R/get_survival_linear.R
In ericdunipace/SLIMpaper: Interpretable Model Summaries Using the Wasserstein Distance
Defines functions
get_survival_linear_model
get_survival_linear_model <- function() {
#### Prior Function ####
rprior_coef <- function(n, mu, sigma){
return(rmvnorm(n, mu, sigma))
}
rprior_sigma <- function(n, alpha, beta){
return(NULL)
}
rprior<- function(n, hyperparameters) {
stopifnot(is.list(hyperparameters))
stopifnot(length(hyperparameters) >= 2)
stopifnot(all(c("mu","sigma") %in% names(hyperparameters) ))
stopifnot(length(hyperparameters$mu) == ncol(as.matrix(hyperparameters$sigma)))
return(list(theta = rprior_coef(n, hyperparameters$mu, hyperparameters$sigma),
sigma2 = NULL))
}
#### X Data ####
# trajectory <- function(nT, cov, p, ARM) {
# sds <- sqrt(diag(cov))
# corr <- diag(1/sds) %*% cov %*% diag(1/sds)
# x <- rmvnorm(nT, rep(0, p), corr)
# for(i in 2:nT) x[i,] <- x[i-1,] * ARM + x[i,]
#
# return(x)
# }
# rX <- function(n, corr, p,...)
# {
# dots <- list(...)
# nT <- dots$nT
# if(is.null(nT)) nT <- 1
# ARM <- dots$ARM
# if(is.null(ARM)) ARM <- rnorm(p-1)
# if (is.matrix(corr)) {
# x_cov <- corr
# }
# else {
# x_cov <- matrix(corr, nrow = p - 1, ncol = p - 1)
# }
# diag(x_cov) <- 1
# x_list <- lapply(1:n, function(i) trajectory(nT, x_cov, p-1, ARM))
# x <- matrix(NA, nrow=n*nT, ncol=p-1)
# for(i in 1:n) x[(nT*(i-1)+1):(nT*i),] <- x_list[[i]]
# X <- cbind(1,x)
# return(X)
# }
rX <- gen_x()$rX
#### Y Data ####
rdata <- function(n, x, theta, ...) {
dots <- list(...)
# id <- dots$id
corr.x <- dots$corr
scale <- dots$scale
if(is.null(corr.x)) corr.x <- 0
if(is.null(scale)) scale <- TRUE
if(ncol(x) > length(theta)) {
x <- x[, 1: length(theta), drop=FALSE]
warning("Ncol X > length(theta). Only using first length(theta) columns of X.")
}
is.intercept <- all(x[,1] == 1)
if(is.intercept) {
intercept <- theta[1]
theta <- theta[-1]
x <- x[,-1, drop=FALSE]
} else {
intercept <- 0
}
p <- ncol(x)
corr.mat <- corr_mat_construct(corr.x, p)
diag(corr.mat) <- 1
theta_norm <- c(t(theta) %*% corr.mat %*% theta)
theta_scaled <- if(scale) {
theta/sqrt(theta_norm)
} else {
theta
}
log.rate <- x %*% theta_scaled + intercept
mu <- exp(log.rate)
Y <- rexp(n, mu)
return(list(Y= Y, mu = mu, eta = log.rate, link = Gamma(link = log)$linkfun, invlink = Gamma(link = log)$linkinv, param = theta))
}
#### Parameters ####
rparam <- get_param()$exponential
#### Posterior on Coefficients
rpost_coef <- function(x){NULL}
rpost_sigma <- function(x){NULL}
rpost <- function(n.samp, x, y, hyperparameters=list(),...) { #uses RJAGS
# require(rjags)
dots <- list(...)
id <- dots$id
follow.up <- y
fail <- dots$fail
if (is.matrix(follow.up)) {
if(ncol(follow.up) == 2 & is.null(fail)) {
fail <- follow.up[,2]
follow.up <- follow.up[,1]
} else if (ncol(follow.up) == 2 & !is.null(fail) ){
stop("if fail is null, then follow.up must be a two column matrix with the first column the failure times and the second column the event indicator. Do not specify both fail and make follow.up a matrix")
}
}
jags_dir <- dots$jags_dir
thin <- dots$thin
model <- dots$model
nchain <- dots$nchain
X.test <- dots$X.test
method <- dots$method
seed <- dots$seed
parallel <- dots$parallel
cutpoints <- dots$cutpoints
n.intervals <- dots$n.intervals
is.exponential <- dots$is.exponential
if(is.null(is.exponential)) is.exponential <- FALSE
if(is.null(method)) method <- "ss-jags"
test <- list(eta = NULL, mu = NULL)
if(is.null(thin)) thin <- 1
if(all(x[,1]==1) || all(x[,1]==0)) x <- x[,-1]
if(is.null(nchain)) nchain <- 1
if(is.null(id)) id <- 1:length(y)
# a <- hyperparameters$alpha
# b <- hyperparameters$beta
m <- hyperparameters$mu
s <- hyperparameters$sigma
prior_frac <- hyperparameters$prior_frac
spike_a <- hyperparameters$spike_a
spike_b <- hyperparameters$spike_b
if(is.null(spike_a)) spike_a <- 1
if(is.null(spike_b)) spike_b <- 1
lambda <- 1/s
# n <- length(y)
# get_x <- function(x, id, follow.up, time) {
# X <- matrix(NA, nrow=length(id) * length(follow.up), ncol=ncol(x))
# id_unique <- unique(id)
# for(i in seq_along(id_unique)){
# fup <- follow.up[i]
# current_id <- id == id_unique[i]
# curr_x <- x[current_id ,]
#
# }
# }
# if(any(follow.up > dots$time)) follow.up[follow.up > dots$time] <- max(dots$time)
times <- sort(c(0,unique(follow.up)))
mu <- eta <- alpha <- theta <- NULL
n <- length(id)
# obs.time <- tapply(dots$time, id, max)
obs.time <- follow.up
if (is.null(fail )) {
fail <- rep(1, length(unique(id))) # ifelse(obs.time < 5, 1, 0)
}
surv.calc <- function(eta, fit) {
n <- nrow(eta)
max.t <- max(fit$Surv[, 1], na.rm = T)
last.d <- nrow(fit$d.scaled)
d.scaled <- fit$d.scaled
d.scaled[last.d,] <- max.t
length.int <- apply(d.scaled, 2, diff)
H <- apply(length.int * fit$h.scaled[-1,], 2, cumsum)
mu <- lapply(1:n, function(i) exp(-H * exp(eta[i,])))
for(i in 1:n) rownames(mu[[i]]) <- paste0("(",format(d.scaled[1:(last.d-1),1]),", ", format(d.scaled[2:last.d,1]),"]")
return(mu)
}
if(!is.null(parallel)) {
if( parallel ) {
parallel.MPI <- TRUE
ncpu <- parallel::detectCores()-1
} else {
parallel.MPI <- FALSE
ncpu <- 1
}
} else {
parallel.MPI <- FALSE
ncpu <- 1
}
if(!is.null(seed)) set.seed(seed)
if(method == "ss-jags") {
mu_vec <- rep(m, ncol(x))
prec_matrix <- matrix(0, ncol(x), ncol(x))
diag(prec_matrix) <- lambda
jags_data <-
list(
N = length(unique(id)),
T = length(times)-1,
P = ncol(x),
obs.time = obs.time,
time = times,
eps = 0.0001,
X = x,
fail = fail,
mu_0 = as.double(m),
prec_0 = as.double(lambda),
prior_frac = prior_frac,
spike_a = spike_a,
spike_b = spike_b,
mu_vec = mu_vec,
prec_matrix = prec_matrix
)
if (is.null(model)) {
model <- rjags::jags.model(file=jags_dir,data = jags_data, n.chains = nchain, n.adapt = n.samp*thin*.1)
} else {
model$recompile()
}
total_iter <- n.samp*thin + ceiling(n.samp*thin/nchain)
samples <- rjags::jags.samples(model,
variable.names = c("beta","alpha","prob","baseHaz"),
n.iter = total_iter, thin = thin)
remove_burnin.idx <- round(seq(nsamp+1, total_iter, length.out = nsamp))
final_nsamp <- length(remove_burnin.idx)
# nsamp_portion <- floor(final_nsamp/nchain)
# adjust_remove.idx <- remove_burnin.idx[((nsamp - nsamp_portion + 1):nsamp)]
adjust_remove.idx <- remove_burnin.idx[((nsamp - final_nsamp + 1):nsamp)]
theta_samp <- samples$beta[ , adjust_remove.idx, ]
alpha_samp <- samples$alpha[, adjust_remove.idx, ]
if(nchain > 1) {
theta <- matrix(NA, ncol=final_nsamp, nrow = dim(theta_samp)[1])
alpha <- matrix(NA, ncol=final_nsamp, nrow = dim(alpha_samp)[1])
for(i in 1:nchain){
get_rows <- (i-1)*nsamp_portion + (1:nsamp_portion)
theta[,get_rows] <- theta_samp[,,i]
alpha[,get_rows] <- alpha_samp[,,i]
}
} else {
theta <- theta_samp
alpha <- alpha_samp
}
eta <- cbind(1,x) %*% theta
if(!is.null(X.test)){
if(all(X.test[,1]==1) || all(X.test[,1]==0)) X.test <- cbind(1,X.test)
test$eta <- X.test %*% theta
}
} else if(method == "jags") {
mu_vec <- rep(m, ncol(x))
prec_matrix <- matrix(0, ncol(x), ncol(x))
diag(prec_matrix) <- lambda
jags_data <-
list(
N = length(unique(id)),
T = length(times)-1,
P = ncol(x),
obs.time = obs.time,
time = times,
eps = 0.0001,
X = x,
fail = fail,
mu_0 = as.double(m),
prec_0 = as.double(lambda),
prior_frac = prior_frac,
spike_a = spike_a,
spike_b = spike_b,
mu_vec = mu_vec,
prec_matrix = prec_matrix
)
if (is.null(model)) {
model <- rjags::jags.model(file=jags_dir,data = jags_data, n.chains = nchain, n.adapt = n.samp*thin*.1)
} else {
model$recompile()
}
total_iter <- n.samp*thin + ceiling(n.samp*thin/nchain)
samples <- rjags::jags.samples(model,
variable.names = c("beta", "baseHaz"),
n.iter = total_iter, thin = thin)
saved_iter <- n.samp + ceiling(n.samp / nchain)
nsamp_portion <- ceiling(n.samp/nchain)
remove_burnin.idx <- round(seq(nsamp+1, saved_iter, length.out = nsamp_portion))
# final_nsamp <- length(remove_burnin.idx)
# adjust_remove.idx <- remove_burnin.idx[((nsamp - nsamp_portion + 1):nsamp)]
theta_samp <- samples$beta[ , remove_burnin.idx, ]
# alpha_samp <- samples$alpha[, adjust_remove.idx, ]
if(nchain > 1) {
theta <- matrix(NA, ncol=nsamp, nrow = dim(theta_samp)[1])
# alpha <- matrix(NA, nrow=final_nsamp, ncol = dim(alpha_samp)[1])
for(i in 1:nchain){
get_rows <- (i-1)*nsamp_portion + (1:nsamp_portion)
theta[,get_rows] <- theta_samp[,,i]
# alpha[get_rows,] <- t(alpha_samp[,,i])
}
} else {
theta <- theta_samp
# alpha <- t(alpha_samp)
}
if(nrow(theta) == (ncol(x) + 1)){
X.prod <- cbind(1,x)
} else{
X.prod <- x
}
eta <- X.prod %*% theta
if(!is.null(X.test)){
if(ncol(theta) == (ncol(X.test) + 1)) X.test <- cbind(1,X.test)
test$eta <- X.test %*% theta
}
} else if (method == "bvs-cox") {
# require(BVSNLP)
# select median prob model first so don't have to run all 13000+ covariates in bayesian regression
cat("Selecting median probability model (MPM)")
resp <- cbind(follow.up, fail)
xdf <- as.data.frame(log(x))
sel <- BVSNLP::bvs(X = xdf, resp = resp, family = "survival",
niter = n.samp*10, prep = TRUE, mod_prior = "unif", logT = FALSE,
inseed = sample.int(.Machine$integer.max,1), ncpu = ncpu, parallel.MPI = parallel.MPI)
mpm <- sel$MPM
if(length(mpm)==0) {
mpm <- sort(unique(unlist(sel$max_models[sel$max_prob_vec > (log(0.01) + sel$max_prob)])))
}
# eta <- BVSNLP::predBMA(fit, X=df, resp = resp, prep=TRUE, logT=FALSE, family = "survival")
# eta <- fit$des_mat %*% fit$beta_hat
# alpha <- fit$inc_probs
#
# test$eta <- X.test[,colnames(fit$des_mat)] %*% fit$beta_hat
survform <- formula(survival::Surv(time = follow.up, event = fail) ~ .)
# x_sc <- scale(log(x))
# xt_sc <- NULL
# if(!is.null(X.test)) {
# xt_sc <- scale(log(X.test), center = attr(x_sc,"scaled:center"), scale = attr(x_sc, "scaled:scale"))
# }
x_sc <- xdf[,mpm, drop=FALSE]
df <- as.data.frame(cbind(follow.up, fail, x_sc))
colnames(df) <- c("follow.up","fail", colnames(x_sc))
pred <- list(xpred = x_sc)
if(!is.null(X.test)) {
xt_sc <- scale(log(X.test[,mpm,drop=FALSE]), center = colMeans(x_sc), scale = colSD(x_sc))
pred <- list(xpred = rbind(scale(x_sc), xt_sc))
}
cat("Running Bayesian cox on MPM")
fit <- spBayesSurv::indeptCoxph(survform, data = df, prediction = pred,
mcmc = list(nburn = n.samp, nsave = n.samp, nskip = 0, ndisplay = 100),
prior = NULL, state = NULL, scale.designX = TRUE)
eta <- fit$X.scaled %*% fit$beta.scaled
mu <- list(time = NULL, S = NULL)
mu$time <- fit$Tpred[1:n,,drop=FALSE]
mu$S <- surv.calc(eta, fit)
if(!is.null(X.test)) {
test$eta <- xt_sc %*% fit$beta.scaled
test$mu <- list(time = NULL, S = NULL)
test$mu$time <- fit$Tpred[(n+1):nrow(fit$Tpred),,drop=FALSE]
test$mu$S <- surv.calc(test$eta, fit)
}
model <- fit
} else if (method == "cox") {
x_sc <- scale(x) #log(x)
df <- as.data.frame(cbind(follow.up, fail, x_sc))
colnames(df) <- c("follow.up","fail", colnames(x_sc))
pred <- list(xpred = x_sc)
if(!is.null(X.test)) {
xt_sc <- scale(X.test[,mpm,drop=FALSE], center = colMeans(x_sc), scale = colSD(x_sc))
pred <- list(xpred = rbind(scale(x_sc), xt_sc))
}
survform <- formula(survival::Surv(time = follow.up, event = fail) ~ .)
fit <- spBayesSurv::indeptCoxph(survform, data = df, prediction = pred,
mcmc = list(nburn = n.samp*thin, nsave = n.samp, nskip = thin,
ndisplay = n.samp/10),
prior = NULL, state = NULL, scale.designX = TRUE)
eta <- fit$X.scaled %*% fit$beta.scaled
theta <- fit$beta.scaled
mu <- list(time = NULL, S = NULL)
mu$time <- fit$Tpred[1:n,,drop=FALSE]
mu$S <- surv.calc(eta, fit)
if(!is.null(X.test)) {
test$eta <- xt_sc %*% fit$beta.scaled
test$mu <- list(time = NULL, S = NULL)
test$mu$time <- fit$Tpred[(n+1):nrow(fit$Tpred),,drop=FALSE]
test$mu$S <- surv.calc(test$eta, fit)
}
model <- fit
} else if (method == "bvs") {
theta <- NULL
alpha <- NULL
mu <- NULL
eta <- NULL
# require(BVSNLP)
# require(doParallel)
# require(foreach)
resp <- cbind(follow.up, fail)
xdf <- as.data.frame(log(x))
model <- bvsmod(X = xdf, resp = resp, family = "survival",
niter = n.samp, prep = TRUE, mod_prior = "unif", logT = FALSE,
inseed = sample.int(.Machine$integer.max, 1), ncpu = ncpu, parallel.MPI = parallel.MPI)
} else if (method == "inla") {
# if(all(x[,1]==1) || all(x[,1]==0)) x <- x[,-1]
sx <- scale(x)
xdf <- as.data.frame(sx)
pred.names <- colnames(xdf)
colnames(xdf) <- paste0("x",1:ncol(xdf))
intercept1 <- rep(1,n)
# index_df <- matrix(1,nrow=n, ncol=ncol(xdf))
# index_names <- paste0("index_", colnames(xdf))
# colnames(index_df) <- index_names
# df <- cbind(data.frame(time = follow.up, event = fail), xdf, index_df)
#lambda = 0.4712777
# hc <- "expression:
# lambda = 0.01;
# precision = exp(log_precision);
# logdens = -1.5*log_precision-log(pi*lambda)-log(1+1/(precision*lambda^2));
# log_jacobian = log_precision;
# return(logdens+log_jacobian);"
# hcprior <- list(prec = list(prior = hc))
# fs <- paste0("f(", index_names,",", colnames(xdf), ", model = 'iid', hyper = hcprior)")
# survform <- formula(paste(c("inla.surv(time, event) ~ 1 ", fs), collapse = " + "))
df <- cbind(data.frame(time = follow.up, event = fail), intercept1, xdf)
survform <- formula(paste(c("inla.surv(time, event) ~ 0 + intercept1", colnames(xdf)), collapse = " + "))
# if(is.null(cutpoints)) cutpoints <- NULL
if(is.null(n.intervals)) n.intervals <- 15
cox.call <- inla.coxph(survform, df, control.hazard = list(model = "rw1",
n.intervals = n.intervals,
cutpoints = cutpoints,
constr = TRUE))
# timings <- proc.time()
# model <- inla(survform, family = "coxph",
# data = df, control.fixed=list(mean=m[1], prec=1/(s[1])),
# debug = TRUE, verbose=FALSE)
model.gauss <- inla(cox.call$formula, family = cox.call$family,
data = c(as.list(cox.call$data), cox.call$data.list),
E = cox.call$E,
control.fixed=list(mean=m[1], prec=lambda[1]),
control.inla = list(strategy = "gaussian")
)
model <- inla(cox.call$formula, family = cox.call$family,
data = c(as.list(cox.call$data), cox.call$data.list),
E = cox.call$E,
control.fixed=list(mean=m[1], prec=lambda[1]),
control.inla = list(strategy = "laplace", fast = FALSE),
control.mode = list(result = model.gauss, restart = TRUE),
control.compute=list(config=TRUE))
# print(proc.time() - timings)
samples <- inla.posterior.sample(n = n.samp, result = model, intern = FALSE,
use.improved.mean = TRUE,
add.names = FALSE, seed = -1L, num.threads = 1L)
fe.names <- model$names.fixed
st.names <- rownames(samples[[1]]$latent)
pred.exclude <- !grepl("Predictor", st.names)
bh.exclude <- !grepl("baseline.hazard", st.names)
which.fe <- which(pred.exclude & bh.exclude)
which.not.pred <- which(pred.exclude)
theta <- matrix(sapply(samples, function(ss) ss$latent[which.fe,]), ncol=n.samp)
rownames(theta) <- fe.names
save.samples <- sapply(samples, function(ss) ss$latent[which.not.pred,])
model$samples <- save.samples
surv.calc <- function(model, theta, x) {
n <- nrow(x)
intercept <- theta[1,]
theta_reg <- theta[-1,]
eta <- x %*% theta_reg
haz.times <- model$.args$data$baseline.hazard.values
# if(is.null(times)) times <- haz.times
log_BH <- model$samples[grep("baseline.hazard", rownames(model$samples)),]
nT <- nrow(log_BH)
nS <- ncol(theta)
log_BH <- log_BH + matrix(intercept, nT, nS, byrow=TRUE)
# cutTimes <- cut(times, haz.times, include.lowest = TRUE)
BH <- exp(log_BH) * diff(c(haz.times, Inf))
# BH_times <- BH
cumHaz <- apply(BH,2,cumsum)
baseSurv <- exp(-cumHaz)
rownames(baseSurv) <- haz.times
Surv <- simplify2array(lapply(1:n, function(i) baseSurv^matrix(exp(eta[i,]), nT, nS, byrow=TRUE)))
return(list(surv = Surv, base = baseSurv))
}
survlist <- surv.calc(model, theta, sx)
mu$S <- survlist
# mu$time
eta <- sx %*% theta[-1,]
if(!is.null(X.test)) {
mm <- attr(sx, "scaled:center")
ss <- attr(sx, "scaled:scale")
sxt <- scale(X.test, center =mm, scale = ss)
test$eta <- sxt %*% theta[-1,]
test$mu <- list()
test$mu$S <- surv.calc(model, theta, sxt)
}
} else if (method == "inla.cens") {
# if(all(x[,1]==1) || all(x[,1]==0)) x <- x[,-1]
# xdf <- as.data.frame(scale(x))
# pred.names <- colnames(xdf)
# colnames(xdf) <- paste0("x",1:ncol(xdf))
intercept1 <- rep(1,n)
# index_df <- matrix(1,nrow=n, ncol=ncol(xdf))
# index_names <- paste0("index_", colnames(xdf))
# colnames(index_df) <- index_names
# df <- cbind(data.frame(time = follow.up, event = fail), xdf, index_df)
#lambda = 0.4712777
# hc <- "expression:
# lambda = 0.01;
# precision = exp(log_precision);
# logdens = -1.5*log_precision-log(pi*lambda)-log(1+1/(precision*lambda^2));
# log_jacobian = log_precision;
# return(logdens+log_jacobian);"
# hcprior <- list(prec = list(prior = hc))
# fs <- paste0("f(", index_names,",", colnames(xdf), ", model = 'iid', hyper = hcprior)")
# survform <- formula(paste(c("inla.surv(time, event) ~ 1 ", fs), collapse = " + "))
df <- cbind(data.frame(time = follow.up, event = fail), intercept1)
survform <- formula(inla.surv(time, event) ~ 0 + intercept1)
# if(is.null(cutpoints)) cutpoints <- NULL
if(is.null(n.intervals)) n.intervals <- 15
cox.call <- inla.coxph(survform, df, control.hazard = list(model = "rw1",
n.intervals = n.intervals,
cutpoints = cutpoints,
constr = TRUE))
# timings <- proc.time()
# model <- inla(survform, family = "coxph",
# data = df, control.fixed=list(mean=m[1], prec=1/(s[1])),
# debug = TRUE, verbose=FALSE)
model.gauss <- inla(cox.call$formula, family = cox.call$family,
data = c(as.list(cox.call$data), cox.call$data.list),
E = cox.call$E,
control.fixed=list(mean=m[1], prec=lambda[1]),
control.inla = list(strategy = "gaussian")
)
model <- inla(cox.call$formula, family = cox.call$family,
data = c(as.list(cox.call$data), cox.call$data.list),
E = cox.call$E,
control.fixed=list(mean=m[1], prec=lambda[1]),
control.inla = list(strategy = "laplace", fast = FALSE),
control.mode = list(result = model.gauss, restart = TRUE),
control.compute=list(config=TRUE))
# print(proc.time() - timings)
samples <- inla.posterior.sample(n = n.samp, result = model, intern = FALSE,
use.improved.mean = TRUE,
add.names = FALSE, seed = -1L, num.threads = 1L)
fe.names <- model$names.fixed
st.names <- rownames(samples[[1]]$latent)
pred.exclude <- !grepl("Predictor", st.names)
bh.exclude <- !grepl("baseline.hazard", st.names)
which.fe <- which(pred.exclude & bh.exclude)
which.not.pred <- which(pred.exclude)
theta <- matrix(sapply(samples, function(ss) ss$latent[which.fe,]), ncol=n.samp)
rownames(theta) <- fe.names
save.samples <- sapply(samples, function(ss) ss$latent[which.not.pred,])
model$samples <- save.samples
surv.calc <- function(model, theta,n) {
intercept <- theta[1,]
# theta_reg <- theta[-1,]
# eta <- x %*% theta_reg
haz.times <- model$.args$data$baseline.hazard.values
# if(is.null(times)) times <- haz.times
log_BH <- model$samples[grep("baseline.hazard", rownames(model$samples)),]
nT <- nrow(log_BH)
nS <- ncol(theta)
log_BH <- log_BH + matrix(intercept, nT, nS, byrow=TRUE)
# cutTimes <- cut(times, haz.times, include.lowest = TRUE)
BH <- exp(log_BH) * diff(c(haz.times, Inf))
# BH_times <- BH
cumHaz <- apply(BH,2,cumsum)
baseSurv <- exp(-cumHaz)
rownames(baseSurv) <- haz.times
Surv <- simplify2array(lapply(1:n, function(i) baseSurv))
# Surv <- baseSurv
return(list(surv = Surv, base = baseSurv))
}
survlist <- surv.calc(model, theta, n)
mu$S <- survlist
# mu$time
# eta <- x %*% theta[-1,]
}
else if (method == "stan-cox") {
sx <- scale(x)
p <- ncol(x)
n <- nrow(x)
stan_dir <- dots$stan_dir
m0 <- dots$m0
# scale_intercept <- dots$scale_intercept
chains <- dots$chains
X.test <- dots$X.test
if(is.null(n.intervals) & is.null(cutpoints)) n.intervals <- 15
if(is.null(m0)) m0 <- round(0.1 * p)
if(m0 < 1) m0 <- round(m0 * p)
# if(is.null(scale_intercept)) scale_intercept <- 2.5
if(is.null(chains)) chains <- 4
# if(any(times) == 0) times <- times[times != 0]
scaled.times <- follow.up#/max(follow.up)
# scaled.times <- times
if(is.null(cutpoints)) {
# cutpoints <- quantile(scaled.times, seq(0,1,length.out = n.intervals))
# cutpoints <- c(0, cutpoints)
cutpoints <- seq(0,max(scaled.times), length.out = n.intervals + 1)
} else {
if(is.null(n.intervals)) n.intervals <- length(cutpoints) - 1
}
if(m0 >= ncol(sx)) {
m0 <- ncol(sx) - 1
warning("Adjusting m0 value. Must be less than number of predictors")
}
time.order <- order(scaled.times)
reverse.time.order <- order(time.order)
stan_dat <- list(
N = as.integer(length(unique(id))),
NT = as.integer(n.intervals),
obs_t = as.double(scaled.times)[time.order],
times = as.double(cutpoints),
fail = as.integer(fail)[time.order],
P = as.integer(ncol(sx)),
X = as.matrix(sx)[time.order,],
m0 = as.double(m0)
)
warmup <- max(n.samp*(2-1/chains), 1000)
iter <- warmup + ceiling(n.samp/chains)
rstan::rstan_options(auto_write = TRUE)
stanModel <- rstan::stan_model(stan_dir)
stanFit <- rstan::sampling(stanModel, data=stan_dat, iter=iter,
warmup = warmup, chains=chains,
pars = c("baseline_S","individ_S","beta","log_hazard","eta","intercept"), #,"dN_out","int_dur"),
control = list(adapt_delta = 0.95,
max_treedepth = 20),
check_data = FALSE)
# sample_file = "cox_stan.csv")
samples <- rstan::extract(stanFit, pars= c("baseline_S","individ_S","log_hazard","beta","eta","intercept"))
theta <- t( samples$beta)
theta <- diag(1/attr(sx, "scaled:scale")) %*% theta
mu <- list(S = list(surv = aperm(samples$individ_S, c(2,1,3)), base = t(samples$baseline_S)))
eta <- t(samples$eta)[reverse.time.order,]
model <- stanFit
# mu$Xtrans <- list(center=attr(sx, "scaled:center"), scale = attr(sx, "scaled:scale"))
surv.calc <- function(baseSurv,x, theta) {
# sx <- scale(x, center = model$Xtrans$center, scale=model$Xtrans$scale)
# baseSurv <- t(rstan::extract(stanFit, pars= c("baseline_S"))$baseline_S)
# theta <- rstan::extract(stanFit, pars= c("beta"))$beta
eta <- x %*% theta
exp_eta <- exp(eta)
nT <- nrow(baseSurv)
nS <- ncol(baseSurv)
Surv <- simplify2array(lapply(1:n, function(i) baseSurv^matrix(exp_eta[i,], nT, nS, byrow=TRUE)))
return(Surv)
}
# if(is.exponential){
# intercept <- t(rowMeans(samples$log_dL0)) - t(attr(sx, "scaled:center")) %*% theta
# theta <- rbind(intercept, theta)
# eta <- t(samples$eta + rowMeans(samples$log_dL0))
# mu$intercept <- intercept
# }
if(is.exponential){
intercept <- t(samples$intercept) - t(attr(sx, "scaled:center")) %*% theta
theta <- rbind(intercept, theta)
eta <- t(samples$eta + c(samples$intercept))
mu$intercept <- intercept
}
if(!is.null(X.test)){
if(all(X.test[,1] == 1)) X.test <- X.test[,-1, drop = FALSE]
if(is.exponential) {
S.test <- surv.calc(mu$S$base, X.test, theta[-1,])
X.test <- cbind(1, X.test)
} else {
S.test <- surv.calc(mu$S$base, X.test, theta)
}
# sxt <- scale(X.test, center = attr(sx, "scaled:center"), scale = attr(sx, "scaled:scale"))
testEta <- X.test %*% theta
test <- list(eta = testEta, mu = list(S = S.test))
}
}
else if (method == "inla-GAM") {
# sx <- scale(x)
xdf <- as.data.frame(x)
pred.names <- colnames(xdf)
colnames(xdf) <- paste0("x",1:ncol(xdf))
intercept1 <- rep(1,n)
# index_df <- matrix(1,nrow=n, ncol=ncol(xdf))
# index_names <- paste0("index_", colnames(xdf))
# colnames(index_df) <- index_names
# df <- cbind(data.frame(time = follow.up, event = fail), xdf, index_df)
#lambda = 0.4712777
# hc <- "expression:
# lambda = 0.01;
# precision = exp(log_precision);
# logdens = -1.5*log_precision-log(pi*lambda)-log(1+1/(precision*lambda^2));
# log_jacobian = log_precision;
# return(logdens+log_jacobian);"
# hcprior <- list(prec = list(prior = hc))
if(is.null(n.intervals)) n.intervals <- 15
time.vars <- paste("time", 1:ncol(xdf), sep=".")
fs <- paste0("f(INLA::inla.group(",time.vars, ", n = ", n.intervals, "), ", colnames(xdf), ", model = 'rw2')")
# survform <- formula(paste(c("inla.surv(time, event) ~ 0 + incercept1 ", fs), collapse = " + "))
df <- cbind(data.frame(time = follow.up, time = sapply(1:ncol(xdf), function(i) follow.up), event = fail), intercept1, xdf)
# survform <- formula(paste(c("inla.surv(time, event) ~ 0 + intercept1", colnames(xdf)), collapse = " + "))
survform <- formula(paste(c("INLA::inla.surv(time, event) ~ -1 + intercept1 ", fs), collapse = " + "))
if(!is.null(X.test)) {
xtdf <- as.data.frame(X.test)
times <- sort(unique(follow.up))
cut.times <- seq(0, max(times), length.out = n.intervals + 1)[-1]
max.time <- max(cut.times)
# test.df <- do.call("rbind", lapply(1:nrow(xtdf),
# function(i) do.call("rbind",
# lapply(cut.times, function(j)
# data.frame(time = j,
# time = t(sapply(1:ncol(xtdf), function(i) j)),
# fail = 1,
# intercept1[1], xtdf[i,,drop=FALSE])))))
test.df <- do.call("rbind", lapply(1:nrow(xtdf),
function(i) #do.call("rbind",
# lapply(cut.times, function(j)
data.frame(time = max.time,
time = t(sapply(1:ncol(xtdf), function(i) max.time)),
fail = 1,
intercept1[1], xtdf[i,,drop=FALSE])))
colnames(test.df) <- colnames(df)
df <- rbind(df, test.df)
}
# if(is.null(cutpoints)) cutpoints <- NULL
cox.call <- INLA::inla.coxph(survform, df, control.hazard = list(model = "rw1",
n.intervals = n.intervals,
cutpoints = cutpoints,
constr = TRUE))
if(!is.null(X.test)) {
idx <- which(!(cox.call$data$expand..coxph %in% c(1:n)))
cox.call$data$y..coxph[idx] <- NA
}
times.cols <- grep("time.[[:digit:]]", colnames(cox.call$data))
cox.call$data[,times.cols] <- matrix(cox.call$data$E..coxph + cox.call$data$baseline.hazard,
nrow= nrow(cox.call$data), ncol = length(times.cols))
# times <- rep(NA, nrow(cox.call$data))
# for(i in 1:n) {
# idx <- which(cox.call$data$expand..coxph == i)
# indiv.time <- cox.call$data$baseline.hazard.time[idx]
# times[idx] <- c(indiv.time[-1], follow.up[i])
# }
# cox.call$data[time.vars] <- sapply(time.vars, function(j) times)
# timings <- proc.time()
# model <- inla(survform, family = "coxph",
# data = df, control.fixed=list(mean=m[1], prec=1/(s[1])),
# debug = TRUE, verbose=FALSE)
model.gauss <- INLA::inla(cox.call$formula, family = cox.call$family,
data = c(as.list(cox.call$data), cox.call$data.list),
E = cox.call$E,
control.fixed=list(mean=m[1], prec=lambda[1]),
control.inla = list(strategy = "gaussian"),
control.predictor = list(link = 1)
)
model <- INLA::inla(cox.call$formula, family = cox.call$family,
data = c(as.list(cox.call$data), cox.call$data.list),
E = cox.call$E,
control.fixed=list(mean=m[1], prec=lambda[1]),
control.inla = list(strategy = "laplace", fast = FALSE),
control.mode = list(result = model.gauss, restart = TRUE),
control.compute=list(config=TRUE),
control.predictor=list(compute=TRUE, link = 1))
# print(proc.time() - timings)
samples <- INLA::inla.posterior.sample(n = n.samp, result = model, intern = FALSE,
use.improved.mean = TRUE,
add.names = TRUE, seed = -1L, num.threads = 1L)
predictor <- sapply(samples, function(s) s$latent[grep("Predictor", rownames(s$latent))])
model$samples <- exp(predictor
+ matrix(log(cox.call$E), nrow(predictor), ncol(predictor))
)
surv.calc <- function(samples, idx, sel.idx) {
baseSurv <- NULL
nS <- ncol(samples)
n <- length(unique(sel.idx))
nT <- nrow(samples)/n
Surv <- simplify2array(lapply(unique(sel.idx), function(i) exp(-apply(samples[idx == i,,drop=FALSE],2,cumsum))))
return(list(surv = Surv, base = baseSurv))
}
survlist <- surv.calc(model$samples, idx = cox.call$data$expand..coxph,
sel.idx = cox.call$data$expand..coxph[cox.call$data$expand..coxph %in% 1:n])
mu$S <- survlist
# mu$time
eta <- NULL #sx %*% theta[-1,]
if(!is.null(X.test)) {
test$mu <- list()
sel.idx <- cox.call$data$expand..coxph[!(cox.call$data$expand..coxph %in% 1:n)]
test$mu$S <- surv.calc(model$samples, cox.call$data$expand..coxph, sel.idx)
}
} else if(method == "survival.pkg-cox") {
warning("This is an MLE method that samples from the asymptotic distribution")
sx <- scale(x)
p <- ncol(x)
n <- nrow(x)
X.test <- dots$X.test
df <- data.frame(follow.up, fail, x)
model <- survival::coxph(formula = survival::Surv(time = follow.up, event = fail) ~ ., data = df, x = TRUE, y = TRUE)
beta <- coef(model)
Sigma <- vcov(model)
theta <- 1/attributes(sx)$`scaled:scale` * t(SLIMpaper::rmvnorm(nsamples = n.samp, mean = beta, covariance = Sigma))
eta <- x %*% theta
surv.calc <- function(baseSurv,x, theta) {
# sx <- scale(x, center = model$Xtrans$center, scale=model$Xtrans$scale)
# baseSurv <- t(rstan::extract(stanFit, pars= c("baseline_S"))$baseline_S)
# theta <- rstan::extract(stanFit, pars= c("beta"))$beta
n <- nrow(x)
eta <- x %*% theta
exp_eta <- exp(eta)
nT <- nrow(baseSurv)
nS <- ncol(baseSurv)
Surv <- simplify2array(lapply(1:n, function(i) baseSurv^matrix(exp_eta[i,], nT, nS, byrow=TRUE)))
return(Surv)
}
sfit <- survival::survfit(model, stype = 2,
conf.type = "log-log",
se.fit = TRUE)
# browser()
baseSurv <- matrix(0., length(sfit$time), n.samp)
model.temp <- model
survfittemp <- sfit
haz <- surv <- NULL
for (i in 1:n.samp) {
model.temp$coefficients <- theta[,i] * attributes(sx)$`scaled:scale`
survfittemp <- survival::survfit(model.temp, stype = 2,
conf.type = "log-log",
se.fit = TRUE)
haz <- log(-log(survfittemp$surv))
draw <- haz + rnorm(length(haz)) * survfittemp$std.err/log(survfittemp$surv)
surv <- exp(-exp(draw))
baseSurv[,i] <-ifelse(is.na(surv) & survfittemp$std.err == 0, survfittemp$surv, surv)
}
#by default will use a matrix centered at means, which should all be at means anyway
# lsurv <- ifelse(sfit$surv > 0, log(sfit$surv), NA)
# gt0 <- !is.na(lsurv)
# baseSurv <- matrix(0., length(sfit$time), n.samp)
# baseSurv[gt0,] <-exp(replicate(n.samp, rnorm(length(sfit$time), mean = lsurv[gt0], sd = sfit$std.err[gt0])))
baseSurv[baseSurv > 1] <- 1
baseSurv[baseSurv < 0] <- 0
surv <- surv.calc(baseSurv, x, theta)
mu <- list(S = list(surv = surv, base = baseSurv))
if(is.exponential){
intercept <- t(samples$intercept) - t(attr(sx, "scaled:center")) %*% theta
theta <- rbind(intercept, theta)
eta <- t(samples$eta + c(samples$intercept))
mu$intercept <- intercept
}
if(!is.null(X.test)){
if(all(X.test[,1] == 1)) X.test <- X.test[,-1, drop = FALSE]
if(is.exponential) {
S.test <- surv.calc(mu$S$base, X.test, theta[-1,])
X.test <- cbind(1, X.test)
} else {
S.test <- surv.calc(mu$S$base, X.test, theta)
}
# sxt <- scale(X.test, center = attr(sx, "scaled:center"), scale = attr(sx, "scaled:scale"))
testEta <- X.test %*% theta
test <- list(eta = testEta, mu = list(S = S.test))
}
}
else if(method == "survival.pkg-km") {
warning("This is an MLE method that samples from the asymptotic distribution")
n <- length(follow.up)
surv.calc <- function(baseSurv,n) {
# sx <- scale(x, center = model$Xtrans$center, scale=model$Xtrans$scale)
# baseSurv <- t(rstan::extract(stanFit, pars= c("baseline_S"))$baseline_S)
# theta <- rstan::extract(stanFit, pars= c("beta"))$beta
nT <- nrow(baseSurv)
nS <- ncol(baseSurv)
Surv <- simplify2array(lapply(1:n, function(i) baseSurv))
return(Surv)
}
sfit <- survival::survfit(survival::Surv(time = follow.up, event = event) ~ 1)
lsurv <- ifelse(sfit$surv > 0 & sfit$surv < 1, log(-log(sfit$surv)), NA)
# gt0 <- !is.na(lsurv)
# baseSurv <- matrix(NA, length(sfit$time), n.samp)
baseSurv <- exp(-exp(replicate(n.samp, rnorm(length(sfit$time), mean = lsurv, sd = sfit$std.err/log(sfit$surv)))))
replace <- is.na(lsurv) & sfit$std.err == 0
baseSurv[replace,] <- matrix(sfit$surv[replace], ncol = n.samp)
baseSurv[baseSurv > 1] <- 1
baseSurv[baseSurv < 0] <- 0
surv <- surv.calc(baseSurv, n)
model <- sfit
mu <- list(S = list(surv = surv, base = baseSurv))
}
return(list(theta=theta, alpha = alpha, mu = mu, eta = eta, test = test, model = model, surv.calc = surv.calc))
}
cindex <- function(times, event, risk = NULL, surv = NULL, surv.times=NULL, cens = NULL,...) {
# ntimes <- length(surv.times)
# if (is.null(risk) & is.null(surv) ) {
# stop("Must provide either survival data or risk data")
# } else if (is.null(surv)) {
#
# pred <- array(risk, dim = c(ntimes, dim(risk)[2:1]))
#
# } else if (is.null(risk)) {
# pred <- 1 - surv
# } else {
# pred <- array(risk, dim = c(ntimes, dim(risk)))
# }
#
# if (is.null(surv.times)) {
# surv.times <- as.numeric(sapply(strsplit(dimnames(surv)[[1]],":"), function(x) x[2]))
# ntimes <- length(surv.times) - 1
# surv.times <- surv.times[-1]
# }
# cut.times <- cut(times, c(0,surv.times), include.lowest = TRUE)
#
# n <- dim(pred)[3]
if (is.null(risk) & is.null(surv) ) {
stop("Must provide either survival data or risk data")
} else if (is.null(surv)) {
pred <- -risk
} else if (is.null(risk)) {
pred <- surv
} else {
pred <- -risk
}
nsamp <- dim(pred)[2]
#
# stopifnot(n == length(event))
#
# # sort times if using rank method
# # ordtime <- order(times)
# # event <- event[ordtime]
# # times <- times[ordtime]
#
# # indicator of event times
# eventbycol <- matrix(event, ncol=n, nrow=n)
# eventbyrow <- matrix(event, ncol=n,nrow=n, byrow = TRUE)
# timeless <- sapply(times, function(tt) as.integer(tt < times))
# timeequal <- sapply(times, function(tt) as.integer(tt == times))
# K <- (timeless + timeequal * eventbycol) * eventbyrow
# # diag(K) <- 0 #don't care for same person
#
# # ranks
# # timgings <- proc.time()
# rank_fun <- function(i, n, surv) {
# ranks <- apply(surv[,i:n, drop=FALSE], 1, rank)
# avg_rank <- colMeans(ranks)
# return(2*(ranks[1,] - avg_rank))
# }
# # U <- D <- matrix(NA, nrow= ntimes , ncol = nsamp)
# # for(tt in seq_along(surv.times)) {
# # timetrue <- times <= surv.times[tt]
# # idx.dead <- which(timetrue & event == 1)
# # idx.time <- which(timetrue)
# #
# # U[tt, ] <- rowSums(sapply(idx.dead, rank_fun, n = n, surv = pred[tt,,]))
# # D[tt, ] <- Reduce("+", sapply(idx.time, function(i) if(i < n) {sum(K[(i+1):n,i])} else {0}))
# # }
# # cstat <- 0.5*(U/D + 1)
# # rowMeans(cstat)
# # print(proc.time() - timgings)
# #risk of predictors over time and over posterior samples
# # risk_array <- sapply(1:n, function(i) K[i,-i] * (as.numeric(pred[,,-i] < pred[,,i]) +
# # as.numeric(pred[,,-i] == pred[,,i])/2))
# # timgings <- proc.time()
# risk_mat <- matrix(NA, nrow=ntimes, ncol=nsamp)
# compare <- lapply(1:n, function(i) matrix(K[-i,i], nrow=nsamp, ncol=n-1, byrow=TRUE) *
# (pred[cut.times[i],,i] > pred[cut.times[i],,-i] + (pred[cut.times[i],,i] == pred[cut.times[i],,-i])/2))
# oldidx <- idx <- newidx <- prev <- NULL
#
# for (tt in seq_along(surv.times)) {
# idx <- which(times <= surv.times[tt] & event == 1)
# newidx <- idx[!(idx %in% oldidx)]
# # not.idx <- which(times > surv.times[tt])
# if(tt>1) {
# prev <- risk_mat[tt-1,]
# } else {
# prev <- 0
# }
# risk_mat[tt,] <- prev + rowSums(Reduce("+", compare[newidx]))
# oldidx <- idx
# }
#
# denoms <- sapply(surv.times, function(ss) sum(K[,times <= ss]))
# cstat <- risk_mat/matrix(denoms, nrow=ntimes, ncol=nsamp)
survobj <- survival::Surv(time = times, event = event)
cstat <- sapply(1:nsamp, function(i) survival::concordancefit(y = survobj, x = pred[,i],
timefix = TRUE)$concordance)
# print(proc.time() - timgings)
output <- list(mean = mean(cstat), low = quantile(cstat, 0.025),
high = quantile(cstat, 0.975), cindex = cstat)
return(output)
}
brier.score <- function(times, event, surv, surv.times, cens_prob, ...) {
# stopifnot(all(dim(event) == dim(probs) ))
readTime <- sort(unique(surv.times))
nst <- length(surv.times)
ntimes <- length(readTime)
# nct <- length(cens.times)
if (nst != ntimes) {
if(dim(surv)[1] != nst) stop("surv.times must match first dimension of surv probs")
# surv <- expandPred(readTime, surv, surv.times)
}
if(dim(cens_prob)[1] != ntimes) {
if(dim(cens_prob)[1] != nst) stop("first dimension of censoring probabilities must match length of surv.times or number of unique times in data")
# cens_prob <- expandPred(readTime, cens_prob, surv.times)
cens.times <- surv.times
} else {
cens.times <- sort(unique(c(0,readTime)))
}
# if (nct != ntimes) {
# cens_prob <- expandPred(readTime, cens, surv.times)
# }
# ot <- order(times)
# times <- times[ot]
# event <- event[ot]
n <- dim(surv)[3]
ncens <- dim(cens_prob)[3]
nsamp <- dim(surv)[2]
eventHappen <- t(sapply(readTime, function(curTime)
as.integer( (times <= curTime) & (event == 1) )))
eventNotHappen <- t(sapply(readTime, function(curTime)
as.integer( times > curTime )))
if(n == 1) {
eventHappen <- t(eventHappen)
eventNotHappen <- t(eventNotHappen)
}
# cens_mat <- vector("list", ncens)
bs <- matrix(0, nrow=ntimes, ncol=nsamp)
if(readTime[1] != 0) {
cutTime <- c(0, readTime)
} else {
cutTime <- readTime
}
time.idx <- as.numeric(cut(times, cutTime, include.lowest = TRUE))
cens_prob_at_event <- matrix(NA, nrow = nsamp, ncol=n)
if(any(is.na(cens_prob))) {
repl_idx <- which(is.na(cens_prob),arr.ind=TRUE)
cens_prob[repl_idx] <- min(cens_prob, na.rm=TRUE)
}
if(any(cens_prob <= .Machine$double.xmin)) {
repl_idx <- which(cens_prob <= .Machine$double.xmin, arr.ind=TRUE)
cens_prob[repl_idx] <- Inf
}
for(i in 1:n) cens_prob_at_event[,i] <- cens_prob[time.idx[i],,i]
# for ( curTime in readTime ) {
# idx_time <- which(readTime == curTime)
# for(s in 1:nsamp){
# for(i in 1:n) {
# bs[idx_time, s] <- bs[idx_time, s] + (0 - surv[idx_time,s,i])^2 *eventHappen[idx_time, i]/cens_prob[time.idx[i],s,i]/n +
# (1 - surv[idx_time,s,i])^2 * eventNotHappen[idx_time,i]/cens_prob[idx_time,s,i]/n
# }
# }
# # bs[idx_time, ] <- bs[idx_time, ]
# }
# lcpe <- log(cens_prob_at_event)
# lcp <- log(cens_prob)
# l_eventHappen <- log(eventHappen)
# l_eventNotHappen <- log(eventNotHappen)
l_normalize <- log_uwt1 <- log_uwt2 <- eh <- enh <- lterm1 <- lterm2 <- lsum1 <- lsum2 <-
surv_at_time <- cnes_at_time <- normalize <- NULL
# ttt <- proc.time()
for ( curTime in readTime ) {
idx_time <- which(readTime == curTime)
# if(idx_time == 399) browser()
eh <- matrix(eventHappen[idx_time,], nrow = nsamp, ncol = n, byrow = TRUE)
enh <- matrix(eventNotHappen[idx_time,], nrow = nsamp, ncol = n, byrow=TRUE)
# l_eh <- matrix(l_eventHappen[idx_time,], nrow = nsamp, ncol = n, byrow = TRUE)
# l_enh <- matrix(l_eventNotHappen[idx_time,], nrow = nsamp, ncol = n, byrow = TRUE)
# log_uwt1 <- apply(l_eh - lcpe, 1, log_sum_exp)
# log_uwt2 <- apply(l_enh - lcp[idx_time,,], 1, log_sum_exp)
# l_normalize <- log_sum_exp2(log_uwt1, log_uwt2)
# lterm1 <- 2 * log(abs(0 - surv[idx_time,,])) + l_eh - lcpe - l_normalize
# lterm2 <- 2 * log(1 - surv[idx_time,,]) + l_enh - lcp[idx_time,,] - l_normalize
# lsum1 <- apply(lterm1, 1, log_sum_exp)
# lsum2 <- apply(lterm2, 1, log_sum_exp)
# bs[idx_time, ] <- exp(log_sum_exp2(lsum1, lsum2))
surv_at_time <- predAtTime(curTime, surv, surv.times)
cens_at_time <- predAtTime(curTime, cens_prob, cens.times)
normalize <- rowSums(eh / cens_prob_at_event + enh / cens_at_time)
normalize <- ifelse(normalize == 0, Inf, normalize)
bs[idx_time, ] <- rowSums(((0 - surv_at_time)^2 * eh / cens_prob_at_event +
( 1 - surv_at_time )^2 * enh / cens_at_time)/ normalize )
# for (samp in nsamp) {
# for(cc in 1:ncens) {
# cens_mat[[cc]] <- ((0 - surv[,samp,] )^2 /cens_prob[,cc,] * eventHappen +
# ( 1 - surv[,samp,] )^2 /cens_prob[idx_time,cc,] * eventNotHappen)/ncens
# }
# int_cens <- Reduce("+", cens_mat)
# bs[idx_time, samp] <- mean( int_cens )
# }
}
# print(proc.time() - ttt)
# idx <- 2:ntimes
# intbs <- diff(readTime) %*% ( ( bs[idx -1,] + bs[idx,]) / 2 )
# intbs <- intbs/diff(range(readTime))
intbs <- (diff(c(0,readTime)) %*% bs)/max(readTime)
# browser()
output <- list(
brier.score = list(
mean = rowMeans(bs),
low = apply(bs, 1, quantile, 0.025),
high = apply(bs,1, quantile, 0.975),
median = apply(bs,1, median),
bscore = bs
),
int.BS = list(
mean = mean(intbs),
low = quantile(intbs, 0.025),
high = quantile(intbs, 0.975),
median = median(intbs),
intBS = c(intbs))
)
return(output)
}
evalfit <- function(times, event, risk = NULL, surv = NULL, cens=NULL, surv.times= NULL, method = c("c-index","brier")) {
meth <- match.arg(method)
efun <- switch(meth, "c-index" = cindex,
"brier" = brier.score)
stat <- efun(times = times, event = event, risk=risk, surv=surv, surv.times=surv.times, cens = cens)
return(stat)
}
predAtTime <- function(time, pred, predTimes) {
dict <- cut(time, predTimes, include.lowest = TRUE)
idx <- as.numeric(dict)
predTime <- pred[idx,,]
return(predTime)
}
expandPred <- function(times, pred, predTimes) {
dims <- if (length(dim(pred)) > 1) {
dim(pred)
} else {
length(pred)
}
stopifnot(dims[1] == length(predTimes))
st <- sort(unique(times))
dict <- cut(st, predTimes, include.lowest = TRUE)
idx <- as.numeric(dict)
predExp <- pred[idx,,]
return(predExp)
}
mf.cox <- function(x,theta) {
#get params
baseline <- theta$base
param <- theta$param
exp_eta <- exp(x %*% param)
# dims
n <- nrow(x)
nT <- nrow(baseline)
nS <- ncol(baseline)
#calc survival
surv <- do.call("rbind", lapply(1:nT, function(i) matrix(baseline[i,], nrow = n, ncol = nS, byrow = TRUE)^exp_eta))
return(surv)
}
mf.bart <- function(x,theta) {
if(!is.data.frame(x)) x <- as.data.frame(x)
stopifnot(is.data.frame(x))
model <- theta
preds <- predict(model, newdata=x)
return(preds)
}
mf.linpred <- function(x, theta) {
return(x %*% theta)
}
sel.pred.fun <- function(method) {
mf <- switch(method, "cox" = mf.cox,
"linpred" = mf.linpred,
"bart" = mf.bart
)
return(mf)
}
return(list(rprior=rprior,
rdata = rdata,
rpost = rpost,
X = list(rX = rX, corr = NULL),
data_gen_function = NULL,
rparam = rparam,
link = Gamma(link = log)$linkfun,
invlink = Gamma(link = log)$linkinv,
sel.pred.fun = sel.pred.fun,
evalfit = evalfit,
expandPred = expandPred))
}
ericdunipace/SLIMpaper documentation built on May 5, 2024, 3:43 p.m.
R Package Documentation
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Defines functions get_survival_linear_model
get_survival_linear_model <- function() {
#### Prior Function ####
rprior_coef <- function(n, mu, sigma){
return(rmvnorm(n, mu, sigma))
}
rprior_sigma <- function(n, alpha, beta){
return(NULL)
}
rprior<- function(n, hyperparameters) {
stopifnot(is.list(hyperparameters))
stopifnot(length(hyperparameters) >= 2)
stopifnot(all(c("mu","sigma") %in% names(hyperparameters) ))
stopifnot(length(hyperparameters$mu) == ncol(as.matrix(hyperparameters$sigma)))
return(list(theta = rprior_coef(n, hyperparameters$mu, hyperparameters$sigma),
sigma2 = NULL))
}
#### X Data ####
# trajectory <- function(nT, cov, p, ARM) {
# sds <- sqrt(diag(cov))
# corr <- diag(1/sds) %*% cov %*% diag(1/sds)
# x <- rmvnorm(nT, rep(0, p), corr)
# for(i in 2:nT) x[i,] <- x[i-1,] * ARM + x[i,]
#
# return(x)
# }
# rX <- function(n, corr, p,...)
# {
# dots <- list(...)
# nT <- dots$nT
# if(is.null(nT)) nT <- 1
# ARM <- dots$ARM
# if(is.null(ARM)) ARM <- rnorm(p-1)
# if (is.matrix(corr)) {
# x_cov <- corr
# }
# else {
# x_cov <- matrix(corr, nrow = p - 1, ncol = p - 1)
# }
# diag(x_cov) <- 1
# x_list <- lapply(1:n, function(i) trajectory(nT, x_cov, p-1, ARM))
# x <- matrix(NA, nrow=n*nT, ncol=p-1)
# for(i in 1:n) x[(nT*(i-1)+1):(nT*i),] <- x_list[[i]]
# X <- cbind(1,x)
# return(X)
# }
rX <- gen_x()$rX
#### Y Data ####
rdata <- function(n, x, theta, ...) {
dots <- list(...)
# id <- dots$id
corr.x <- dots$corr
scale <- dots$scale
if(is.null(corr.x)) corr.x <- 0
if(is.null(scale)) scale <- TRUE
if(ncol(x) > length(theta)) {
x <- x[, 1: length(theta), drop=FALSE]
warning("Ncol X > length(theta). Only using first length(theta) columns of X.")
}
is.intercept <- all(x[,1] == 1)
if(is.intercept) {
intercept <- theta[1]
theta <- theta[-1]
x <- x[,-1, drop=FALSE]
} else {
intercept <- 0
}
p <- ncol(x)
corr.mat <- corr_mat_construct(corr.x, p)
diag(corr.mat) <- 1
theta_norm <- c(t(theta) %*% corr.mat %*% theta)
theta_scaled <- if(scale) {
theta/sqrt(theta_norm)
} else {
theta
}
log.rate <- x %*% theta_scaled + intercept
mu <- exp(log.rate)
Y <- rexp(n, mu)
return(list(Y= Y, mu = mu, eta = log.rate, link = Gamma(link = log)$linkfun, invlink = Gamma(link = log)$linkinv, param = theta))
}
#### Parameters ####
rparam <- get_param()$exponential
#### Posterior on Coefficients
rpost_coef <- function(x){NULL}
rpost_sigma <- function(x){NULL}
rpost <- function(n.samp, x, y, hyperparameters=list(),...) { #uses RJAGS
# require(rjags)
dots <- list(...)
id <- dots$id
follow.up <- y
fail <- dots$fail
if (is.matrix(follow.up)) {
if(ncol(follow.up) == 2 & is.null(fail)) {
fail <- follow.up[,2]
follow.up <- follow.up[,1]
} else if (ncol(follow.up) == 2 & !is.null(fail) ){
stop("if fail is null, then follow.up must be a two column matrix with the first column the failure times and the second column the event indicator. Do not specify both fail and make follow.up a matrix")
}
}
jags_dir <- dots$jags_dir
thin <- dots$thin
model <- dots$model
nchain <- dots$nchain
X.test <- dots$X.test
method <- dots$method
seed <- dots$seed
parallel <- dots$parallel
cutpoints <- dots$cutpoints
n.intervals <- dots$n.intervals
is.exponential <- dots$is.exponential
if(is.null(is.exponential)) is.exponential <- FALSE
if(is.null(method)) method <- "ss-jags"
test <- list(eta = NULL, mu = NULL)
if(is.null(thin)) thin <- 1
if(all(x[,1]==1) || all(x[,1]==0)) x <- x[,-1]
if(is.null(nchain)) nchain <- 1
if(is.null(id)) id <- 1:length(y)
# a <- hyperparameters$alpha
# b <- hyperparameters$beta
m <- hyperparameters$mu
s <- hyperparameters$sigma
prior_frac <- hyperparameters$prior_frac
spike_a <- hyperparameters$spike_a
spike_b <- hyperparameters$spike_b
if(is.null(spike_a)) spike_a <- 1
if(is.null(spike_b)) spike_b <- 1
lambda <- 1/s
# n <- length(y)
# get_x <- function(x, id, follow.up, time) {
# X <- matrix(NA, nrow=length(id) * length(follow.up), ncol=ncol(x))
# id_unique <- unique(id)
# for(i in seq_along(id_unique)){
# fup <- follow.up[i]
# current_id <- id == id_unique[i]
# curr_x <- x[current_id ,]
#
# }
# }
# if(any(follow.up > dots$time)) follow.up[follow.up > dots$time] <- max(dots$time)
times <- sort(c(0,unique(follow.up)))
mu <- eta <- alpha <- theta <- NULL
n <- length(id)
# obs.time <- tapply(dots$time, id, max)
obs.time <- follow.up
if (is.null(fail )) {
fail <- rep(1, length(unique(id))) # ifelse(obs.time < 5, 1, 0)
}
surv.calc <- function(eta, fit) {
n <- nrow(eta)
max.t <- max(fit$Surv[, 1], na.rm = T)
last.d <- nrow(fit$d.scaled)
d.scaled <- fit$d.scaled
d.scaled[last.d,] <- max.t
length.int <- apply(d.scaled, 2, diff)
H <- apply(length.int * fit$h.scaled[-1,], 2, cumsum)
mu <- lapply(1:n, function(i) exp(-H * exp(eta[i,])))
for(i in 1:n) rownames(mu[[i]]) <- paste0("(",format(d.scaled[1:(last.d-1),1]),", ", format(d.scaled[2:last.d,1]),"]")
return(mu)
}
if(!is.null(parallel)) {
if( parallel ) {
parallel.MPI <- TRUE
ncpu <- parallel::detectCores()-1
} else {
parallel.MPI <- FALSE
ncpu <- 1
}
} else {
parallel.MPI <- FALSE
ncpu <- 1
}
if(!is.null(seed)) set.seed(seed)
if(method == "ss-jags") {
mu_vec <- rep(m, ncol(x))
prec_matrix <- matrix(0, ncol(x), ncol(x))
diag(prec_matrix) <- lambda
jags_data <-
list(
N = length(unique(id)),
T = length(times)-1,
P = ncol(x),
obs.time = obs.time,
time = times,
eps = 0.0001,
X = x,
fail = fail,
mu_0 = as.double(m),
prec_0 = as.double(lambda),
prior_frac = prior_frac,
spike_a = spike_a,
spike_b = spike_b,
mu_vec = mu_vec,
prec_matrix = prec_matrix
)
if (is.null(model)) {
model <- rjags::jags.model(file=jags_dir,data = jags_data, n.chains = nchain, n.adapt = n.samp*thin*.1)
} else {
model$recompile()
}
total_iter <- n.samp*thin + ceiling(n.samp*thin/nchain)
samples <- rjags::jags.samples(model,
variable.names = c("beta","alpha","prob","baseHaz"),
n.iter = total_iter, thin = thin)
remove_burnin.idx <- round(seq(nsamp+1, total_iter, length.out = nsamp))
final_nsamp <- length(remove_burnin.idx)
# nsamp_portion <- floor(final_nsamp/nchain)
# adjust_remove.idx <- remove_burnin.idx[((nsamp - nsamp_portion + 1):nsamp)]
adjust_remove.idx <- remove_burnin.idx[((nsamp - final_nsamp + 1):nsamp)]
theta_samp <- samples$beta[ , adjust_remove.idx, ]
alpha_samp <- samples$alpha[, adjust_remove.idx, ]
if(nchain > 1) {
theta <- matrix(NA, ncol=final_nsamp, nrow = dim(theta_samp)[1])
alpha <- matrix(NA, ncol=final_nsamp, nrow = dim(alpha_samp)[1])
for(i in 1:nchain){
get_rows <- (i-1)*nsamp_portion + (1:nsamp_portion)
theta[,get_rows] <- theta_samp[,,i]
alpha[,get_rows] <- alpha_samp[,,i]
}
} else {
theta <- theta_samp
alpha <- alpha_samp
}
eta <- cbind(1,x) %*% theta
if(!is.null(X.test)){
if(all(X.test[,1]==1) || all(X.test[,1]==0)) X.test <- cbind(1,X.test)
test$eta <- X.test %*% theta
}
} else if(method == "jags") {
mu_vec <- rep(m, ncol(x))
prec_matrix <- matrix(0, ncol(x), ncol(x))
diag(prec_matrix) <- lambda
jags_data <-
list(
N = length(unique(id)),
T = length(times)-1,
P = ncol(x),
obs.time = obs.time,
time = times,
eps = 0.0001,
X = x,
fail = fail,
mu_0 = as.double(m),
prec_0 = as.double(lambda),
prior_frac = prior_frac,
spike_a = spike_a,
spike_b = spike_b,
mu_vec = mu_vec,
prec_matrix = prec_matrix
)
if (is.null(model)) {
model <- rjags::jags.model(file=jags_dir,data = jags_data, n.chains = nchain, n.adapt = n.samp*thin*.1)
} else {
model$recompile()
}
total_iter <- n.samp*thin + ceiling(n.samp*thin/nchain)
samples <- rjags::jags.samples(model,
variable.names = c("beta", "baseHaz"),
n.iter = total_iter, thin = thin)
saved_iter <- n.samp + ceiling(n.samp / nchain)
nsamp_portion <- ceiling(n.samp/nchain)
remove_burnin.idx <- round(seq(nsamp+1, saved_iter, length.out = nsamp_portion))
# final_nsamp <- length(remove_burnin.idx)
# adjust_remove.idx <- remove_burnin.idx[((nsamp - nsamp_portion + 1):nsamp)]
theta_samp <- samples$beta[ , remove_burnin.idx, ]
# alpha_samp <- samples$alpha[, adjust_remove.idx, ]
if(nchain > 1) {
theta <- matrix(NA, ncol=nsamp, nrow = dim(theta_samp)[1])
# alpha <- matrix(NA, nrow=final_nsamp, ncol = dim(alpha_samp)[1])
for(i in 1:nchain){
get_rows <- (i-1)*nsamp_portion + (1:nsamp_portion)
theta[,get_rows] <- theta_samp[,,i]
# alpha[get_rows,] <- t(alpha_samp[,,i])
}
} else {
theta <- theta_samp
# alpha <- t(alpha_samp)
}
if(nrow(theta) == (ncol(x) + 1)){
X.prod <- cbind(1,x)
} else{
X.prod <- x
}
eta <- X.prod %*% theta
if(!is.null(X.test)){
if(ncol(theta) == (ncol(X.test) + 1)) X.test <- cbind(1,X.test)
test$eta <- X.test %*% theta
}
} else if (method == "bvs-cox") {
# require(BVSNLP)
# select median prob model first so don't have to run all 13000+ covariates in bayesian regression
cat("Selecting median probability model (MPM)")
resp <- cbind(follow.up, fail)
xdf <- as.data.frame(log(x))
sel <- BVSNLP::bvs(X = xdf, resp = resp, family = "survival",
niter = n.samp*10, prep = TRUE, mod_prior = "unif", logT = FALSE,
inseed = sample.int(.Machine$integer.max,1), ncpu = ncpu, parallel.MPI = parallel.MPI)
mpm <- sel$MPM
if(length(mpm)==0) {
mpm <- sort(unique(unlist(sel$max_models[sel$max_prob_vec > (log(0.01) + sel$max_prob)])))
}
# eta <- BVSNLP::predBMA(fit, X=df, resp = resp, prep=TRUE, logT=FALSE, family = "survival")
# eta <- fit$des_mat %*% fit$beta_hat
# alpha <- fit$inc_probs
#
# test$eta <- X.test[,colnames(fit$des_mat)] %*% fit$beta_hat
survform <- formula(survival::Surv(time = follow.up, event = fail) ~ .)
# x_sc <- scale(log(x))
# xt_sc <- NULL
# if(!is.null(X.test)) {
# xt_sc <- scale(log(X.test), center = attr(x_sc,"scaled:center"), scale = attr(x_sc, "scaled:scale"))
# }
x_sc <- xdf[,mpm, drop=FALSE]
df <- as.data.frame(cbind(follow.up, fail, x_sc))
colnames(df) <- c("follow.up","fail", colnames(x_sc))
pred <- list(xpred = x_sc)
if(!is.null(X.test)) {
xt_sc <- scale(log(X.test[,mpm,drop=FALSE]), center = colMeans(x_sc), scale = colSD(x_sc))
pred <- list(xpred = rbind(scale(x_sc), xt_sc))
}
cat("Running Bayesian cox on MPM")
fit <- spBayesSurv::indeptCoxph(survform, data = df, prediction = pred,
mcmc = list(nburn = n.samp, nsave = n.samp, nskip = 0, ndisplay = 100),
prior = NULL, state = NULL, scale.designX = TRUE)
eta <- fit$X.scaled %*% fit$beta.scaled
mu <- list(time = NULL, S = NULL)
mu$time <- fit$Tpred[1:n,,drop=FALSE]
mu$S <- surv.calc(eta, fit)
if(!is.null(X.test)) {
test$eta <- xt_sc %*% fit$beta.scaled
test$mu <- list(time = NULL, S = NULL)
test$mu$time <- fit$Tpred[(n+1):nrow(fit$Tpred),,drop=FALSE]
test$mu$S <- surv.calc(test$eta, fit)
}
model <- fit
} else if (method == "cox") {
x_sc <- scale(x) #log(x)
df <- as.data.frame(cbind(follow.up, fail, x_sc))
colnames(df) <- c("follow.up","fail", colnames(x_sc))
pred <- list(xpred = x_sc)
if(!is.null(X.test)) {
xt_sc <- scale(X.test[,mpm,drop=FALSE], center = colMeans(x_sc), scale = colSD(x_sc))
pred <- list(xpred = rbind(scale(x_sc), xt_sc))
}
survform <- formula(survival::Surv(time = follow.up, event = fail) ~ .)
fit <- spBayesSurv::indeptCoxph(survform, data = df, prediction = pred,
mcmc = list(nburn = n.samp*thin, nsave = n.samp, nskip = thin,
ndisplay = n.samp/10),
prior = NULL, state = NULL, scale.designX = TRUE)
eta <- fit$X.scaled %*% fit$beta.scaled
theta <- fit$beta.scaled
mu <- list(time = NULL, S = NULL)
mu$time <- fit$Tpred[1:n,,drop=FALSE]
mu$S <- surv.calc(eta, fit)
if(!is.null(X.test)) {
test$eta <- xt_sc %*% fit$beta.scaled
test$mu <- list(time = NULL, S = NULL)
test$mu$time <- fit$Tpred[(n+1):nrow(fit$Tpred),,drop=FALSE]
test$mu$S <- surv.calc(test$eta, fit)
}
model <- fit
} else if (method == "bvs") {
theta <- NULL
alpha <- NULL
mu <- NULL
eta <- NULL
# require(BVSNLP)
# require(doParallel)
# require(foreach)
resp <- cbind(follow.up, fail)
xdf <- as.data.frame(log(x))
model <- bvsmod(X = xdf, resp = resp, family = "survival",
niter = n.samp, prep = TRUE, mod_prior = "unif", logT = FALSE,
inseed = sample.int(.Machine$integer.max, 1), ncpu = ncpu, parallel.MPI = parallel.MPI)
} else if (method == "inla") {
# if(all(x[,1]==1) || all(x[,1]==0)) x <- x[,-1]
sx <- scale(x)
xdf <- as.data.frame(sx)
pred.names <- colnames(xdf)
colnames(xdf) <- paste0("x",1:ncol(xdf))
intercept1 <- rep(1,n)
# index_df <- matrix(1,nrow=n, ncol=ncol(xdf))
# index_names <- paste0("index_", colnames(xdf))
# colnames(index_df) <- index_names
# df <- cbind(data.frame(time = follow.up, event = fail), xdf, index_df)
#lambda = 0.4712777
# hc <- "expression:
# lambda = 0.01;
# precision = exp(log_precision);
# logdens = -1.5*log_precision-log(pi*lambda)-log(1+1/(precision*lambda^2));
# log_jacobian = log_precision;
# return(logdens+log_jacobian);"
# hcprior <- list(prec = list(prior = hc))
# fs <- paste0("f(", index_names,",", colnames(xdf), ", model = 'iid', hyper = hcprior)")
# survform <- formula(paste(c("inla.surv(time, event) ~ 1 ", fs), collapse = " + "))
df <- cbind(data.frame(time = follow.up, event = fail), intercept1, xdf)
survform <- formula(paste(c("inla.surv(time, event) ~ 0 + intercept1", colnames(xdf)), collapse = " + "))
# if(is.null(cutpoints)) cutpoints <- NULL
if(is.null(n.intervals)) n.intervals <- 15
cox.call <- inla.coxph(survform, df, control.hazard = list(model = "rw1",
n.intervals = n.intervals,
cutpoints = cutpoints,
constr = TRUE))
# timings <- proc.time()
# model <- inla(survform, family = "coxph",
# data = df, control.fixed=list(mean=m[1], prec=1/(s[1])),
# debug = TRUE, verbose=FALSE)
model.gauss <- inla(cox.call$formula, family = cox.call$family,
data = c(as.list(cox.call$data), cox.call$data.list),
E = cox.call$E,
control.fixed=list(mean=m[1], prec=lambda[1]),
control.inla = list(strategy = "gaussian")
)
model <- inla(cox.call$formula, family = cox.call$family,
data = c(as.list(cox.call$data), cox.call$data.list),
E = cox.call$E,
control.fixed=list(mean=m[1], prec=lambda[1]),
control.inla = list(strategy = "laplace", fast = FALSE),
control.mode = list(result = model.gauss, restart = TRUE),
control.compute=list(config=TRUE))
# print(proc.time() - timings)
samples <- inla.posterior.sample(n = n.samp, result = model, intern = FALSE,
use.improved.mean = TRUE,
add.names = FALSE, seed = -1L, num.threads = 1L)
fe.names <- model$names.fixed
st.names <- rownames(samples[[1]]$latent)
pred.exclude <- !grepl("Predictor", st.names)
bh.exclude <- !grepl("baseline.hazard", st.names)
which.fe <- which(pred.exclude & bh.exclude)
which.not.pred <- which(pred.exclude)
theta <- matrix(sapply(samples, function(ss) ss$latent[which.fe,]), ncol=n.samp)
rownames(theta) <- fe.names
save.samples <- sapply(samples, function(ss) ss$latent[which.not.pred,])
model$samples <- save.samples
surv.calc <- function(model, theta, x) {
n <- nrow(x)
intercept <- theta[1,]
theta_reg <- theta[-1,]
eta <- x %*% theta_reg
haz.times <- model$.args$data$baseline.hazard.values
# if(is.null(times)) times <- haz.times
log_BH <- model$samples[grep("baseline.hazard", rownames(model$samples)),]
nT <- nrow(log_BH)
nS <- ncol(theta)
log_BH <- log_BH + matrix(intercept, nT, nS, byrow=TRUE)
# cutTimes <- cut(times, haz.times, include.lowest = TRUE)
BH <- exp(log_BH) * diff(c(haz.times, Inf))
# BH_times <- BH
cumHaz <- apply(BH,2,cumsum)
baseSurv <- exp(-cumHaz)
rownames(baseSurv) <- haz.times
Surv <- simplify2array(lapply(1:n, function(i) baseSurv^matrix(exp(eta[i,]), nT, nS, byrow=TRUE)))
return(list(surv = Surv, base = baseSurv))
}
survlist <- surv.calc(model, theta, sx)
mu$S <- survlist
# mu$time
eta <- sx %*% theta[-1,]
if(!is.null(X.test)) {
mm <- attr(sx, "scaled:center")
ss <- attr(sx, "scaled:scale")
sxt <- scale(X.test, center =mm, scale = ss)
test$eta <- sxt %*% theta[-1,]
test$mu <- list()
test$mu$S <- surv.calc(model, theta, sxt)
}
} else if (method == "inla.cens") {
# if(all(x[,1]==1) || all(x[,1]==0)) x <- x[,-1]
# xdf <- as.data.frame(scale(x))
# pred.names <- colnames(xdf)
# colnames(xdf) <- paste0("x",1:ncol(xdf))
intercept1 <- rep(1,n)
# index_df <- matrix(1,nrow=n, ncol=ncol(xdf))
# index_names <- paste0("index_", colnames(xdf))
# colnames(index_df) <- index_names
# df <- cbind(data.frame(time = follow.up, event = fail), xdf, index_df)
#lambda = 0.4712777
# hc <- "expression:
# lambda = 0.01;
# precision = exp(log_precision);
# logdens = -1.5*log_precision-log(pi*lambda)-log(1+1/(precision*lambda^2));
# log_jacobian = log_precision;
# return(logdens+log_jacobian);"
# hcprior <- list(prec = list(prior = hc))
# fs <- paste0("f(", index_names,",", colnames(xdf), ", model = 'iid', hyper = hcprior)")
# survform <- formula(paste(c("inla.surv(time, event) ~ 1 ", fs), collapse = " + "))
df <- cbind(data.frame(time = follow.up, event = fail), intercept1)
survform <- formula(inla.surv(time, event) ~ 0 + intercept1)
# if(is.null(cutpoints)) cutpoints <- NULL
if(is.null(n.intervals)) n.intervals <- 15
cox.call <- inla.coxph(survform, df, control.hazard = list(model = "rw1",
n.intervals = n.intervals,
cutpoints = cutpoints,
constr = TRUE))
# timings <- proc.time()
# model <- inla(survform, family = "coxph",
# data = df, control.fixed=list(mean=m[1], prec=1/(s[1])),
# debug = TRUE, verbose=FALSE)
model.gauss <- inla(cox.call$formula, family = cox.call$family,
data = c(as.list(cox.call$data), cox.call$data.list),
E = cox.call$E,
control.fixed=list(mean=m[1], prec=lambda[1]),
control.inla = list(strategy = "gaussian")
)
model <- inla(cox.call$formula, family = cox.call$family,
data = c(as.list(cox.call$data), cox.call$data.list),
E = cox.call$E,
control.fixed=list(mean=m[1], prec=lambda[1]),
control.inla = list(strategy = "laplace", fast = FALSE),
control.mode = list(result = model.gauss, restart = TRUE),
control.compute=list(config=TRUE))
# print(proc.time() - timings)
samples <- inla.posterior.sample(n = n.samp, result = model, intern = FALSE,
use.improved.mean = TRUE,
add.names = FALSE, seed = -1L, num.threads = 1L)
fe.names <- model$names.fixed
st.names <- rownames(samples[[1]]$latent)
pred.exclude <- !grepl("Predictor", st.names)
bh.exclude <- !grepl("baseline.hazard", st.names)
which.fe <- which(pred.exclude & bh.exclude)
which.not.pred <- which(pred.exclude)
theta <- matrix(sapply(samples, function(ss) ss$latent[which.fe,]), ncol=n.samp)
rownames(theta) <- fe.names
save.samples <- sapply(samples, function(ss) ss$latent[which.not.pred,])
model$samples <- save.samples
surv.calc <- function(model, theta,n) {
intercept <- theta[1,]
# theta_reg <- theta[-1,]
# eta <- x %*% theta_reg
haz.times <- model$.args$data$baseline.hazard.values
# if(is.null(times)) times <- haz.times
log_BH <- model$samples[grep("baseline.hazard", rownames(model$samples)),]
nT <- nrow(log_BH)
nS <- ncol(theta)
log_BH <- log_BH + matrix(intercept, nT, nS, byrow=TRUE)
# cutTimes <- cut(times, haz.times, include.lowest = TRUE)
BH <- exp(log_BH) * diff(c(haz.times, Inf))
# BH_times <- BH
cumHaz <- apply(BH,2,cumsum)
baseSurv <- exp(-cumHaz)
rownames(baseSurv) <- haz.times
Surv <- simplify2array(lapply(1:n, function(i) baseSurv))
# Surv <- baseSurv
return(list(surv = Surv, base = baseSurv))
}
survlist <- surv.calc(model, theta, n)
mu$S <- survlist
# mu$time
# eta <- x %*% theta[-1,]
}
else if (method == "stan-cox") {
sx <- scale(x)
p <- ncol(x)
n <- nrow(x)
stan_dir <- dots$stan_dir
m0 <- dots$m0
# scale_intercept <- dots$scale_intercept
chains <- dots$chains
X.test <- dots$X.test
if(is.null(n.intervals) & is.null(cutpoints)) n.intervals <- 15
if(is.null(m0)) m0 <- round(0.1 * p)
if(m0 < 1) m0 <- round(m0 * p)
# if(is.null(scale_intercept)) scale_intercept <- 2.5
if(is.null(chains)) chains <- 4
# if(any(times) == 0) times <- times[times != 0]
scaled.times <- follow.up#/max(follow.up)
# scaled.times <- times
if(is.null(cutpoints)) {
# cutpoints <- quantile(scaled.times, seq(0,1,length.out = n.intervals))
# cutpoints <- c(0, cutpoints)
cutpoints <- seq(0,max(scaled.times), length.out = n.intervals + 1)
} else {
if(is.null(n.intervals)) n.intervals <- length(cutpoints) - 1
}
if(m0 >= ncol(sx)) {
m0 <- ncol(sx) - 1
warning("Adjusting m0 value. Must be less than number of predictors")
}
time.order <- order(scaled.times)
reverse.time.order <- order(time.order)
stan_dat <- list(
N = as.integer(length(unique(id))),
NT = as.integer(n.intervals),
obs_t = as.double(scaled.times)[time.order],
times = as.double(cutpoints),
fail = as.integer(fail)[time.order],
P = as.integer(ncol(sx)),
X = as.matrix(sx)[time.order,],
m0 = as.double(m0)
)
warmup <- max(n.samp*(2-1/chains), 1000)
iter <- warmup + ceiling(n.samp/chains)
rstan::rstan_options(auto_write = TRUE)
stanModel <- rstan::stan_model(stan_dir)
stanFit <- rstan::sampling(stanModel, data=stan_dat, iter=iter,
warmup = warmup, chains=chains,
pars = c("baseline_S","individ_S","beta","log_hazard","eta","intercept"), #,"dN_out","int_dur"),
control = list(adapt_delta = 0.95,
max_treedepth = 20),
check_data = FALSE)
# sample_file = "cox_stan.csv")
samples <- rstan::extract(stanFit, pars= c("baseline_S","individ_S","log_hazard","beta","eta","intercept"))
theta <- t( samples$beta)
theta <- diag(1/attr(sx, "scaled:scale")) %*% theta
mu <- list(S = list(surv = aperm(samples$individ_S, c(2,1,3)), base = t(samples$baseline_S)))
eta <- t(samples$eta)[reverse.time.order,]
model <- stanFit
# mu$Xtrans <- list(center=attr(sx, "scaled:center"), scale = attr(sx, "scaled:scale"))
surv.calc <- function(baseSurv,x, theta) {
# sx <- scale(x, center = model$Xtrans$center, scale=model$Xtrans$scale)
# baseSurv <- t(rstan::extract(stanFit, pars= c("baseline_S"))$baseline_S)
# theta <- rstan::extract(stanFit, pars= c("beta"))$beta
eta <- x %*% theta
exp_eta <- exp(eta)
nT <- nrow(baseSurv)
nS <- ncol(baseSurv)
Surv <- simplify2array(lapply(1:n, function(i) baseSurv^matrix(exp_eta[i,], nT, nS, byrow=TRUE)))
return(Surv)
}
# if(is.exponential){
# intercept <- t(rowMeans(samples$log_dL0)) - t(attr(sx, "scaled:center")) %*% theta
# theta <- rbind(intercept, theta)
# eta <- t(samples$eta + rowMeans(samples$log_dL0))
# mu$intercept <- intercept
# }
if(is.exponential){
intercept <- t(samples$intercept) - t(attr(sx, "scaled:center")) %*% theta
theta <- rbind(intercept, theta)
eta <- t(samples$eta + c(samples$intercept))
mu$intercept <- intercept
}
if(!is.null(X.test)){
if(all(X.test[,1] == 1)) X.test <- X.test[,-1, drop = FALSE]
if(is.exponential) {
S.test <- surv.calc(mu$S$base, X.test, theta[-1,])
X.test <- cbind(1, X.test)
} else {
S.test <- surv.calc(mu$S$base, X.test, theta)
}
# sxt <- scale(X.test, center = attr(sx, "scaled:center"), scale = attr(sx, "scaled:scale"))
testEta <- X.test %*% theta
test <- list(eta = testEta, mu = list(S = S.test))
}
}
else if (method == "inla-GAM") {
# sx <- scale(x)
xdf <- as.data.frame(x)
pred.names <- colnames(xdf)
colnames(xdf) <- paste0("x",1:ncol(xdf))
intercept1 <- rep(1,n)
# index_df <- matrix(1,nrow=n, ncol=ncol(xdf))
# index_names <- paste0("index_", colnames(xdf))
# colnames(index_df) <- index_names
# df <- cbind(data.frame(time = follow.up, event = fail), xdf, index_df)
#lambda = 0.4712777
# hc <- "expression:
# lambda = 0.01;
# precision = exp(log_precision);
# logdens = -1.5*log_precision-log(pi*lambda)-log(1+1/(precision*lambda^2));
# log_jacobian = log_precision;
# return(logdens+log_jacobian);"
# hcprior <- list(prec = list(prior = hc))
if(is.null(n.intervals)) n.intervals <- 15
time.vars <- paste("time", 1:ncol(xdf), sep=".")
fs <- paste0("f(INLA::inla.group(",time.vars, ", n = ", n.intervals, "), ", colnames(xdf), ", model = 'rw2')")
# survform <- formula(paste(c("inla.surv(time, event) ~ 0 + incercept1 ", fs), collapse = " + "))
df <- cbind(data.frame(time = follow.up, time = sapply(1:ncol(xdf), function(i) follow.up), event = fail), intercept1, xdf)
# survform <- formula(paste(c("inla.surv(time, event) ~ 0 + intercept1", colnames(xdf)), collapse = " + "))
survform <- formula(paste(c("INLA::inla.surv(time, event) ~ -1 + intercept1 ", fs), collapse = " + "))
if(!is.null(X.test)) {
xtdf <- as.data.frame(X.test)
times <- sort(unique(follow.up))
cut.times <- seq(0, max(times), length.out = n.intervals + 1)[-1]
max.time <- max(cut.times)
# test.df <- do.call("rbind", lapply(1:nrow(xtdf),
# function(i) do.call("rbind",
# lapply(cut.times, function(j)
# data.frame(time = j,
# time = t(sapply(1:ncol(xtdf), function(i) j)),
# fail = 1,
# intercept1[1], xtdf[i,,drop=FALSE])))))
test.df <- do.call("rbind", lapply(1:nrow(xtdf),
function(i) #do.call("rbind",
# lapply(cut.times, function(j)
data.frame(time = max.time,
time = t(sapply(1:ncol(xtdf), function(i) max.time)),
fail = 1,
intercept1[1], xtdf[i,,drop=FALSE])))
colnames(test.df) <- colnames(df)
df <- rbind(df, test.df)
}
# if(is.null(cutpoints)) cutpoints <- NULL
cox.call <- INLA::inla.coxph(survform, df, control.hazard = list(model = "rw1",
n.intervals = n.intervals,
cutpoints = cutpoints,
constr = TRUE))
if(!is.null(X.test)) {
idx <- which(!(cox.call$data$expand..coxph %in% c(1:n)))
cox.call$data$y..coxph[idx] <- NA
}
times.cols <- grep("time.[[:digit:]]", colnames(cox.call$data))
cox.call$data[,times.cols] <- matrix(cox.call$data$E..coxph + cox.call$data$baseline.hazard,
nrow= nrow(cox.call$data), ncol = length(times.cols))
# times <- rep(NA, nrow(cox.call$data))
# for(i in 1:n) {
# idx <- which(cox.call$data$expand..coxph == i)
# indiv.time <- cox.call$data$baseline.hazard.time[idx]
# times[idx] <- c(indiv.time[-1], follow.up[i])
# }
# cox.call$data[time.vars] <- sapply(time.vars, function(j) times)
# timings <- proc.time()
# model <- inla(survform, family = "coxph",
# data = df, control.fixed=list(mean=m[1], prec=1/(s[1])),
# debug = TRUE, verbose=FALSE)
model.gauss <- INLA::inla(cox.call$formula, family = cox.call$family,
data = c(as.list(cox.call$data), cox.call$data.list),
E = cox.call$E,
control.fixed=list(mean=m[1], prec=lambda[1]),
control.inla = list(strategy = "gaussian"),
control.predictor = list(link = 1)
)
model <- INLA::inla(cox.call$formula, family = cox.call$family,
data = c(as.list(cox.call$data), cox.call$data.list),
E = cox.call$E,
control.fixed=list(mean=m[1], prec=lambda[1]),
control.inla = list(strategy = "laplace", fast = FALSE),
control.mode = list(result = model.gauss, restart = TRUE),
control.compute=list(config=TRUE),
control.predictor=list(compute=TRUE, link = 1))
# print(proc.time() - timings)
samples <- INLA::inla.posterior.sample(n = n.samp, result = model, intern = FALSE,
use.improved.mean = TRUE,
add.names = TRUE, seed = -1L, num.threads = 1L)
predictor <- sapply(samples, function(s) s$latent[grep("Predictor", rownames(s$latent))])
model$samples <- exp(predictor
+ matrix(log(cox.call$E), nrow(predictor), ncol(predictor))
)
surv.calc <- function(samples, idx, sel.idx) {
baseSurv <- NULL
nS <- ncol(samples)
n <- length(unique(sel.idx))
nT <- nrow(samples)/n
Surv <- simplify2array(lapply(unique(sel.idx), function(i) exp(-apply(samples[idx == i,,drop=FALSE],2,cumsum))))
return(list(surv = Surv, base = baseSurv))
}
survlist <- surv.calc(model$samples, idx = cox.call$data$expand..coxph,
sel.idx = cox.call$data$expand..coxph[cox.call$data$expand..coxph %in% 1:n])
mu$S <- survlist
# mu$time
eta <- NULL #sx %*% theta[-1,]
if(!is.null(X.test)) {
test$mu <- list()
sel.idx <- cox.call$data$expand..coxph[!(cox.call$data$expand..coxph %in% 1:n)]
test$mu$S <- surv.calc(model$samples, cox.call$data$expand..coxph, sel.idx)
}
} else if(method == "survival.pkg-cox") {
warning("This is an MLE method that samples from the asymptotic distribution")
sx <- scale(x)
p <- ncol(x)
n <- nrow(x)
X.test <- dots$X.test
df <- data.frame(follow.up, fail, x)
model <- survival::coxph(formula = survival::Surv(time = follow.up, event = fail) ~ ., data = df, x = TRUE, y = TRUE)
beta <- coef(model)
Sigma <- vcov(model)
theta <- 1/attributes(sx)$`scaled:scale` * t(SLIMpaper::rmvnorm(nsamples = n.samp, mean = beta, covariance = Sigma))
eta <- x %*% theta
surv.calc <- function(baseSurv,x, theta) {
# sx <- scale(x, center = model$Xtrans$center, scale=model$Xtrans$scale)
# baseSurv <- t(rstan::extract(stanFit, pars= c("baseline_S"))$baseline_S)
# theta <- rstan::extract(stanFit, pars= c("beta"))$beta
n <- nrow(x)
eta <- x %*% theta
exp_eta <- exp(eta)
nT <- nrow(baseSurv)
nS <- ncol(baseSurv)
Surv <- simplify2array(lapply(1:n, function(i) baseSurv^matrix(exp_eta[i,], nT, nS, byrow=TRUE)))
return(Surv)
}
sfit <- survival::survfit(model, stype = 2,
conf.type = "log-log",
se.fit = TRUE)
# browser()
baseSurv <- matrix(0., length(sfit$time), n.samp)
model.temp <- model
survfittemp <- sfit
haz <- surv <- NULL
for (i in 1:n.samp) {
model.temp$coefficients <- theta[,i] * attributes(sx)$`scaled:scale`
survfittemp <- survival::survfit(model.temp, stype = 2,
conf.type = "log-log",
se.fit = TRUE)
haz <- log(-log(survfittemp$surv))
draw <- haz + rnorm(length(haz)) * survfittemp$std.err/log(survfittemp$surv)
surv <- exp(-exp(draw))
baseSurv[,i] <-ifelse(is.na(surv) & survfittemp$std.err == 0, survfittemp$surv, surv)
}
#by default will use a matrix centered at means, which should all be at means anyway
# lsurv <- ifelse(sfit$surv > 0, log(sfit$surv), NA)
# gt0 <- !is.na(lsurv)
# baseSurv <- matrix(0., length(sfit$time), n.samp)
# baseSurv[gt0,] <-exp(replicate(n.samp, rnorm(length(sfit$time), mean = lsurv[gt0], sd = sfit$std.err[gt0])))
baseSurv[baseSurv > 1] <- 1
baseSurv[baseSurv < 0] <- 0
surv <- surv.calc(baseSurv, x, theta)
mu <- list(S = list(surv = surv, base = baseSurv))
if(is.exponential){
intercept <- t(samples$intercept) - t(attr(sx, "scaled:center")) %*% theta
theta <- rbind(intercept, theta)
eta <- t(samples$eta + c(samples$intercept))
mu$intercept <- intercept
}
if(!is.null(X.test)){
if(all(X.test[,1] == 1)) X.test <- X.test[,-1, drop = FALSE]
if(is.exponential) {
S.test <- surv.calc(mu$S$base, X.test, theta[-1,])
X.test <- cbind(1, X.test)
} else {
S.test <- surv.calc(mu$S$base, X.test, theta)
}
# sxt <- scale(X.test, center = attr(sx, "scaled:center"), scale = attr(sx, "scaled:scale"))
testEta <- X.test %*% theta
test <- list(eta = testEta, mu = list(S = S.test))
}
}
else if(method == "survival.pkg-km") {
warning("This is an MLE method that samples from the asymptotic distribution")
n <- length(follow.up)
surv.calc <- function(baseSurv,n) {
# sx <- scale(x, center = model$Xtrans$center, scale=model$Xtrans$scale)
# baseSurv <- t(rstan::extract(stanFit, pars= c("baseline_S"))$baseline_S)
# theta <- rstan::extract(stanFit, pars= c("beta"))$beta
nT <- nrow(baseSurv)
nS <- ncol(baseSurv)
Surv <- simplify2array(lapply(1:n, function(i) baseSurv))
return(Surv)
}
sfit <- survival::survfit(survival::Surv(time = follow.up, event = event) ~ 1)
lsurv <- ifelse(sfit$surv > 0 & sfit$surv < 1, log(-log(sfit$surv)), NA)
# gt0 <- !is.na(lsurv)
# baseSurv <- matrix(NA, length(sfit$time), n.samp)
baseSurv <- exp(-exp(replicate(n.samp, rnorm(length(sfit$time), mean = lsurv, sd = sfit$std.err/log(sfit$surv)))))
replace <- is.na(lsurv) & sfit$std.err == 0
baseSurv[replace,] <- matrix(sfit$surv[replace], ncol = n.samp)
baseSurv[baseSurv > 1] <- 1
baseSurv[baseSurv < 0] <- 0
surv <- surv.calc(baseSurv, n)
model <- sfit
mu <- list(S = list(surv = surv, base = baseSurv))
}
return(list(theta=theta, alpha = alpha, mu = mu, eta = eta, test = test, model = model, surv.calc = surv.calc))
}
cindex <- function(times, event, risk = NULL, surv = NULL, surv.times=NULL, cens = NULL,...) {
# ntimes <- length(surv.times)
# if (is.null(risk) & is.null(surv) ) {
# stop("Must provide either survival data or risk data")
# } else if (is.null(surv)) {
#
# pred <- array(risk, dim = c(ntimes, dim(risk)[2:1]))
#
# } else if (is.null(risk)) {
# pred <- 1 - surv
# } else {
# pred <- array(risk, dim = c(ntimes, dim(risk)))
# }
#
# if (is.null(surv.times)) {
# surv.times <- as.numeric(sapply(strsplit(dimnames(surv)[[1]],":"), function(x) x[2]))
# ntimes <- length(surv.times) - 1
# surv.times <- surv.times[-1]
# }
# cut.times <- cut(times, c(0,surv.times), include.lowest = TRUE)
#
# n <- dim(pred)[3]
if (is.null(risk) & is.null(surv) ) {
stop("Must provide either survival data or risk data")
} else if (is.null(surv)) {
pred <- -risk
} else if (is.null(risk)) {
pred <- surv
} else {
pred <- -risk
}
nsamp <- dim(pred)[2]
#
# stopifnot(n == length(event))
#
# # sort times if using rank method
# # ordtime <- order(times)
# # event <- event[ordtime]
# # times <- times[ordtime]
#
# # indicator of event times
# eventbycol <- matrix(event, ncol=n, nrow=n)
# eventbyrow <- matrix(event, ncol=n,nrow=n, byrow = TRUE)
# timeless <- sapply(times, function(tt) as.integer(tt < times))
# timeequal <- sapply(times, function(tt) as.integer(tt == times))
# K <- (timeless + timeequal * eventbycol) * eventbyrow
# # diag(K) <- 0 #don't care for same person
#
# # ranks
# # timgings <- proc.time()
# rank_fun <- function(i, n, surv) {
# ranks <- apply(surv[,i:n, drop=FALSE], 1, rank)
# avg_rank <- colMeans(ranks)
# return(2*(ranks[1,] - avg_rank))
# }
# # U <- D <- matrix(NA, nrow= ntimes , ncol = nsamp)
# # for(tt in seq_along(surv.times)) {
# # timetrue <- times <= surv.times[tt]
# # idx.dead <- which(timetrue & event == 1)
# # idx.time <- which(timetrue)
# #
# # U[tt, ] <- rowSums(sapply(idx.dead, rank_fun, n = n, surv = pred[tt,,]))
# # D[tt, ] <- Reduce("+", sapply(idx.time, function(i) if(i < n) {sum(K[(i+1):n,i])} else {0}))
# # }
# # cstat <- 0.5*(U/D + 1)
# # rowMeans(cstat)
# # print(proc.time() - timgings)
# #risk of predictors over time and over posterior samples
# # risk_array <- sapply(1:n, function(i) K[i,-i] * (as.numeric(pred[,,-i] < pred[,,i]) +
# # as.numeric(pred[,,-i] == pred[,,i])/2))
# # timgings <- proc.time()
# risk_mat <- matrix(NA, nrow=ntimes, ncol=nsamp)
# compare <- lapply(1:n, function(i) matrix(K[-i,i], nrow=nsamp, ncol=n-1, byrow=TRUE) *
# (pred[cut.times[i],,i] > pred[cut.times[i],,-i] + (pred[cut.times[i],,i] == pred[cut.times[i],,-i])/2))
# oldidx <- idx <- newidx <- prev <- NULL
#
# for (tt in seq_along(surv.times)) {
# idx <- which(times <= surv.times[tt] & event == 1)
# newidx <- idx[!(idx %in% oldidx)]
# # not.idx <- which(times > surv.times[tt])
# if(tt>1) {
# prev <- risk_mat[tt-1,]
# } else {
# prev <- 0
# }
# risk_mat[tt,] <- prev + rowSums(Reduce("+", compare[newidx]))
# oldidx <- idx
# }
#
# denoms <- sapply(surv.times, function(ss) sum(K[,times <= ss]))
# cstat <- risk_mat/matrix(denoms, nrow=ntimes, ncol=nsamp)
survobj <- survival::Surv(time = times, event = event)
cstat <- sapply(1:nsamp, function(i) survival::concordancefit(y = survobj, x = pred[,i],
timefix = TRUE)$concordance)
# print(proc.time() - timgings)
output <- list(mean = mean(cstat), low = quantile(cstat, 0.025),
high = quantile(cstat, 0.975), cindex = cstat)
return(output)
}
brier.score <- function(times, event, surv, surv.times, cens_prob, ...) {
# stopifnot(all(dim(event) == dim(probs) ))
readTime <- sort(unique(surv.times))
nst <- length(surv.times)
ntimes <- length(readTime)
# nct <- length(cens.times)
if (nst != ntimes) {
if(dim(surv)[1] != nst) stop("surv.times must match first dimension of surv probs")
# surv <- expandPred(readTime, surv, surv.times)
}
if(dim(cens_prob)[1] != ntimes) {
if(dim(cens_prob)[1] != nst) stop("first dimension of censoring probabilities must match length of surv.times or number of unique times in data")
# cens_prob <- expandPred(readTime, cens_prob, surv.times)
cens.times <- surv.times
} else {
cens.times <- sort(unique(c(0,readTime)))
}
# if (nct != ntimes) {
# cens_prob <- expandPred(readTime, cens, surv.times)
# }
# ot <- order(times)
# times <- times[ot]
# event <- event[ot]
n <- dim(surv)[3]
ncens <- dim(cens_prob)[3]
nsamp <- dim(surv)[2]
eventHappen <- t(sapply(readTime, function(curTime)
as.integer( (times <= curTime) & (event == 1) )))
eventNotHappen <- t(sapply(readTime, function(curTime)
as.integer( times > curTime )))
if(n == 1) {
eventHappen <- t(eventHappen)
eventNotHappen <- t(eventNotHappen)
}
# cens_mat <- vector("list", ncens)
bs <- matrix(0, nrow=ntimes, ncol=nsamp)
if(readTime[1] != 0) {
cutTime <- c(0, readTime)
} else {
cutTime <- readTime
}
time.idx <- as.numeric(cut(times, cutTime, include.lowest = TRUE))
cens_prob_at_event <- matrix(NA, nrow = nsamp, ncol=n)
if(any(is.na(cens_prob))) {
repl_idx <- which(is.na(cens_prob),arr.ind=TRUE)
cens_prob[repl_idx] <- min(cens_prob, na.rm=TRUE)
}
if(any(cens_prob <= .Machine$double.xmin)) {
repl_idx <- which(cens_prob <= .Machine$double.xmin, arr.ind=TRUE)
cens_prob[repl_idx] <- Inf
}
for(i in 1:n) cens_prob_at_event[,i] <- cens_prob[time.idx[i],,i]
# for ( curTime in readTime ) {
# idx_time <- which(readTime == curTime)
# for(s in 1:nsamp){
# for(i in 1:n) {
# bs[idx_time, s] <- bs[idx_time, s] + (0 - surv[idx_time,s,i])^2 *eventHappen[idx_time, i]/cens_prob[time.idx[i],s,i]/n +
# (1 - surv[idx_time,s,i])^2 * eventNotHappen[idx_time,i]/cens_prob[idx_time,s,i]/n
# }
# }
# # bs[idx_time, ] <- bs[idx_time, ]
# }
# lcpe <- log(cens_prob_at_event)
# lcp <- log(cens_prob)
# l_eventHappen <- log(eventHappen)
# l_eventNotHappen <- log(eventNotHappen)
l_normalize <- log_uwt1 <- log_uwt2 <- eh <- enh <- lterm1 <- lterm2 <- lsum1 <- lsum2 <-
surv_at_time <- cnes_at_time <- normalize <- NULL
# ttt <- proc.time()
for ( curTime in readTime ) {
idx_time <- which(readTime == curTime)
# if(idx_time == 399) browser()
eh <- matrix(eventHappen[idx_time,], nrow = nsamp, ncol = n, byrow = TRUE)
enh <- matrix(eventNotHappen[idx_time,], nrow = nsamp, ncol = n, byrow=TRUE)
# l_eh <- matrix(l_eventHappen[idx_time,], nrow = nsamp, ncol = n, byrow = TRUE)
# l_enh <- matrix(l_eventNotHappen[idx_time,], nrow = nsamp, ncol = n, byrow = TRUE)
# log_uwt1 <- apply(l_eh - lcpe, 1, log_sum_exp)
# log_uwt2 <- apply(l_enh - lcp[idx_time,,], 1, log_sum_exp)
# l_normalize <- log_sum_exp2(log_uwt1, log_uwt2)
# lterm1 <- 2 * log(abs(0 - surv[idx_time,,])) + l_eh - lcpe - l_normalize
# lterm2 <- 2 * log(1 - surv[idx_time,,]) + l_enh - lcp[idx_time,,] - l_normalize
# lsum1 <- apply(lterm1, 1, log_sum_exp)
# lsum2 <- apply(lterm2, 1, log_sum_exp)
# bs[idx_time, ] <- exp(log_sum_exp2(lsum1, lsum2))
surv_at_time <- predAtTime(curTime, surv, surv.times)
cens_at_time <- predAtTime(curTime, cens_prob, cens.times)
normalize <- rowSums(eh / cens_prob_at_event + enh / cens_at_time)
normalize <- ifelse(normalize == 0, Inf, normalize)
bs[idx_time, ] <- rowSums(((0 - surv_at_time)^2 * eh / cens_prob_at_event +
( 1 - surv_at_time )^2 * enh / cens_at_time)/ normalize )
# for (samp in nsamp) {
# for(cc in 1:ncens) {
# cens_mat[[cc]] <- ((0 - surv[,samp,] )^2 /cens_prob[,cc,] * eventHappen +
# ( 1 - surv[,samp,] )^2 /cens_prob[idx_time,cc,] * eventNotHappen)/ncens
# }
# int_cens <- Reduce("+", cens_mat)
# bs[idx_time, samp] <- mean( int_cens )
# }
}
# print(proc.time() - ttt)
# idx <- 2:ntimes
# intbs <- diff(readTime) %*% ( ( bs[idx -1,] + bs[idx,]) / 2 )
# intbs <- intbs/diff(range(readTime))
intbs <- (diff(c(0,readTime)) %*% bs)/max(readTime)
# browser()
output <- list(
brier.score = list(
mean = rowMeans(bs),
low = apply(bs, 1, quantile, 0.025),
high = apply(bs,1, quantile, 0.975),
median = apply(bs,1, median),
bscore = bs
),
int.BS = list(
mean = mean(intbs),
low = quantile(intbs, 0.025),
high = quantile(intbs, 0.975),
median = median(intbs),
intBS = c(intbs))
)
return(output)
}
evalfit <- function(times, event, risk = NULL, surv = NULL, cens=NULL, surv.times= NULL, method = c("c-index","brier")) {
meth <- match.arg(method)
efun <- switch(meth, "c-index" = cindex,
"brier" = brier.score)
stat <- efun(times = times, event = event, risk=risk, surv=surv, surv.times=surv.times, cens = cens)
return(stat)
}
predAtTime <- function(time, pred, predTimes) {
dict <- cut(time, predTimes, include.lowest = TRUE)
idx <- as.numeric(dict)
predTime <- pred[idx,,]
return(predTime)
}
expandPred <- function(times, pred, predTimes) {
dims <- if (length(dim(pred)) > 1) {
dim(pred)
} else {
length(pred)
}
stopifnot(dims[1] == length(predTimes))
st <- sort(unique(times))
dict <- cut(st, predTimes, include.lowest = TRUE)
idx <- as.numeric(dict)
predExp <- pred[idx,,]
return(predExp)
}
mf.cox <- function(x,theta) {
#get params
baseline <- theta$base
param <- theta$param
exp_eta <- exp(x %*% param)
# dims
n <- nrow(x)
nT <- nrow(baseline)
nS <- ncol(baseline)
#calc survival
surv <- do.call("rbind", lapply(1:nT, function(i) matrix(baseline[i,], nrow = n, ncol = nS, byrow = TRUE)^exp_eta))
return(surv)
}
mf.bart <- function(x,theta) {
if(!is.data.frame(x)) x <- as.data.frame(x)
stopifnot(is.data.frame(x))
model <- theta
preds <- predict(model, newdata=x)
return(preds)
}
mf.linpred <- function(x, theta) {
return(x %*% theta)
}
sel.pred.fun <- function(method) {
mf <- switch(method, "cox" = mf.cox,
"linpred" = mf.linpred,
"bart" = mf.bart
)
return(mf)
}
return(list(rprior=rprior,
rdata = rdata,
rpost = rpost,
X = list(rX = rX, corr = NULL),
data_gen_function = NULL,
rparam = rparam,
link = Gamma(link = log)$linkfun,
invlink = Gamma(link = log)$linkinv,
sel.pred.fun = sel.pred.fun,
evalfit = evalfit,
expandPred = expandPred))
}
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