class

m1, m2

margins for mutual information

condition

conditional margin

...

other arguments to methods

Value

Numeric value for mutual information, or a vector of mutual information values.

Methods (by class)

Number of tables

Description

Number of tables

Usage

ntables(x)

Arguments

x

an object of class tables

Details

Gives the number of tables in an object of class tables.

Value

An integer.

Permute indices for variable k

Description

Currently only works for binary dimensions.

Usage

perm_dim(x, k, perm, ...)

Arguments

x

array or related object

k

index to permute

perm

permutation to perform

...

other arguments (not currently used)

Details

Permutes the levels of one variable according to the permutation given in perm. Can be applied to matrices, arrays or tables.

Value

A permuted array or tables object.

Print tables

Description

Print method for object of class tables.

Usage

## S3 method for class 'tables'
print(x, ...)

Arguments

x

object of class tables

...

arguments to pass to print method for an array

Value

The input provided (invisibly).

Turn distributions into tables

Description

Turn distributions into tables

Usage

repTables(n, f, ...)

Arguments

n

number of distributions to generate

f

function that generates a probability distribution

...

arguments to f

Value

a tables object containing the outputs of f

Generate matrix of (conditional) probability distributions

Description

Generates discrete probability distributions in a matrix.

Usage

rprobMat(n, dim, d, alpha = 1)

rcondProbMat(n, dim, d, alpha = 1, condition)

Arguments

n

number of distributions

dim

dimension of contingency table for distributions

d

number of dimensions of table

alpha

parameter to use in dirichlet distribution

condition

which dimensions should be conditioned upon

Details

Returns an object of class tables consisting of discrete probability distributions. Each distribution is assumed to be a contingency table of dimension dim, and the probabilities are generated using a Dirichlet distribution with parameters all equal to alpha.

Value

A tables object containing random distributions.

Functions

Examples

dat <- rprobMat(10, c(2,2,2))

Create blank tables

Description

Create blank tables

Usage

tables(n, tdim)

Arguments

n

number of tables

tdim

dimension of each table

Dimension of distributions over contingency tables

Description

Dimension of distributions over contingency tables

Usage

tdim(x)

tdim(x) <- value

Arguments

x

an object of class tables

value

value to set parameters to

Details

The class tables is used to represent a collection of multidimentional tables; this function returns the dimension of each table.

Value

an integer vector of the dimensions

the tables object inputted with the new dimensions

Functions

Dimension names for distributions over contingency tables

Description

Dimension names for distributions over contingency tables

Usage

tdimnames(x)

tdimnames(x) <- value

Arguments

x

tables object

value

value to set dimension names to

Value

the tables object inputted with the new dimension names

Functions