simCNVdataPois: Simulate Poisson data
In CNVassoc: Association Analysis of CNV Data and Imputed SNPs

Description Usage Arguments Details Value See Also Examples

This function simulates intensity for a CNV and a discrete counting trait response for different scenarios

1	simCNVdataPois(n, mu.surrog, sd.surrog, w, lambda, cnv.random = FALSE)

`n`	An integer indicating the desired number of individuals to be simulated
`mu.surrog`	A vector containing the signal (surrogate variable) means for every copy number status (latent classes). Its length must be equal to the number of latent classes
`sd.surrog`	A vector containing the signal standard deviation for every copy number status. Its length must be equal to mu.surrog.
`w`	A vector containing the frequencies for every copy number status. Its length must be equal to mu.surrog and its components must sum up one.
`lambda`	A vector containing the means of the response variable for every copy number status. Its length must be equal to mu.surrog.
`cnv.random`	A logical value. TRUE means that copy number status is drawn under a multinomial distribution with proportions indicated by 'w'. FALSE means that the real simulated frequency is always the same and is rounded to the most similar integer to the frequencies indicated by 'w'. Default value is FALSE

This function is useful to calculate the power of association models for discrete counting trait under different scenarios ,e.g. setting different degrees of association (risk ratios), considering different degrees of uncertainty controlled by the distribution of intensity signal data, i.e. mean mu.surrog, standard deviation sd.surrog and proportion w, etc.

Data frame with individual simulated data per row and with the following variables:

`resp`	Discrete variable with simulated counts (response)
`surrog`	Signal intensity following a mixture of normals with means, standard deviations and proportions specified by `mu.surrog`, `sd.surrog` and `w` respectively
`cnv`	True copy number status

simCNVdataBinary, simCNVdataCaseCon, simCNVdataNorm, simCNVdataWeibull, cnv, CNVassoc

set.seed(123)
rr<-1.5
maf<-0.3
simData<-simCNVdataPois(n=1000, mu.surrog=c(0,0.5,1), sd.surrog=rep(0.15,3), 
         w=c((1-maf)^2,2*maf*(1-maf), maf^2), lambda=3*c(1,rr,rr^2),  cnv.random = FALSE)
CNV<-cnv(simData$surrog,mix.method="EMmixt")
getQualityScore(CNV,type="CNVtools")
mod<-CNVassoc(resp~CNV,data=simData,family="poisson",emsteps=10)
CNVtest(mod)
summary(mod)