simCNVdataBinary: Simulation of CNV and discrete traits
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 binary trait response for different scenarios

1	simCNVdataBinary(n, mu.surrog, sd.surrog, w, p0, or, cnv.random = FALSE)

`n`	number of simulated individuals
`mu.surrog`	a vector of intensity signal means for every copy number status
`sd.surrog`	a vector of intensity signal standard deviations for every copy number status
`w`	a vector of copy number status proportions
`p0`	prevalence of disease (trait) for populations with zero copies (reference category)
`or`	a vector of odds ratio for one, two,... copies respect to zero copies
`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 with binary traits 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.

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

`resp`	Trait (response) variable following a Bernoulli distribution given the CNV status
`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

simCNVdataCaseCon, simCNVdataNorm, simCNVdataPois, simCNVdataWeibull, cnv, CNVassoc

maf<-0.3
set.seed(123)
simData<-simCNVdataBinary(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), p0=0.1, or=c(1.3,1.3^2), cnv.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)

Loading required package: CNVassocData
Loading required package: mixdist
Loading required package: mclust
Package 'mclust' version 5.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: survival
--CNVtools Quality Score: 3.348075 
----CNV Wald test----
Chi= 1.156755 (df= 2 ) , pvalue= 0.5608077 


Call:
CNVassoc(formula = resp ~ CNV, data = simData, family = "binomial")

Deviance: 736.682 
Number of parameters: 3 
Number of individuals: 1000 

Coefficients:
         OR lower.lim upper.lim     SE   stat pvalue
CNV0 1.0000                                         
CNV1 1.1912    0.7665    1.8512 0.2249 0.7777  0.437
CNV2 1.3851    0.6897    2.7816 0.3558 0.9156  0.360

(Dispersion parameter for  binomial  family taken to be  1 )


Covariance between coefficients:
     CNV1    CNV2    CNV3   
CNV1  0.0224 -0.0019  0.0003
CNV2          0.0243 -0.0038
CNV3                  0.1047