# P-values of the "QPCorr.matrix" obtained using a permutation approach P-values of the `"QPCorr.matrix "` obtained using a permutation approach, the interactions with smaller p-values are regarded as more important. ```r QPCorr.pval( G, E, Y, tau, w = NULL, permutation_t = 1000, family = c("continuous", "survival") ) ``` ## Arguments - `G`: Input matrix of `p` genetic (G) measurements consisting of `n` rows. Each row is an observation vector. - `E`: Input matrix of `q` environmental (E) risk factors, each row is an observation vector. - `Y`: Response variable. A quantitative vector for `family="continuous"`. For `family="survival"`, `Y` should be a two-column matrix with the first column being the log(survival time) and the second column being the censoring indicator. The indicator is a binary variable, with "1" indicating dead, and "0" indicating right censored. - `tau`: Quantile. - `w`: Weight for accommodating censoring if `family="survival"`. Default is NULL and a Kaplan-Meier estimator-based weight is used. - `permutation_t`: Number of permutation. - `family`: Response type of `Y` (see above). ## Returns Matrix of p-value, with the element in the `i`th row and the `j` column represents the p-value of the (censored) quantile partial correlation corresponding to the `i`th E and the `j`th G. ## Examples ```r n=50 alpha=matrix(0,5,1) alpha[1:2]=1 beta=matrix(0,6,20) beta[1,1:4]=1 beta[2:3,1:4]=2 sigmaG<-AR(rho=0.3,20) sigmaE<-AR(rho=0.3,5) G<-MASS::mvrnorm(n,rep(0,20),sigmaG) E<-MASS::mvrnorm(n,rep(0,5),sigmaE) e1<-rnorm(n*.05,50,1);e2<-rnorm(n*.05,-50,1);e3<-rnorm((n-length(e1)-length(e2))) e<-c(e1,e2,e3) # continuous y1=simulated_data(G=G,E=E,alpha=alpha,beta=beta,error=e,family="continuous") cpqcorr_pvalue1<-QPCorr.pval(G,E,y1,tau=0.5,permutation_t=500,family="continuous") # survival y2=simulated_data(G,E,alpha,beta,rnorm(n,0,1),family="survival",0.7,0.9) cpqcorr_pvalue2<-QPCorr.pval(G,E,y2,tau=0.5,permutation_t=500,family="survival") ``` ## References Yaqing Xu, Mengyun Wu, Qingzhao Zhang, and Shuangge Ma. **Robust identification of gene-environment interactions for prognosis using a quantile partial correlation approach. Genomics, 111(5):1115-1123, 2019.** ## See Also `QPCorr.matrix` method.

QPCorr.pval function