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| # https://gist.github.com/bobthecat/5024079 | |
| bigcorPar <- function(x, nblocks = 10, verbose = TRUE, ncore="all",pvalue=0.05, ...){ | |
| library(ff, quietly = TRUE) | |
| library(psych) | |
| require(doMC) | |
| if(ncore=="all"){ | |
| ncore = 30 | |
| registerDoMC(cores = ncore) | |
| } else{ | |
| registerDoMC(cores = ncore) | |
| } | |
| NCOL <- ncol(x) | |
| ## test if ncol(x) %% nblocks gives remainder 0 | |
| if (NCOL %% nblocks != 0){stop("Choose different 'nblocks' so that ncol(x) %% nblocks = 0!")} | |
| ## preallocate square matrix of dimension | |
| ## ncol(x) in 'ff' single format | |
| corMAT <- ff(vmode = "single", dim = c(NCOL, NCOL)) | |
| pMAT <- ff(vmode = "single", dim = c(NCOL, NCOL)) | |
| ## split column numbers into 'nblocks' groups | |
| SPLIT <- split(1:NCOL, rep(1:nblocks, each = NCOL/nblocks)) | |
| ## create all unique combinations of blocks | |
| COMBS <- expand.grid(1:length(SPLIT), 1:length(SPLIT)) | |
| COMBS <- t(apply(COMBS, 1, sort)) | |
| COMBS <- unique(COMBS) | |
| ## iterate through each block combination, calculate correlation matrix | |
| ## between blocks and store them in the preallocated matrix on both | |
| ## symmetric sides of the diagonal | |
| results <- foreach(i = 1:nrow(COMBS)) %dopar% { | |
| COMB <- COMBS[i, ] | |
| G1 <- SPLIT[[COMB[1]]] | |
| G2 <- SPLIT[[COMB[2]]] | |
| if (verbose) cat("Block", COMB[1], "with Block", COMB[2], "\n") | |
| flush.console() | |
| #COR <- cor(x[, G1], x[, G2], ...) | |
| tmpCOR <- corr.test(x[, G1], x[, G2], adjust = "none",ci = F,use = "complete", ...) | |
| COR<- tmpCOR$r#[which(tmpCOR$p > pvalue)] <- 0 | |
| PVALUE <- tmpCOR$p | |
| corMAT[G1, G2] <- COR | |
| corMAT[G2, G1] <- t(COR) | |
| pMAT[G1, G2] <- PVALUE | |
| pMAT[G2, G1] <- t(PVALUE) | |
| COR <- NULL | |
| PVALUE <- NULL | |
| } | |
| gc() | |
| return(list(cor=corMAT,pvalue=pMAT)) | |
| } |
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use psych::corr.test to filter p-value less than a threshold