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Update estimate_adj_matrix_for_stacked_imputed_datasets.r #5

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114 changes: 112 additions & 2 deletions pruning_missing_data/estimate_adj_matrix_for_stacked_imputed_datasets.r
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,116 @@ if (!require(miselect)) {

library(miselect)

cv.saenet_modified <- function(x, y, pf, adWeight, weights, family = c("gaussian", "binomial"),
alpha = 1, nlambda = 100, lambda.min.ratio =
ifelse(isTRUE(all.equal(adWeight, rep(1, p))), 1e-3, 1e-6),
lambda = NULL, nfolds = 5, foldid = NULL, maxit = 1000,
eps = 1e-5)
{
call <- match.call()

if (!is.list(x))
stop("'x' should be a list of numeric matrices.")
if (any(sapply(x, function(.x) !is.matrix(.x) || !is.numeric(.x))))
stop("Every 'x' should be a numeric matrix.")

dim <- dim(x[[1]])
n <- dim[1]
p <- dim[2]
m <- length(x)

if (!is.numeric(nfolds) || length(nfolds) > 1)
stop("'nfolds' should a be single number.")

if (!is.null(foldid))
if (!is.numeric(foldid) || length(foldid) != length(y[[1]]))
stop("'nfolds' should a be single number.")

fit <- saenet(x, y, pf, adWeight, weights, family, alpha, nlambda,
lambda.min.ratio, lambda, maxit, eps)

X <- do.call("rbind", x)
Y <- do.call("c", y)

weights <- rep(weights / m , m)

if (!is.null(foldid)) {
if (!is.numeric(foldid) || !is.vector(foldid) || length(foldid) != n)
stop("'foldid' must be length n numeric vector.")
nfolds <- max(foldid)
} else {
r <- n %% nfolds
q <- (n - r) / nfolds
if(r == 0) {
foldid = rep(seq(nfolds), q)
} else {
foldid = c(rep(seq(nfolds), q), seq(r))
}
foldid <- sample(foldid, n)
foldid <- rep(foldid, m)
}
if (nfolds < 3)
stop("'nfolds' must be bigger than 3.")

lambda <- fit$lambda
nlambda <- length(lambda)
X.scaled <- scale(X, scale = apply(X, 2, function(.X) stats::sd(.X) * sqrt(m)))

cvm <- array(0, c(nlambda, length(alpha), nfolds))
cvse <- matrix(nlambda, length(alpha))
for (j in seq(nfolds)) {
Y.train <- Y[foldid != j]
X.train <- subset_scaled_matrix(X.scaled, foldid != j)
w.train <- weights[foldid != j]

X.test <- X[foldid == j, , drop = F]
Y.test <- Y[foldid == j]
w.test <- weights[foldid == j]

cv.fit <- switch(match.arg(family),
gaussian = fit.saenet.gaussian(X.train, Y.train, n, p, m, w.train,
nlambda, lambda, alpha, pf, adWeight,
maxit, eps),
binomial = fit.saenet.binomial(X.train, Y.train, n, p, m, w.train,
nlambda, lambda, alpha, pf, adWeight,
maxit, eps)
)

cvm[,, j] <- cv.saenet.err(cv.fit, X.test, Y.test, w.test, m)
}

cvse <- apply(cvm, c(1, 2), stats::sd) / sqrt(nfolds)
cvm <- apply(cvm, c(1, 2), mean)

min.id = which(cvm == min(cvm), arr.ind = TRUE)
se = cvse[min.id[1,1], min.id[1,2]] # modified
range = min(cvm) + se

all.id = which(cvm < range, arr.ind = TRUE)
lambda.seq = lambda[all.id[, 1]]
alpha.seq = alpha[all.id[, 2]]
L1 = lambda.seq * alpha.seq
L1.max.id = which(L1 == max(L1))
lambda.1se.id = all.id[L1.max.id, 1]
alpha.1se.id = all.id[L1.max.id, 2]
lambda.1se = lambda[lambda.1se.id]
alpha.1se = alpha[alpha.1se.id]
i.min <- which.min(apply(cvm, 1, min))
j.min <- which.min(apply(cvm, 2, min))

lambda.min <- fit$lambda[i.min]
alpha.min <- fit$alpha[j.min]

structure(list(call = call, lambda = fit$lambda, alpha = alpha, cvm = cvm,
cvse = cvse, saenet.fit = fit,
lambda.min = lambda.min,
alpha.min = alpha.min,
lambda.1se = lambda.1se, alpha.1se =
alpha.1se, df = fit$df), class = "cv.saenet")
}

environment(cv.saenet_modified) <- asNamespace("miselect")

# command args
temp_dir <- NULL
seed <- NULL
Expand Down Expand Up @@ -104,7 +214,7 @@ for (i in 2:dim(causal_order)[1]) {
adWeight_cv <- rep(1, length(predictors))

fit <- saenet(x, y, pf, adWeight_cv, weights, family=family)
CV <- cv.saenet(x, y, pf, adWeight_cv, weights, lambda=fit$lambda, family=family)
CV <- cv.saenet_modified(x, y, pf, adWeight_cv, weights, lambda=fit$lambda, family=family)
coef_cv <- fit$coef[, 1, ]

lambda_list_cv <- fit$lambda
Expand All @@ -129,7 +239,7 @@ for (i in 2:dim(causal_order)[1]) {
adWeight_cv <- abs_beta_hat_cv ** (-gamma)

fit <- saenet(x, y, pf, adWeight_cv, weights, family=family)
CV <- cv.saenet(x, y, pf, adWeight_cv, weights, lambda=fit$lambda, family=family)
CV <- cv.saenet_modified(x, y, pf, adWeight_cv, weights, lambda=fit$lambda, family=family)

lambda_list_cv <- fit$lambda
lambda_cv <- CV[[key]]
Expand Down