CNVassoc (version 1.4)

simCNVdataNorm: Simulation of CNV and quantitative traits

Description

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

Usage

simCNVdataNorm(n, mu.surrog, sd.surrog, w, mu.y, sd.y, cnv.random = FALSE)

Arguments

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.

mu.y

A vector containing the means of the response variable for every copy number status. Its length must be equal to mu.surrog.

sd.y

A single number indicating the residual standard deviation

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

Value

• Data frame with individual simulated data per row and with the following variables:
• resp
  Continous trait variable (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

Details

This function is useful to calculate the power of association models for a continuous (normal-distributed) trait under different scenarios ,e.g. setting different degrees of association (effects), 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.

See Also

simCNVdataBinary, simCNVdataCaseCon, simCNVdataPois, simCNVdataWeibull, cnv, CNVassoc

Examples

set.seed(123)
maf<-0.3
effect<-3
simData<-simCNVdataNorm(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), mu.y=100+c(0,effect,2*effect), sd.y=rep(20,3), cnv.random = FALSE)
CNV<-cnv(simData$surrog,mix.method="EMmixt")
getQualityScore(CNV,type="CNVtools")
mod<-CNVassoc(resp~CNV,data=simData,family="gaussian",emsteps=10)
CNVtest(mod)
summary(mod)