Nothing
## The function implementing the sign-flip for Pelora - empirical covariance
sign.change <- function(x,y)
{
if(is.null(dx <- dim(x))) stop("'x' must be a numeric matrix")
signs <- sign(apply(x, 2, cov, y))
list(x.new = x * rep(signs, each = dx[1]), signs = signs)
}
## Computing the coefficients for penalized logistic regression
ridge.coef <- function(x,y,lambda)
{
X <- cbind(rep(1,length(y)),x)
pnlty <- diag(apply(X,2,var)*lambda*nrow(X), nrow=ncol(X))
th <- c(log(mean(y)/(1-mean(y))),rep(0,ncol(X)-1))
for(j in 1:2) {
p <- 1 / (1+exp(- drop(X %*% th)))
W <- diag(p*(1-p))
WX <- W %*% X ## <<-- FIXME: make faster (W is diagonal !)
X.WX <- crossprod(X, WX)
th <- solve(X.WX + pnlty,
crossprod(X, y-p) + X.WX %*% th)
}
drop(th)
}
## The function for the standardization of genes
standardize.genes <- function(exmat)
{
means <- apply(exmat,2,mean)
sdevs <- apply(exmat,2,sd)
for (i in 1:(dim(exmat)[2]))
{
exmat[,i] <- (exmat[,i]-means[i])/sdevs[i]
}
list(x=exmat, means=means, sdevs=sdevs)
}
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