R: Bonferroni correction of p values

Bonferroni.sig {SNPassoc} | R Documentation

## Bonferroni correction of p values

### Description

This function shows the SNPs that are statistically significant after correcting for the number of tests performed (Bonferroni correction) for an object of class "WGassociation"

### Usage

     Bonferroni.sig(x, model = "codominant", alpha = 0.05,
          include.all.SNPs=FALSE)

### Arguments

`x` | an object of class 'WGassociation'.
`model` | a character string specifying the type of genetic model (mode of inheritance). This indicantes how the genotypes should be collapsed when 'plot.summary' is TRUE. Possible values are "codominant", "dominant", "recessive", "overdominant", or "log-additive". The default is "codominant". Only the first words are required, e.g "co", "do", ... .
`alpha` | nominal level of significance. Default is 0.05
`include.all.SNPs` | logical value indicating whether all SNPs are considered in the Bonferroni correction. That is, the number of performed tests is equal to the number of SNPs or equal to the number of SNPs where a p value may be computed. The default value is FALSE indicating that the number of tests is equal to the number of SNPs that are non Monomorphic and the rate of genotyping is greater than the percentage indicated in the `GeneticModel.pval` function.

### Details

After deciding the genetic model, the function shows the SNPs that are statistically significant at `alpha` level corrected by the number of performed tests.

### Value

A data frame with the SNPs and the p values for those SNPs that are statistically significant after Bonferroni correction

### See Also

`WGassociation`

### Examples

    data(SNPs)
    datSNP<-setupSNP(SNPs,6:40,sep="")
    ans<-WGassociation(protein~1,data=datSNP,model="all")
    Bonferroni.sig(ans, model="codominant", alpha=0.05, include.all.SNPs=FALSE)

[Package SNPassoc version 1.9-2 Index]