# poisson.anovas: Many analysis of variance tests with a discrete variable In Rfast: A Collection of Efficient and Extremely Fast R Functions

## Description

Many analysis of variance tests with a discrete variable.

## Usage

 ```1 2 3``` ```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 φ parameter is also returned.

## Author(s)

Michail Tsagris

``` g2tests, poisson.anova, anova, poisson_only, poisson.mle ```
 ```1 2 3 4 5``` ```ina <- rbinom(500, 3, 0.5) + 1 ## Poisson example y <- matrix( rpois(500 * 100, 10), ncol= 100 ) system.time(a1 <- poisson.anovas(y, ina) ) y <- NULL ```