normalityCheckQQ | R Documentation |
Produce a qq-plot to visually check whether the assumption of multivariate normality of the linear predictors is valid for the data and model fit with 'squeezy'.
normalityCheckQQ(X,groupset,fit.squeezy,nSim=500)
X |
Observed data; (nxp)-dimensional matrix (p: number of covariates) with each row the observed high-dimensional feature vector of a sample. |
groupset |
Co-data group set; list with G groups. Each group is a vector containing the indices of the covariates in that group. |
fit.squeezy |
Model fit obtained by the function squeezy. |
nSim |
Number of simulated vectors of linear predictors. |
The qqplot of the empirical versus theoretical quantiles is plotted. If ‘ggplot2’ is installed, the plot is returned as ‘ggplot’ object.
#Simulate toy data n<-100 p<-300 X <- matrix(rnorm(n*p),n,p) Y <- rnorm(n) groupset <- list(1:(p/2),(p/2+1):p) sigmahat <- 2 alpha <- 0.5 tauMR <- c(0.01,0.005) #Fit group-regularised elastic net model with squeezy fit.squeezy <- squeezy(Y,X,groupset,alpha=alpha, lambdas=sigmahat/tauMR,sigmasq=sigmahat, lambdaglobal=mean(sigmahat/tauMR)) #Check qq-plot normalityCheckQQ(X,groupset,fit.squeezy)
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