Nothing
## ----ex1-----------------------------------------------------------------
library(pcev)
set.seed(12345)
Y <- matrix(rnorm(100*20), nrow=100)
X <- rnorm(100)
pcev_out <- computePCEV(Y, X)
pcev_out
## ----ex2-----------------------------------------------------------------
pcev_out2 <- computePCEV(Y, X, Wilks=TRUE)
pcev_out2
## ----ex3-----------------------------------------------------------------
pcev_out3 <- computePCEV(Y, X, shrink=TRUE)
pcev_out3
## ----data----------------------------------------------------------------
data(methylation)
data(pheno)
data(position)
## ----manPlot, fig.align='center', fig.cap="Manhattan plot"---------------
# Compute univariate p-values
fit <- lm(methylation ~ pheno)
pval <- vapply(summary(fit), function(sum) {
pvalue <- sum$coef[2,4]
return(pvalue)
}, numeric(1))
# Manhattan plot univariate
plot(position$Pos/1e6, -log10(pval), xlab="Position (Mb)",
ylab="-log10 pvalue", pch=19, cex=0.5)
abline(h=-log10(8.3*10^-6), lty=2)
## ----cluster, eval=FALSE-------------------------------------------------
# # Break the region into sub-regions of 500 kB
# cl <- bumphunter::clusterMaker(chr=position$Chr,
# pos=position$Pos,
# assumeSorted=TRUE,
# maxGap = 500)
#
# # Some blocks are too big... put limit at 30
# index <- cl
# maxInd <- max(index) + 1
#
# blockLengths <- table(index)
# while(sum(blockLengths > 30) > 0) {
#
# for (j in unique(index)) {
# p <- length(index[index == j])
# if (p > 30) {
# q <- floor(p/2); r <- p - q
# index[index == j] <- c(rep_len(maxInd, q),
# rep_len(maxInd + 1, r))
# maxInd <- maxInd + 2
# }
# }
# blockLengths <- table(index)
# }
#
# # Re-index so that we have consecutive indices
# cl <- index
# index <- cl
# counter <- 0
# for(j in sort(unique(cl))) {
# counter <- counter + 1
# index[index == j] <- counter
# }
## ----index---------------------------------------------------------------
data(index)
table(table(index))
## ----output, tidy=TRUE---------------------------------------------------
pcev_out <- computePCEV(methylation, covariate = pheno,
estimation = "block",
inference = "permutation",
index = index, nperm = 10)
pcev_out
## ----manPlotVIP, fig.align='center', fig.cap="Manhattan-VIMP plot"-------
# Manhattan plot VIMP
BLK_boundaries <- c(11235000, 11385000)
plot(position$Pos/1e6, pcev_out$VIMP, xlab = "Position (Mb)",
ylab = "Variable Importance", pch = 19, cex = 0.5,
ylim = c(0,1))
lines(x = BLK_boundaries/1e6, y = rep_len(0.9,2),
lwd = 3, col = 'red')
## ------------------------------------------------------------------------
sessionInfo()
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