# simCNVdataCaseCon: Simulation of CNV in a case-control study design In CNVassoc: Association Analysis of CNV Data and Imputed SNPs

## Description

This function simulates intensity for a CNV within cases and control groups for different scenarios

## Usage

 ```1 2``` ```simCNVdataCaseCon(n0, n1, w0, or, mu.surrog0, sd.surrog0, mu.surrog1 = mu.surrog0, sd.surrog1 = sd.surrog0, random = TRUE) ```

## Arguments

 `n0` number of controls simulated `n1` number of cases simulated `w0` vector of proportions of copy number status in controls `or` a vector of odds ratio for one, two,... copies respect to zero copies `mu.surrog0` vector of means of CNV intensity signal, per copy number status, in control group `sd.surrog0` vector of standard deviations of CNV intensity signal, per copy number status, in control group `mu.surrog1` vector of means of CNV intensity signal, per copy number status, in control group `sd.surrog1` vector of standard deviations of CNV intensity signal, per copy number status, in control group `random` A logical value. TRUE means that individuals (rows) are randomly permuted, and FALSE means that simulated 'data.frame' contains controls first and then cases. Default value is TRUE

## Details

This function is useful to calculate the power of association models in a case control study design under different scenarios ,e.g. setting different degrees of association (odds 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.

## Value

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

 `resp` Trait (response) variable with 0 or 1 if the individual is a control or a case respectively `surrog` Signal intensity following a mixture of normals with means, standard deviations and proportions specified by `mu.surrog`, `sd.surrog` and `w` respectively, within cases and controls `cnv` True copy number status

`simCNVdataBinary`, `simCNVdataNorm`, `simCNVdataPois`, `simCNVdataWeibull`, `cnv`, `CNVassoc`
 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```maf<-0.3 set.seed(123) simData<-simCNVdataCaseCon(n0=1000, n1=1000, mu.surrog0=c(0,0.5,1), sd.surrog0=rep(0.15,3), mu.surrog1=c(0,0.5,1), sd.surrog1=rep(0.15,3), w0=c((1-maf)^2,2*maf*(1-maf), maf^2), or=c(1.3,1.3^2), random = FALSE) CNV<-cnv(simData\$surrog,mix.method="EMmixt") getQualityScore(CNV,type="CNVtools") mod<-CNVassoc(resp~CNV,data=simData,family="binomial") CNVtest(mod) summary(mod) ```