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.Rhistory
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Sys.getenv()
Sys.getenv('PATH')
library(GPN)
library(GPN)
#'
#' @examples
#' N <- 10000
#' y1 <- replicate(2, sample(c(0,1), N, replace = TRUE, prob = c(0.999, 0.001)))
#' y2 <- replicate(2, sample(c(0,1), N, replace = TRUE, prob = c(0.5, 0.5)))
#' y3 <- replicate(2, rnorm(N))
#' y <- cbind(y1, y2, y3)
#' x <- rbinom(N,2,0.3)
#' CLC_ave(x, y, cov = NULL)
#'
CLC_ave <- function(x, y, cov = NULL){
x <- as.matrix(x)
y <- as.matrix(y)
# Check dimension of the individual-level data: x and y
if (nrow(x) != nrow(y)){
stop("Error: Please check the sample size of x and y. They should be the same!")
}
# Check the existing of the covariance matrix
if (!is.null(cov)){
cov <- as.matrix(cov)
if (nrow(cov) != nrow(x)){
stop("Error: Please check the sample size of covariance matrix, which should be the same as the sample size of individual-level genotyps and phenotypes!")
}
}
# score test statistic
Tstat <- apply(y, 2, function(ytemp){
if (!all(ytemp %in% c(0,1))){
stat <- SCORE(x, ytemp, cov)$Tstat
} else {
ccratio <- sum(ytemp)/length(ytemp)
stat <- ifelse((ccratio >= 0.4 & ccratio <= 0.6),
SCORE(x, ytemp, cov)$Tstat,
Adjusted_Tstat(x,ytemp,cov = NULL))
}
return(stat)
})
Tstat <- as.matrix(Tstat)
# Check if adjust for the covariates
if (!is.null(cov)){
x1 <- apply(x, 2, function(t) return(residuals(lm(t ~ 1+cov))))
y1 <- apply(y, 2, function(t){
if (all(t %in% c(0,1))){
return(residuals(glm(t ~ 1+cov, family = binomial(link = "logit"))))
} else {
return(residuals(lm(t ~ 1+cov)))
}
})
x <- as.matrix(x1)
y <- as.matrix(y1)
}
# Test
K <- ncol(y)
if (K == 1){
pv <- pchisq(Tstat^2, 1, lower.tail = FALSE)
} else if ( K == 2){
Sigma <- cor(y)
W <- ginv_ratio(Sigma)
U <- t(W)%*%ginv_ratio(W%*%Sigma%*%t(W))%*%W
CLC <- t(Tstat)%*%U%*%Tstat
pv <- pchisq(CLC,2,lower.tail = FALSE)
} else {
# Hierarchical clustering based on the number of clusters with minimum height
Sigma <- as.matrix(cor(y))
dist <- 1 - Sigma
hc <- hclust(as.dist(dist),"average")
H <- which.min(diff(hc$height))
index <- cutree(hc,H)
L <- max(index)
B <- sapply(1:L, function(t) as.numeric(index==t))
# CLC test
W <- ginv_ratio( t(B)%*%ginv_ratio(Sigma)%*%B ) %*% t(B) %*% ginv_ratio(Sigma)
CLC <- t( W%*%Tstat ) %*% ginv_ratio( W%*%Sigma%*%t(W) ) %*% ( W%*%Tstat )
pv <- pchisq(CLC,L,lower.tail = FALSE)
}
return(pv)
}
N <- 10000
y1 <- replicate(1, sample(c(0,1), N, replace = TRUE, prob = c(0.999, 0.001)))
x <- rbinom(N,2,0.3)
CLC_ave(x, y1, cov = NULL)
library(GPN)
N <- 10000
x <- rbinom(N,2,0.3)
y1 <- replicate(1, sample(c(0,1), N, replace = TRUE, prob = c(0.999, 0.001)))
CLC_ave(x, y1, cov = NULL)
y1 <- replicate(2, sample(c(0,1), N, replace = TRUE, prob = c(0.999, 0.001)))
CLC_ave(x, y1, cov = NULL)
y1 <- replicate(2, sample(c(0,1), N, replace = TRUE, prob = c(0.999, 0.001)))
y2 <- replicate(2, sample(c(0,1), N, replace = TRUE, prob = c(0.5, 0.5)))
y3 <- replicate(2, rnorm(N))
y <- cbind(y1, y2, y3)
CLC_ave(x, y1, cov = NULL)
devtools::document()
devtools::document()
devtools::document()
devtools::document()
devtools::document()
devtools::document()
devtools::document()
x <- rbinom(N,2,0.3)
x <- rbinom(10,2,0.3)
x <- as.matrix(x)
factor(x)
devtools::document()
devtools::document()
devtools::document()
devtools::document()
devtools::document()
library(GPN)
N <- 10000
y1 <- replicate(2, sample(c(0,1), N, replace = TRUE, prob = c(0.999, 0.001)))
y2 <- replicate(2, sample(c(0,1), N, replace = TRUE, prob = c(0.5, 0.5)))
y3 <- replicate(2, rnorm(N))
y <- cbind(y1, y2, y3)
x <- rbinom(N,2,0.3)
B <- matrix(c(1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1), nrow = 6)
Network_Test(x, y, B, test.method = "ceCLC")
Network_Test(x, y, B, test.method = "CLC")
Network_Test(x, y, B, test.method = "HCLC")
Network_Test(x, y, B, test.method = "OBrien")
Network_Test(x, y, B, test.method = "omnibus")
Network_Test(x, y, B, test.method = "TATES")
Network_Test(x, y, B, test.method = "MultiPhen")
devtools::document()
devtools::load_all()
set.seed(123)
Net <- matrix(rnorm(2000)^2, nrow = 500, ncol = 4)
p <- get_pvalue(Net)$p
out <- adjust_p(p, method = "qvalue")
library(GPN)
library(GPN)
library(GPN)
library(GPN)