Many one sample goodness of fit tests for categorical data {Rfast}

Many one sample goodness of fit tests for categorical data

Description

Many one sample goodness of fit tests for categorical data.

Usage

cat.goftests(x, props, type = "gsquare", logged = FALSE)

Arguments

x

A matrix with the data, where the rows denote the samples and the columns are the variables. The data must be integers and be of the form 1, 2, 3, and so on. The minimum must be 1, and not zero.

props

The assumed distribution of the data. A vector or percentages summing to 1.

type

Either Pearson's \chi^2 test ("chisquare") is used or the G^2 test ("qsquare", default value).

logged

Should the p-values be returned (FALSE) or their logarithm (TRUE)?

Details

Given a matrix of integers, where each column refers to a sample, the values of a categorical variable the function tests wether these values can be assumed to fit a specific distribution.

Value

A matrix with the test statistic and the p-value of each test.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[e-mail]> and Manos Papadakis <[e-mail]>.

See Also

ttests, ttest, ftests

Examples

x <- matrix( rbinom(300 * 100, 4, 0.6), ncol = 100 ) + 1
props <- dbinom(0:4, 4, 0.6)
## can we assume that each column comes from a distribution  whose mass is given by props?
cat.goftests(x, props)
a1 <- cat.goftests(x, props)  ## G-square test
a2 <- cat.goftests(x, props, type = "chisq")  ## Chi-square test
cor(a1, a2)
mean( abs(a1 - a2) ) 
x <- NULL