Nothing
DMR4lm <- function(X, y, clust.method, lam){
out <- prelasso_common(X, y)
X <- out$X #DMR specific
factor_columns<- out$factor_columns #DMR specific
n <- out$n
n.levels <- out$n.levels #DMR specific
n.factors <- out$n.factors #DMR specific
n.cont <- out$n.cont #DMR specific
names.cont <- out$names.cont #DMR specific
levels.listed <- out$levels.listed
#fl <- out$fl #not needed in DMR
x.full <- out$x.full #DMR specific
p <- out$p
p.x <- out$p.x
p.fac <- out$p.fac #DMR specific
ord <- out$ord
#important: x.full (and its successors, like Ro later) is ordered: first intercept, then factors (and their levels), then numeric column
if (p >= n) {
stop("Error: p >= n, DMR works only for p < n, use DMRnet instead")
}
m <- stats::lm.fit(x.full, y)
#QR decompostion of the model matrix
qX <- qr.Q(m$qr, complete=FALSE) #matrix Q, eq. 3.1 of A.Prochenka PhD Thesis
#explicitly stating that we want partial results (https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/QR.Auxiliaries)
rX <- qr.R(m$qr) + diag(rep(lam, ncol(x.full))) #matrix R, eq. 3.1 of A.Prochenka PhD Thesis, regularized with a diagonal matrix
Ro <- solve(rX) #matrix R^-1, eq. 3.1 of A.Prochenka PhD Thesis
#alternatively: z <- qr.qty(m$qr, y)[1:m$qr$rank] #the rest of the columns are from the complete Q matrix (https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/qr)
z <- t(qX)%*%y #vector z, eq. 3.1 of A.Prochenka PhD Thesis
sigma_sq <- as.numeric((t(m$res)%*%m$res)/(n - p)) #variance estimator sigma hat squared, eq. 3.1 of A.Prochenka PhD Thesis
#dissimilarity measures - matrices of squared t-statistics for each factor
if (n.factors > 0){
Tmats <- lapply(1:n.factors, function(i) {
i1 <- ifelse(i == 1, 2, sum(n.levels[1:(i - 1)] - 1) + 2) #first (cumulatively) level of i-th factor
i2 <- sum(n.levels[1:i] - 1) + 1 #last (cumulatively) level of i-th factor
#in Ro levels come first and numeric columns last
out <- t_stats(Ro[i1:i2,], ind1 = i1, ind2 = i2, sigma_sq = sigma_sq, z = z)
rownames(out) <- colnames(out) <- m$xlevels[[i]]
return(out)
})
#cutting dendrograms
models <- lapply(Tmats, function(x) stats::hclust(stats::as.dist(t(x)), method = clust.method, members = NULL))
heig <- lapply(1:n.factors, function(x){
out <- models[[x]]$he
names(out)<- rep(x, length(out))
out
})
heig <- unlist(heig)
} else {
heig <- c()
models <- list()
}
heig <- c(0,heig)
names(heig)[1] = "full"
if ((p.fac + 1) < p){
if((p.fac + 2) == p){
heig.add <- ((Ro[(p.fac + 2):p,]%*%z)^2)/(sigma_sq*sum(Ro[(p.fac + 2):p,]^2))
} else {
heig.add <- ((Ro[(p.fac + 2):p,]%*%z)^2)/(sigma_sq*(apply(Ro[(p.fac + 2):p, ], 1, function(y) t(y)%*%y)))
}
names(heig.add) <- colnames(x.full)[(p.fac + 2):p]
heig <- c(heig, heig.add)
}
heig <- sort(heig)
len <- length(heig)
#fitting models on the path
Z1 <- Z2 <- c()
sp <- list()
form <- c()
nl <- 0
Z1 <- X[, factor_columns, drop = FALSE]
if (n.factors > 0){
for (i in 1:n.factors){
sp[[i]] <- 1:n.levels[i]
sp[[i]][sp[[i]] != 1] <- sp[[i]][sp[[i]] != 1] + nl
nl <- nl + length(unique(sp[[i]])) - 1
}
}
Z2 <- X[,names.cont, drop = FALSE]
Z <- cbind(Z1,Z2)
dane <- data.frame(y=y, Z, check.names = T)
ZZ <- stats::model.matrix(y~., data = dane)
m <- stats::lm.fit(ZZ, y)
b <- m$coef
rss = sum(m$res^2)
form <- names.cont
if (len > 2){
for (i in 2:(len - 1)){
kt <- names(heig)[i]
if(length(intersect(kt, names.cont)) > 0){
form <- form[-which(form == kt)]
Z2 <- Z2[, form, drop = FALSE]
} else {
kt <- as.numeric(kt)
spold <- sp[[kt]]
sp[[kt]] <- stats::cutree(models[[kt]], h = heig[i])
if(length(sp[[kt]][sp[[kt]] != 1]) > 0){
sp[[kt]][sp[[kt]] != 1] <- sp[[kt]][sp[[kt]] != 1] + min(spold[spold != 1]) - min(sp[[kt]][sp[[kt]] != 1])
}
Z1[,kt] <- X[, factor_columns[kt]]
levels(Z1[,kt]) <- sp[[kt]]
Z1[,kt] <- factor(Z1[,kt])
if (kt < length(sp)) for( x in (kt+1):length(sp)){ if (length(sp[[x]][sp[[x]]!=1]) > 0 ) sp[[x]][sp[[x]]!= 1] = sp[[x]][sp[[x]]!=1] - 1}
nl <- nl - 1
}
Z <- cbind(Z1[,which(apply(Z1, 2, function(x) length(unique(x))) != 1)], Z2)
dane <- data.frame(y = y, Z, check.names = T)
ZZ <- stats::model.matrix(y~., data = dane)
m <- stats::lm.fit(ZZ, y)
be <- c(0, m$coef[-1])
bb <- c()
if(n.factors > 0){
bb <- unlist(sapply(1:length(sp), function(j) sp[[j]][-1]))
}
bb2 <- rep(1, n.cont)
names(bb2) <- names.cont
if(length(form) > 0){
bb2[form] <- (nl + 2):(nl + 1 + length(form))
}
bb <- c(bb, bb2)
b=cbind(b, c(m$coef[1], be[bb]))
rss = c(rss, sum(m$res^2))
}
}
m <- stats::lm.fit(as.matrix(rep(1, length(y))), y)
min_value <- min(c(abs(m$coef[!is.na(m$coef)]), abs(b[!is.na(b) & (b!=0)])))
b[is.na(b)] <- min_value / 1000.0
m$coef[is.na(m$coef)] <- min_value / 1000.0 #setting a very small (close to 0) value for the variables exceeding design matrix rank
#consult the comment in part2beta_help() for longer explanation
b <- cbind(b, c(m$coef, rep(0, length(heig) - 1)))
rss <- c(rss, sum(m$res^2))
b <- b[ord,] #reordering betas to reflect the original matrix X
fit <- list(beta = b, df = p:1, rss = rss, n = n, levels.listed = levels.listed, lambda=numeric(0), arguments = list(family = "binomial", clust.method = clust.method), interc = TRUE)
class(fit) = "DMR"
return(fit)
}
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