Many analysis of variance tests with a discrete variable {Rfast}

Many analysis of variance tests with a discrete variable

Description

Many analysis of variance tests with a discrete variable.

Usage

poisson.anovas(y, ina, logged = FALSE) 
quasipoisson.anovas(y, ina, logged = FALSE)
geom.anovas(y, ina, type = 1, logged = FALSE) 

Arguments

y

A numerical matrix with discrete valued data, i.e. counts for the case of the Poisson, or with 0s and 1s for the case of the Bernoulli distribution. Each column represents a variable.

ina

A numerical vector with discrete numbers starting from 1, i.e. 1, 2, 3, 4,... or a factor variable. This is suppose to be a categorical predictor. If you supply a continuous valued vector the function will obviously provide wrong results.

type

This rgument is for the geometric distribution. Type 1 refers to the case where the minimum is zero and type 2 for the case of the minimum being 1.

logged

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

Details

This is the analysis of variance with count data. What we do is many log-likelihood ratio tests. For the quasi Poisson case we scale the difference in the deviances.

Value

A matrix with two values, the difference in the deviances (test statistic) and the relevant p-value. For the case of quasi Poisson the estimated \phi parameter is also returned.

Author(s)

Michail Tsagris

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

See Also

g2tests, poisson.anova, anova, poisson_only, poisson.mle

Examples

ina <- rbinom(500, 3, 0.5) + 1 
## Poisson example
y <- matrix( rpois(500 * 100, 10), ncol= 100 )
a1 <- poisson.anovas(y, ina)
y <- NULL