## ----------------------------------------------------------------------------- library(SKAT) #CRAN library(bigQF) #github/tslumley set.seed(2018-5-18) ## ----------------------------------------------------------------------------- data(SKAT.example) attach(SKAT.example) #look, it's not my fault, that's how they did it. obj<-SKAT_Null_Model(y.c ~ 1, out_type="C") skat.out1<-SKAT(Z, obj) skat.qf1a<-SKAT.matrixfree(Z) skat.qf1b<-SKAT.matrixfree(Z,model=lm(y.c~1)) skat.qf1c<-SKAT.matrixfree(Z,model=glm(y.c~1)) skat.out1$Q skat.qf1a$Q(y.c) skat.qf1b$Q() ## phenotype used in fitting skat.qf1b$Q(y.c) ## new phenotype skat.out1$p.value pQF(skat.out1$Q,skat.qf1a,neig=60,convolution.method="integration" ) pQF(skat.out1$Q,skat.qf1b,neig=60,convolution.method="integration" ) pQF(skat.out1$Q,skat.qf1c,neig=60,convolution.method="integration" ) ## ---- warning=FALSE----------------------------------------------------------- set.seed(2018-5-18) p<-lapply(1:65, function(k) pQF(skat.out1$Q, skat.qf1a, neig=k, convolution.method="integration",tr2.sample.size=1000 ) ) pdf<-data.frame(p=do.call(c,p),k=1:65) plot(p~k,data=pdf,pch=19,col="orange", ylim=c(0.017,0.020)) abline(h=skat.out1$p.value,lty=2)