Nothing
R/mexhazStd.R
In mexhaz: Mixed Effect Excess Hazard Models
Defines functions
mexhazStd
mexhazStd <- function(formula,data,expected=NULL,base="weibull",degree=3,knots=NULL,bound=NULL,n.gleg=20,init=NULL,random=NULL,n.aghq=10,fnoptim="nlm",verbose=0,method="Nelder-Mead",iterlim=10000,numHess=FALSE,print.level=1,gradtol=1e-6,mode="fit",...){
time0 <- as.numeric(proc.time()[3])
FALCenv <- environment()
call <- match.call()
mf <- match.call(expand.dots=FALSE)
m <- match(c("formula","data"),names(mf),0L)
if (m[1]==0){
stop("The 'formula' argument is required...")
}
if (m[2]==0){
stop("The 'data' argument is required...")
}
name.data <- paste(substitute(data),sep="")
if (length(name.data)>1){
name.data <- name.data[2]
}
if (base=="exp.bs" & !degree%in%c(1:3)){
stop("This function can only be used to estimate log-hazards described by B-splines of degree 1 to 3...")
}
## Function that controls what is printed during the optimisation procedure
if (verbose>0){
verbose.ll <- function(){
if (!((iv <- parent.neval/verbose)-floor(iv))){
time1 <- as.numeric(proc.time()[3])
print(data.frame(Eval=parent.neval,LogLik=-parent.logLik,Time=time1-time0,row.names=""))
cat("Param\n")
print(round(parent.param,4))
cat("\n")
}
}
}
else {
verbose.ll <- function(){}
}
## Functions for computing the part of the B-spline bases that depends only on the knots and can therefore be calculated only once at the beginning of the function (used in combination with the IntHazard function to estimate the hazard and the cumulative hazard)
## For B-splines (also used for Natural Cubic Splines)
TransfDeg <- function(vec.knots,deg){
if (deg==1){
Le <- length(vec.knots)
Res <- vec.knots[2:Le]-vec.knots[1:(Le-1)]
}
else {
Dim <- (length(vec.knots)-(2*deg-1))
Res <- matrix(NA,2*(deg-1),Dim)
if (deg==2){
for (i in 1:Dim){
TempK <- vec.knots[i:(i+3)]
Res[1,i] <- 1/((TempK[4]-TempK[2])*(TempK[3]-TempK[2]))
Res[2,i] <- 1/((TempK[3]-TempK[1])*(TempK[3]-TempK[2]))
}
}
else if (deg==3){
for (i in 1:Dim){
TempK <- vec.knots[i:(i+5)]
Res[1,i] <- 1/((TempK[6]-TempK[3])*(TempK[5]-TempK[3])*(TempK[4]-TempK[3]))
Res[2,i] <- 1/((TempK[5]-TempK[2])*(TempK[4]-TempK[2])*(TempK[4]-TempK[3]))
Res[3,i] <- 1/((TempK[5]-TempK[2])*(TempK[5]-TempK[3])*(TempK[4]-TempK[3]))
Res[4,i] <- 1/((TempK[4]-TempK[1])*(TempK[4]-TempK[2])*(TempK[4]-TempK[3]))
}
}
}
return(Res)
}
## For Natural Cubic Splines
if (base=="exp.bs"){
NsAdjust <- function(vec.knots,BoI,BoS){
NULL
}
dbase <- degree
}
else if (base=="exp.ns"){
NsAdjust <- function(vec.knots,BoI,BoS){
Dim <- (length(vec.knots)-5)
Diag <- diag(Dim)
QR <- qr(t(splineDesign(vec.knots,c(BoI,BoS),4,c(2,2))[,-1,drop=FALSE]))
Res1 <- t(apply(Diag,1,function(x){qr.qty(QR,x)})[-c(1:2),,drop=FALSE])
SpI <- c(BoI,splineDesign(vec.knots,rep(BoI,2L),4,c(0,1))[2,1:2])
SpS <- c(BoS,splineDesign(vec.knots,rep(BoS,2L),4,c(0,1))[2,(Dim:(Dim+1))])
Res2 <- cbind(SpI,SpS)
return(list(Res1,Res2))
}
degree <- 3
dbase <- 1
}
## Creating the points and weights of Gauss-Legendre quadrature (used when base is "exp.bs" and degree in c(2:3) or when base is "exp.ns")
if (n.gleg<=0 | round(n.gleg,0)!=n.gleg){
stop("The 'n.gleg' argument must be a strictly positive integer.")
}
gl <- gauss.quad(n=n.gleg,kind="legendre")
gln <- gl$nodes
lglw <- log(gl$weights)
## Data preparation
mf <- mf[c(1L,m)]
mf$drop.unused.levels <- TRUE
mf$na.action <- "na.pass"
mf[[1L]] <- quote(stats::model.frame)
tot.formula <- terms(formula,data=data,specials="nph")
indNph <- attr(tot.formula,"specials")$nph
if (length(indNph)>0){
nTerm <- NULL
nTerm2 <- NULL
for (i in 1:length(indNph)){
nphterm <- attr(tot.formula,"variables")[[1+indNph[i]]]
nTerm <- c(nTerm,deparse(nphterm[[2L]],width.cutoff=500L,backtick=TRUE))
nTerm2 <- c(nTerm2,deparse(nphterm,width.cutoff=500L,backtick=TRUE))
}
nTerm <- paste(nTerm,collapse="+")
nTerm2 <- paste(nTerm2,collapse="-")
FormulaN <- as.formula(paste("~",nTerm))
}
else {
nTerm2 <- 0
FormulaN <- as.formula("~1")
}
mf$formula <- update(tot.formula,paste(".~.",nTerm2,sep="-"))
FormulaF <- update(tot.formula,paste("NULL~.",nTerm2,sep="-"))
Xlevel.formula <- terms(FormulaF,data=data)
m <- eval(mf,parent.frame())
if (nrow(m)==0){
stop("No non-missing observations...")
}
Y <- model.extract(m,"response")
if (!inherits(Y,"Surv")){
stop("Response must be a Surv() object...")
}
Survtype <- attr(Y, "type")
if (ncol(Y)==2){
if (Survtype!="right"){
stop(paste("mexhaz does not support \"", Survtype, "\" type of censoring with (0, time] survival data",
sep = ""))
}
time.obs <- Y[,1,drop=FALSE] # Follow-up time
status.obs <- Y[,2] # Status variable
if (base=="pw.cst"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs0R,x,nph,timecat,fixobs,param,paramf,as.double(matk))
}
}
else if (base=="exp.bs" & degree==1){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs1R,x,nph,timecat,fixobs,param,paramf,as.double(matk),as.double(totk))
}
}
else if (base=="exp.bs" & degree%in%c(2,3)){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs23R,x,nph,timecat,fixobs,param,paramf,deg,n,lw,as.double(matk),as.double(totk))
}
}
else if (base=="exp.ns"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardNsR,x,nph,timecat,fixobs,param,paramf,deg,n,lw,as.double(matk),as.double(totk),as.double(intk),as.double(nsadj1),as.double(nsadj2))
}
}
else if (base=="weibull"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardWeibR,x,nph,fixobs,param,paramf)
}
}
}
else if (ncol(Y)==3){
if (Survtype!="counting"){
stop(paste("mexhaz does not support \"", Survtype, "\" type of censoring with (time, time2] survival data",
sep = ""))
}
time.obs <- Y[,1:2] # Entry time / Follow-up time
status.obs <- Y[,3] # Status variable
if (is.null(random)){
if (base=="pw.cst"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs0C,x0,x,nph,timecat0,timecat,fixobs,param,paramf,as.double(matk))
}
}
else if (base=="exp.bs" & degree==1){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs1C,x0,x,nph,timecat0,timecat,fixobs,param,paramf,as.double(matk),as.double(totk))
}
}
else if (base=="exp.bs" & degree%in%c(2,3)){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs23C,x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,as.double(matk),as.double(totk))
}
}
else if (base=="exp.ns"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardNsC,x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,as.double(matk),as.double(totk),as.double(intk),as.double(nsadj1),as.double(nsadj2))
}
}
else if (base=="weibull"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardWeibC,x0,x,nph,fixobs,param,paramf)
}
}
}
else {
if (base=="pw.cst"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs0L,x0,x,nph,timecat0,timecat,fixobs,param,paramf,as.double(matk))
}
}
else if (base=="exp.bs" & degree==1){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs1L,x0,x,nph,timecat0,timecat,fixobs,param,paramf,as.double(matk),as.double(totk))
}
}
else if (base=="exp.bs" & degree%in%c(2,3)){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs23L,x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,as.double(matk),as.double(totk))
}
}
else if (base=="exp.ns"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardNsL,x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,as.double(matk),as.double(totk),as.double(intk),as.double(nsadj1),as.double(nsadj2))
}
}
else if (base=="weibull"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardWeibL,x0,x,nph,fixobs,param,paramf)
}
}
}
}
if (Survtype=="counting" & !is.null(random)){
Frailty.Adapt <- function(nodes, nodessquare, logweights, clust, clustd, expect, betal, betaL0, betaL, A0, A, var, mh0, muhatcond){
obj <- .Call(C_FrailtyAdaptL, nodes, nodessquare, logweights, clust, clustd, expect, betal, betaL0, betaL, A0, A, var, mh0, muhatcond)
}
}
else {
Frailty.Adapt <- function(nodes, nodessquare, logweights, clust, clustd, expect, betal, betaL0, betaL, A0, A, var, mh0, muhatcond){
obj <- .Call(C_FrailtyAdapt, nodes, nodessquare, logweights, clust, clustd, expect, betal, betaL, A, var, muhatcond)
}
}
data.fix <- model.frame(FormulaF,data=data,na.action=na.pass)
data.nph <- model.frame(FormulaN,data=data,na.action=na.pass)
n.obs.tot <- dim(data)[1] # Total number of observations
## Expected hazard (a vector of 0 is created if NULL)
if (!is.null(expected)){
lambda.pop <- data[,expected]
if (min(lambda.pop,na.rm=TRUE)<0){
stop("The expected hazard for some observations is negative!")
}
withExp <- 1
}
else {
lambda.pop <- rep(0,n.obs.tot)
withExp <- 0
}
## Remove observations containing missing values and perform several formatting operations on the data
if (!is.null(random)){
random.obs <- data[,random]
Idx.NA.Rdm <- which(is.na(random.obs))
if (length(Idx.NA.Rdm)>0){
warning("Cluster information was missing for some observations. These observations were consequently removed...")
random.obs <- random.obs[-Idx.NA.Rdm]
time.obs <- time.obs[-Idx.NA.Rdm,,drop=FALSE]
status.obs <- status.obs[-Idx.NA.Rdm]
lambda.pop <- lambda.pop[-Idx.NA.Rdm]
data.fix <- data.fix[-Idx.NA.Rdm,,drop=FALSE]
data.nph <- data.nph[-Idx.NA.Rdm,,drop=FALSE]
}
random.obs <- as.factor(random.obs)
clust <- levels(random.obs)
IdxRnd <- order(random.obs)
## The dataset MUST be ordered by the levels of the clustering variable
random.obs <- random.obs[IdxRnd]
time.obs <- time.obs[IdxRnd,,drop=FALSE]
status.obs <- status.obs[IdxRnd]
lambda.pop <- lambda.pop[IdxRnd]
data.fix <- data.fix[IdxRnd,,drop=FALSE]
data.nph <- data.nph[IdxRnd,,drop=FALSE]
listIdx <- sapply(levels(random.obs),function(x){which(random.obs==x)},simplify=FALSE)
}
TotData <- cbind(time.obs,status.obs,lambda.pop,data.fix,data.nph)
Xlevels <- .getXlevels(Xlevel.formula,TotData)
Idx.Non.NA <- which(apply(TotData,1,function(vec){sum(is.na(vec))})==0)
if (length(Idx.Non.NA)<n.obs.tot){
warning("Covariables information was missing for some observations. These observations were consequently removed...")
}
time.obs <- time.obs[Idx.Non.NA,,drop=FALSE]
n.obs <- dim(time.obs)[1]
if (n.obs==0){
stop("No non-missing values for some covariables...")
}
if (Survtype=="right"){
time.obs.0 <- NULL
Idx.Time.Neg <- which(time.obs<0)
if (length(Idx.Time.Neg)>0){
stop("Some observations have a negative follow-up time...")
}
Idx.Time.0 <- which(time.obs==0)
if (length(Idx.Time.0)>0){
warning("Some observations had a follow-up time of length 0. A value of 1/730.5 (half a day) was substituted. Please check to see if it is appropriate or deal with 0 follow-up time values before using the mexhaz function.")
}
time.obs[Idx.Time.0] <- 1/730.5
}
if (Survtype=="counting"){
time.obs.0 <- time.obs[,1]
time.obs <- time.obs[,2]
Idx.Time.Neg1 <- which(time.obs.0<0)
if (length(Idx.Time.Neg1)>0){
stop("Some observations have a negative entry time...")
}
Idx.Time.Neg2 <- which(time.obs<0)
if (length(Idx.Time.Neg2)>0){
stop("Some observations have a negative exit time...")
}
}
max.time <- max(time.obs)
TestBo <- 0
if (!is.null(bound) & base%in%c("exp.bs","exp.ns")){
if (!is.numeric(bound) | length(bound)!=2 | bound[1]==bound[2]){
warning("The 'bound' argument should be a vector of two non-equal numeric values. Consequently, the boundary knots were assigned the default values (0,max.time).")
TestBo <- 1
}
else {
if (bound[2]<bound[1]){
bound <- bound[order(bound)]
warning("The 'bound' argument should be a vector of two ordered numeric values. Consequently, the values given were re-ordered.")
}
if (base=="exp.bs" & (bound[1]>0 | bound[2]<max.time)){
warning("When used with the 'exp.bs' hazard, bound[1] should not be greater than 0 and bound[2] should not be lower than max.time. Consequently, the boundary knots were assigned the default values (0,max.time).")
TestBo <- 1
}
}
}
else {
TestBo <- 1
}
if (TestBo==1){
BoI <- 0
BoS <- max.time
}
else {
BoI <- bound[1]
BoS <- bound[2]
}
status.obs <- status.obs[Idx.Non.NA]
n.events <- sum(status.obs)
data.fix <- data.fix[Idx.Non.NA,,drop=FALSE]
fix.obs <- model.matrix(FormulaF,data=data.fix,drop.unused.levels=TRUE)
names.fix <- colnames(fix.obs)[-1]
data.nph <- data.nph[Idx.Non.NA,,drop=FALSE]
nph.obs <- model.matrix(FormulaN,data=data.nph,drop.unused.levels=TRUE)
nbtd <- dim(nph.obs)[2]
names.nph <- colnames(nph.obs)[-1]
if (sum(!names.nph%in%names.fix)){
stop("Some variables in the 'nph()' sub-formula are not included in the formula...")
}
lambda.pop <- lambda.pop[Idx.Non.NA]
## Remove unnecessary objects
rm(TotData,data.fix,data.nph)
## Creation of the different objects necessary to compute the hazards
## Weibull hazard
if (base=="weibull"){
time.cat.0 <- NULL
time.cat <- NULL
degree <- NA
vec.knots <- NULL
int.knots <- NULL
MatK <- NULL
NsAdj <- list(NULL,NULL)
n.td.base <- 1
n.ntd <- dim(fix.obs)[2]
if (n.ntd>1){
which.ntd <- c(1,2+1:(n.ntd-1))
}
else {
which.ntd <- 1
}
n.td.nph <- length(names.nph)
if (n.td.nph>0){
names.nph <- paste("Rho",names.nph,sep="*")
which.td <- c(2,(2+(n.ntd-1))+1:n.td.nph)
}
else {
which.td <- 2
}
fix.obs <- fix.obs[,-1,drop=FALSE]
nph.obs <- nph.obs[,-1,drop=FALSE]
param.names <- c("logLambda","logRho",names.fix,names.nph)
n.par.fix <- n.td.base+n.ntd+n.td.nph
param.init <- rep(0,n.par.fix)
param.init[1:2] <- 0.1
}
## Hazard modelled by the exponential of a B-spline / restricted cubic B-spline / Piecewise constant
else if (base%in%c("exp.bs","exp.ns","pw.cst")){
## Baseline hazard-related objects
if (!is.null(knots)){
if (min(knots,na.rm=TRUE)<=0 | max(knots,na.rm=TRUE)>=max.time | is.na(sum(knots))){
stop("The 'knots' argument should be a vector of values strictly between 0 and max.time, the maximum follow-up time observed in the dataset")
}
else {
if (sum(abs(knots-knots[order(knots)]))>0){
warning("The knots were not given in increasing order. They were consequently re-ordered.")
knots <- knots[order(knots)]
}
if (length(unique(knots))<length(knots)){
warning("There were duplicate values in the vector of knots. Duplicate values were removed.")
knots <- unique(knots)
}
}
}
cuts <- c(0,knots,max.time)
time.cat.lab <- cut(time.obs,breaks=cuts,include.lowest=TRUE)
time.cat <- as.numeric(time.cat.lab)-1
if (base=="pw.cst"){
degree <- 0
time.obs <- time.obs-cuts[time.cat+1]
if (Survtype=="counting"){
time.cat.0 <- as.numeric(cut(time.obs.0,breaks=cuts,include.lowest=TRUE))-1
time.obs.0 <- time.obs.0-cuts[time.cat.0+1]
}
else if (Survtype=="right"){
time.cat.0 <- NULL
}
vec.knots <- NULL
int.knots <- NULL
MatK <- c(knots,BoS)-c(BoI,knots)
NsAdj <- list(NULL,NULL)
n.td.base <- length(knots)+1
names.base <- levels(time.cat.lab)
## For non time-dependent effects
fix.obs <- fix.obs[,-which(colnames(fix.obs)%in%colnames(nph.obs)),drop=FALSE]
names.fix <- colnames(fix.obs)
if (!is.null(names.fix)){
n.ntd <- dim(fix.obs)[2]
}
else {
n.ntd <- 0
fix.obs <- 0
}
if (n.ntd>0){
which.ntd <- c(n.td.base+1:n.ntd)
}
else {
which.ntd <- NULL
}
intercept <- NULL
n.inter <- 0
}
else if (base%in%c("exp.bs","exp.ns")){
if (Survtype=="counting"){
time.cat.0 <- as.numeric(cut(time.obs.0,breaks=cuts,include.lowest=TRUE))-1
}
else if (Survtype=="right"){
time.cat.0 <- NULL
}
vec.knots <- c(rep(BoI,degree),knots,rep(BoS,degree))
ltk <- length(vec.knots)
MatK <- TransfDeg(vec.knots,degree)
NsAdj <- NsAdjust(c(BoI,vec.knots,BoS),BoI,BoS)
n.td.base <- dbase + length(knots)
names.base <- paste(ifelse(base=="exp.bs","BS","NS"),degree,".",1:n.td.base,sep = "")
if (base=="exp.bs") {
int.knots <- cuts
}
else {
BOI <- NULL
BOS <- NULL
firstK <- 1
if (BoI>0){
BOI <- BoI
MatK <- cbind(0,MatK)
vec.knots <- c(BoI,vec.knots)
firstK <- 0
}
if (BoS<max.time){
BOS <- BoS
MatK <- cbind(MatK,0)
vec.knots <- c(vec.knots,BoS)
}
int.knots <- c(0,BOI,knots,BOS,max.time)
time.cat.lab <- cut(time.obs,breaks=int.knots,include.lowest=TRUE)
time.cat <- as.numeric(time.cat.lab)-1
degree <- c(degree,ltk,firstK) # ltk and firstK are passed to the HazardInt function through the 'degree' argument which is not used with NS
}
## For non time-dependent effects
n.ntd <- dim(fix.obs)[2]
if (n.ntd>1){
which.ntd <- c(1,(n.td.base+1)+1:(n.ntd-1))
}
else {
which.ntd <- 1
}
intercept <- "Intercept"
n.inter <- 1
}
## For time-dependent effects
n.td.nph <- length(names.nph)*n.td.base
if (n.td.nph>0){
which.td <- c(n.inter+1:n.td.base,(n.td.base+n.ntd)+1:n.td.nph)
}
else {
which.td <- c(n.inter+1:n.td.base)
}
names.nph <- unlist(sapply(names.nph,function(x){paste(x,names.base,sep="*")}))
param.names <- c(intercept,names.base,names.fix,names.nph)
n.par.fix <- n.td.base+n.ntd+n.td.nph
param.init <- rep(0,n.par.fix)
param.init[1:(n.td.base+n.inter)] <- -1
}
fix.obs <- t(fix.obs) # Matrix of fixed effects has to be transposed for use by the Int/Delta functions
nph.obs <- t(nph.obs) # Matrix of time-dependent effects has to be transposed for use by the Int/Delta functions
## Creation of objects related to the random effect
n.clust <- 1
n.rand <- 0
n.par <- n.td.base + n.ntd + n.td.nph
which.rdm <- NULL
if (!is.null(random)){
n.rand <- 1
n.par <- n.par + 1
which.rdm <- n.par
random.obs <- random.obs[Idx.Non.NA]
status.one <- which(status.obs==1)
lambda.pop.delta <- lambda.pop[status.one]
n.clust <- length(clust) # Number of clusters
n.by.clust <- as.vector(table(random.obs)) # Size of each cluster
n.by.clust.delta <- as.vector(table(random.obs[status.one]))
Idx.clust.no.obs <- which(n.by.clust==0)
if (length(Idx.clust.no.obs)>0){
warning("Some clusters had no non-missing values for some covariables. These observations were consequently removed...")
cat("The following clusters had no non-missing values for some covariables:\n")
print(names(table(random.obs))[Idx.clust.no.obs])
n.by.clust <- n.by.clust[-Idx.clust.no.obs]
n.by.clust.delta <- n.by.clust.delta[-Idx.clust.no.obs]
clust <- clust[-Idx.clust.no.obs]
n.clust <- length(clust)
}
parent.cst.adj <- rep(0,n.clust)
if (Survtype=="counting"){
parent.cst.adj0 <- rep(0,n.clust)
}
else {
parent.cst.adj0 <- NULL
}
param.names <- c(param.names,paste(random," [log(sd)]",sep=""))
## Creation of the points and weights of Gauss-Hermite quadrature
if (n.aghq<=0 | round(n.aghq,0)!=n.aghq){
stop("The 'n.aghq' argument must be a strictly positive integer.")
}
gq <- gauss.quad(n=n.aghq,kind="hermite")
x.H <- gq$nodes
x.H.2 <- x.H^2
log.rho.H <- log(gq$weights)
log.rho.H[log.rho.H==-Inf] <- -.Machine$double.xmax
}
## Initial values
if (!is.null(init)){
if (length(init)!=n.par){
print(data.frame(Time.Dep.baseline=n.td.base,Non.TD.Effects=n.ntd,TD.Effects=n.td.nph,Random.Effect=n.rand))
stop("Wrong number of init parameters",call.=FALSE)
}
}
else {
init <- c(param.init,rep(0.1,n.rand))
}
## Creation of variables that will be modified by the Hazard and LL.Tot functions
parent.neval <- 0
parent.param <- init
parent.logLik <- -.Machine$double.xmax
## Function that actually computes the log-likelihood
LL.Tot <- function(p.LT,mu.hat.LT=0,env=FALCenv){
p.td <- p.LT[which.td]
p.ntd <- p.LT[which.ntd]
temp.H <- HazardInt(x0=time.obs.0,x=time.obs,nph=nph.obs,timecat0=time.cat.0,timecat=time.cat,fixobs=fix.obs,param=p.td,paramf=p.ntd,deg=degree,n=gln,lw=lglw,matk=MatK,totk=vec.knots,intk=int.knots,nsadj1=NsAdj[[1]],nsadj2=NsAdj[[2]])
if (temp.H$Test){
res.LT <- .Machine$double.xmax
}
else {
if (!is.null(random)){
MH0 <- 0
var.w <- exp(2*p.LT[which.rdm])
if (Survtype=="counting"){
MH0 <- -lambertW0(unlist(lapply(listIdx,function(x){sum(temp.H$HazCum0[x])*var.w})))
}
temp.LT <- Frailty.Adapt(nodes=x.H, nodessquare=x.H.2, logweights=log.rho.H, clust=n.by.clust, clustd=n.by.clust.delta, expect=lambda.pop.delta, betal=temp.H$LogHaz[status.one], betaL0=temp.H$HazCum0, betaL=temp.H$HazCum, A0=parent.cst.adj0, A=parent.cst.adj, var=var.w, mh0=MH0, muhatcond=mu.hat.LT)
if (mu.hat.LT==1)
res.LT <- temp.LT$MuHat
else if (mu.hat.LT==2)
res.LT <- temp.LT$SigmaHat
else {
env$parent.cst.adj0 <- temp.LT$CstAdj0
env$parent.cst.adj <- temp.LT$CstAdj
res.LT <- temp.LT$LogLik
res.LT[is.nan(res.LT) | abs(res.LT)==Inf] <- .Machine$double.xmax
}
}
else {
if (withExp==1){
temp.l <- exp(temp.H$LogHaz)+lambda.pop
if (sum(temp.l-lambda.pop)==0){
res.LT <- .Machine$double.xmax
} # Prevents a kind of catastrophic cancellation
else {
log.lambda <- log(temp.l)
log.lambda[log.lambda==Inf] <- .Machine$double.xmax
res.LT <- sum(temp.H$HazCum - status.obs*log.lambda)
res.LT <- min(res.LT,.Machine$double.xmax)
}
}
else {
log.lambda <- temp.H$LogHaz
log.lambda[log.lambda==Inf] <- .Machine$double.xmax
res.LT <- sum(temp.H$HazCum - status.obs*log.lambda)
res.LT <- min(res.LT,.Machine$double.xmax)
}
}
if (mu.hat.LT==0){
if (sum(p.LT-parent.param)){
verbose.ll()
env$parent.neval <- parent.neval + 1
}
env$parent.param <- p.LT + 0 # Necessary...
env$parent.logLik <- res.LT
}
}
return(res.LT)
}
if (mode=="fit"){
## Launching the optimisation procedure
if (fnoptim=="nlm"){
mod.lik <- nlm(LL.Tot,init,iterlim=iterlim,hessian=TRUE,print.level=print.level,...)
param.fin <- mod.lik$estimate
code.fin <- mod.lik$code
loglik <- -mod.lik$minimum
gradient <- mod.lik$gradient
iterations <- mod.lik$iterations
}
else if (fnoptim=="optim"){
mod.lik <- optim(init,LL.Tot,method=method,hessian=TRUE,...)
param.fin <- mod.lik$par
code.fin <- mod.lik$convergence
loglik <- -mod.lik$value
gradient <- rep(NA,n.par)
iterations <- "---"
}
names(param.fin) <- param.names
## Hessian matrix
verbose.ll <- function(){} # Suppresses iteration printing
if (numHess){
cat("Computation of the Hessian\n")
hessian <- hessian(LL.Tot,param.fin,mu.hat.LT=0,method.args=list(r=4))
}
else {
hessian <- mod.lik$hessian
}
vcov <- try(solve(hessian),silent=TRUE)
if (is.character(vcov)){
vcov <- matrix(NA,n.par,n.par)
warning("Unable to invert the Hessian matrix...")
}
colnames(vcov) <- rownames(vcov) <- param.names
## Empirical Bayes Estimates
if (!is.null(random)) {
cat("Computation of the covariance matrix of the shrinkage estimators\n")
mu.hat.fin <- LL.Tot(param.fin,mu.hat.LT=1)
sigma.hat.fin <- LL.Tot(param.fin,mu.hat.LT=2)
deriv.mu.hat.fin <- jacobian(LL.Tot,param.fin,mu.hat.LT=1,method.args=list(r=4))
vcov.par.mu.hat <- vcov%*%t(deriv.mu.hat.fin)
var.mu.hat <- diag(sigma.hat.fin^2)+(deriv.mu.hat.fin%*%vcov.par.mu.hat)
mu.hat.df <- data.frame(cluster=clust,mu.hat=mu.hat.fin,sigma.hat=sigma.hat.fin)
vcov.fix.mu.hat <- vcov.par.mu.hat[-which.rdm,,drop=FALSE]
}
else {
mu.hat.df <- NA
var.mu.hat <- NA
vcov.fix.mu.hat <- NA
}
}
else if (mode=="eval"){
loglik <- LL.Tot(init)
gradient <- grad(LL.Tot,init,method.args=list(r=4))
hessian <- hessian(LL.Tot,init,mu.hat.LT=0,method.args=list(r=4))
param.fin <- init
names(param.fin) <- param.names
code.fin <- 9
iterations <- 0
vcov <- matrix(NA,n.par,n.par)
colnames(vcov) <- rownames(vcov) <- param.names
mu.hat.df <- NA
var.mu.hat <- NA
vcov.fix.mu.hat <- NA
if (!is.null(random)) {
mu.hat.fin <- LL.Tot(init,mu.hat.LT=1)
sigma.hat.fin <- LL.Tot(init,mu.hat.LT=2)
mu.hat.df <- data.frame(cluster=clust,mu.hat=mu.hat.fin,sigma.hat=sigma.hat.fin)
}
}
time1 <- as.numeric(proc.time()[3])
PrintData <- data.frame(Name=name.data,N.Obs.Tot=n.obs.tot,N.Obs=n.obs,N.Events=n.events,N.Clust=n.clust,row.names="")
PrintDetails <- data.frame(Iter=iterations,Eval=parent.neval+1,Base=base,Nb.Leg=n.gleg,
Nb.Aghq=n.aghq,Optim=fnoptim,Method=ifelse(fnoptim=="optim",method,"---"),
Code=code.fin,LogLik=loglik,Total.Time=(time1-time0),row.names="")
## Part of the results printed on screen
cat("\nData\n")
print(PrintData)
cat("\nDetails\n")
print(PrintDetails)
res.FAR <- list(dataset=name.data,
call=call,
formula=formula,
expected=ifelse(!is.null(expected),expected,NA),
xlevels=Xlevels,
n.obs.tot=n.obs.tot,
n.obs=n.obs,
n.events=n.events,
n.clust=ifelse(!is.null(random),n.clust,1),
n.time.0=ifelse(Survtype=="right",length(Idx.Time.0),0),
base=base,
max.time=max.time,
boundary.knots=c(BoI,BoS),
degree=degree[1],
knots=knots,
names.ph=names.fix,
random=ifelse(!is.null(random),random,NA),
init=init,
coefficients=param.fin,
std.errors=sqrt(diag(vcov)),
vcov=vcov,
gradient=gradient,
hessian=hessian,
mu.hat=mu.hat.df,
var.mu.hat=var.mu.hat,
vcov.fix.mu.hat=t(vcov.fix.mu.hat),
n.par=n.par,
n.gleg=n.gleg,
n.aghq=n.aghq,
fnoptim=fnoptim,
method=ifelse(fnoptim=="optim",method,NA),
code=code.fin,
loglik=loglik,
iter=iterations,
eval=parent.neval,
time.elapsed=time1-time0)
class(res.FAR) <- "mexhaz"
res.FAR
}
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mexhaz documentation built on Oct. 31, 2022, 5:08 p.m.
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Defines functions mexhazStd
mexhazStd <- function(formula,data,expected=NULL,base="weibull",degree=3,knots=NULL,bound=NULL,n.gleg=20,init=NULL,random=NULL,n.aghq=10,fnoptim="nlm",verbose=0,method="Nelder-Mead",iterlim=10000,numHess=FALSE,print.level=1,gradtol=1e-6,mode="fit",...){
time0 <- as.numeric(proc.time()[3])
FALCenv <- environment()
call <- match.call()
mf <- match.call(expand.dots=FALSE)
m <- match(c("formula","data"),names(mf),0L)
if (m[1]==0){
stop("The 'formula' argument is required...")
}
if (m[2]==0){
stop("The 'data' argument is required...")
}
name.data <- paste(substitute(data),sep="")
if (length(name.data)>1){
name.data <- name.data[2]
}
if (base=="exp.bs" & !degree%in%c(1:3)){
stop("This function can only be used to estimate log-hazards described by B-splines of degree 1 to 3...")
}
## Function that controls what is printed during the optimisation procedure
if (verbose>0){
verbose.ll <- function(){
if (!((iv <- parent.neval/verbose)-floor(iv))){
time1 <- as.numeric(proc.time()[3])
print(data.frame(Eval=parent.neval,LogLik=-parent.logLik,Time=time1-time0,row.names=""))
cat("Param\n")
print(round(parent.param,4))
cat("\n")
}
}
}
else {
verbose.ll <- function(){}
}
## Functions for computing the part of the B-spline bases that depends only on the knots and can therefore be calculated only once at the beginning of the function (used in combination with the IntHazard function to estimate the hazard and the cumulative hazard)
## For B-splines (also used for Natural Cubic Splines)
TransfDeg <- function(vec.knots,deg){
if (deg==1){
Le <- length(vec.knots)
Res <- vec.knots[2:Le]-vec.knots[1:(Le-1)]
}
else {
Dim <- (length(vec.knots)-(2*deg-1))
Res <- matrix(NA,2*(deg-1),Dim)
if (deg==2){
for (i in 1:Dim){
TempK <- vec.knots[i:(i+3)]
Res[1,i] <- 1/((TempK[4]-TempK[2])*(TempK[3]-TempK[2]))
Res[2,i] <- 1/((TempK[3]-TempK[1])*(TempK[3]-TempK[2]))
}
}
else if (deg==3){
for (i in 1:Dim){
TempK <- vec.knots[i:(i+5)]
Res[1,i] <- 1/((TempK[6]-TempK[3])*(TempK[5]-TempK[3])*(TempK[4]-TempK[3]))
Res[2,i] <- 1/((TempK[5]-TempK[2])*(TempK[4]-TempK[2])*(TempK[4]-TempK[3]))
Res[3,i] <- 1/((TempK[5]-TempK[2])*(TempK[5]-TempK[3])*(TempK[4]-TempK[3]))
Res[4,i] <- 1/((TempK[4]-TempK[1])*(TempK[4]-TempK[2])*(TempK[4]-TempK[3]))
}
}
}
return(Res)
}
## For Natural Cubic Splines
if (base=="exp.bs"){
NsAdjust <- function(vec.knots,BoI,BoS){
NULL
}
dbase <- degree
}
else if (base=="exp.ns"){
NsAdjust <- function(vec.knots,BoI,BoS){
Dim <- (length(vec.knots)-5)
Diag <- diag(Dim)
QR <- qr(t(splineDesign(vec.knots,c(BoI,BoS),4,c(2,2))[,-1,drop=FALSE]))
Res1 <- t(apply(Diag,1,function(x){qr.qty(QR,x)})[-c(1:2),,drop=FALSE])
SpI <- c(BoI,splineDesign(vec.knots,rep(BoI,2L),4,c(0,1))[2,1:2])
SpS <- c(BoS,splineDesign(vec.knots,rep(BoS,2L),4,c(0,1))[2,(Dim:(Dim+1))])
Res2 <- cbind(SpI,SpS)
return(list(Res1,Res2))
}
degree <- 3
dbase <- 1
}
## Creating the points and weights of Gauss-Legendre quadrature (used when base is "exp.bs" and degree in c(2:3) or when base is "exp.ns")
if (n.gleg<=0 | round(n.gleg,0)!=n.gleg){
stop("The 'n.gleg' argument must be a strictly positive integer.")
}
gl <- gauss.quad(n=n.gleg,kind="legendre")
gln <- gl$nodes
lglw <- log(gl$weights)
## Data preparation
mf <- mf[c(1L,m)]
mf$drop.unused.levels <- TRUE
mf$na.action <- "na.pass"
mf[[1L]] <- quote(stats::model.frame)
tot.formula <- terms(formula,data=data,specials="nph")
indNph <- attr(tot.formula,"specials")$nph
if (length(indNph)>0){
nTerm <- NULL
nTerm2 <- NULL
for (i in 1:length(indNph)){
nphterm <- attr(tot.formula,"variables")[[1+indNph[i]]]
nTerm <- c(nTerm,deparse(nphterm[[2L]],width.cutoff=500L,backtick=TRUE))
nTerm2 <- c(nTerm2,deparse(nphterm,width.cutoff=500L,backtick=TRUE))
}
nTerm <- paste(nTerm,collapse="+")
nTerm2 <- paste(nTerm2,collapse="-")
FormulaN <- as.formula(paste("~",nTerm))
}
else {
nTerm2 <- 0
FormulaN <- as.formula("~1")
}
mf$formula <- update(tot.formula,paste(".~.",nTerm2,sep="-"))
FormulaF <- update(tot.formula,paste("NULL~.",nTerm2,sep="-"))
Xlevel.formula <- terms(FormulaF,data=data)
m <- eval(mf,parent.frame())
if (nrow(m)==0){
stop("No non-missing observations...")
}
Y <- model.extract(m,"response")
if (!inherits(Y,"Surv")){
stop("Response must be a Surv() object...")
}
Survtype <- attr(Y, "type")
if (ncol(Y)==2){
if (Survtype!="right"){
stop(paste("mexhaz does not support \"", Survtype, "\" type of censoring with (0, time] survival data",
sep = ""))
}
time.obs <- Y[,1,drop=FALSE] # Follow-up time
status.obs <- Y[,2] # Status variable
if (base=="pw.cst"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs0R,x,nph,timecat,fixobs,param,paramf,as.double(matk))
}
}
else if (base=="exp.bs" & degree==1){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs1R,x,nph,timecat,fixobs,param,paramf,as.double(matk),as.double(totk))
}
}
else if (base=="exp.bs" & degree%in%c(2,3)){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs23R,x,nph,timecat,fixobs,param,paramf,deg,n,lw,as.double(matk),as.double(totk))
}
}
else if (base=="exp.ns"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardNsR,x,nph,timecat,fixobs,param,paramf,deg,n,lw,as.double(matk),as.double(totk),as.double(intk),as.double(nsadj1),as.double(nsadj2))
}
}
else if (base=="weibull"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardWeibR,x,nph,fixobs,param,paramf)
}
}
}
else if (ncol(Y)==3){
if (Survtype!="counting"){
stop(paste("mexhaz does not support \"", Survtype, "\" type of censoring with (time, time2] survival data",
sep = ""))
}
time.obs <- Y[,1:2] # Entry time / Follow-up time
status.obs <- Y[,3] # Status variable
if (is.null(random)){
if (base=="pw.cst"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs0C,x0,x,nph,timecat0,timecat,fixobs,param,paramf,as.double(matk))
}
}
else if (base=="exp.bs" & degree==1){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs1C,x0,x,nph,timecat0,timecat,fixobs,param,paramf,as.double(matk),as.double(totk))
}
}
else if (base=="exp.bs" & degree%in%c(2,3)){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs23C,x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,as.double(matk),as.double(totk))
}
}
else if (base=="exp.ns"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardNsC,x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,as.double(matk),as.double(totk),as.double(intk),as.double(nsadj1),as.double(nsadj2))
}
}
else if (base=="weibull"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardWeibC,x0,x,nph,fixobs,param,paramf)
}
}
}
else {
if (base=="pw.cst"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs0L,x0,x,nph,timecat0,timecat,fixobs,param,paramf,as.double(matk))
}
}
else if (base=="exp.bs" & degree==1){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs1L,x0,x,nph,timecat0,timecat,fixobs,param,paramf,as.double(matk),as.double(totk))
}
}
else if (base=="exp.bs" & degree%in%c(2,3)){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardBs23L,x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,as.double(matk),as.double(totk))
}
}
else if (base=="exp.ns"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardNsL,x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,as.double(matk),as.double(totk),as.double(intk),as.double(nsadj1),as.double(nsadj2))
}
}
else if (base=="weibull"){
HazardInt <- function(x0,x,nph,timecat0,timecat,fixobs,param,paramf,deg,n,lw,matk,totk,intk,nsadj1,nsadj2){
.Call(C_HazardWeibL,x0,x,nph,fixobs,param,paramf)
}
}
}
}
if (Survtype=="counting" & !is.null(random)){
Frailty.Adapt <- function(nodes, nodessquare, logweights, clust, clustd, expect, betal, betaL0, betaL, A0, A, var, mh0, muhatcond){
obj <- .Call(C_FrailtyAdaptL, nodes, nodessquare, logweights, clust, clustd, expect, betal, betaL0, betaL, A0, A, var, mh0, muhatcond)
}
}
else {
Frailty.Adapt <- function(nodes, nodessquare, logweights, clust, clustd, expect, betal, betaL0, betaL, A0, A, var, mh0, muhatcond){
obj <- .Call(C_FrailtyAdapt, nodes, nodessquare, logweights, clust, clustd, expect, betal, betaL, A, var, muhatcond)
}
}
data.fix <- model.frame(FormulaF,data=data,na.action=na.pass)
data.nph <- model.frame(FormulaN,data=data,na.action=na.pass)
n.obs.tot <- dim(data)[1] # Total number of observations
## Expected hazard (a vector of 0 is created if NULL)
if (!is.null(expected)){
lambda.pop <- data[,expected]
if (min(lambda.pop,na.rm=TRUE)<0){
stop("The expected hazard for some observations is negative!")
}
withExp <- 1
}
else {
lambda.pop <- rep(0,n.obs.tot)
withExp <- 0
}
## Remove observations containing missing values and perform several formatting operations on the data
if (!is.null(random)){
random.obs <- data[,random]
Idx.NA.Rdm <- which(is.na(random.obs))
if (length(Idx.NA.Rdm)>0){
warning("Cluster information was missing for some observations. These observations were consequently removed...")
random.obs <- random.obs[-Idx.NA.Rdm]
time.obs <- time.obs[-Idx.NA.Rdm,,drop=FALSE]
status.obs <- status.obs[-Idx.NA.Rdm]
lambda.pop <- lambda.pop[-Idx.NA.Rdm]
data.fix <- data.fix[-Idx.NA.Rdm,,drop=FALSE]
data.nph <- data.nph[-Idx.NA.Rdm,,drop=FALSE]
}
random.obs <- as.factor(random.obs)
clust <- levels(random.obs)
IdxRnd <- order(random.obs)
## The dataset MUST be ordered by the levels of the clustering variable
random.obs <- random.obs[IdxRnd]
time.obs <- time.obs[IdxRnd,,drop=FALSE]
status.obs <- status.obs[IdxRnd]
lambda.pop <- lambda.pop[IdxRnd]
data.fix <- data.fix[IdxRnd,,drop=FALSE]
data.nph <- data.nph[IdxRnd,,drop=FALSE]
listIdx <- sapply(levels(random.obs),function(x){which(random.obs==x)},simplify=FALSE)
}
TotData <- cbind(time.obs,status.obs,lambda.pop,data.fix,data.nph)
Xlevels <- .getXlevels(Xlevel.formula,TotData)
Idx.Non.NA <- which(apply(TotData,1,function(vec){sum(is.na(vec))})==0)
if (length(Idx.Non.NA)<n.obs.tot){
warning("Covariables information was missing for some observations. These observations were consequently removed...")
}
time.obs <- time.obs[Idx.Non.NA,,drop=FALSE]
n.obs <- dim(time.obs)[1]
if (n.obs==0){
stop("No non-missing values for some covariables...")
}
if (Survtype=="right"){
time.obs.0 <- NULL
Idx.Time.Neg <- which(time.obs<0)
if (length(Idx.Time.Neg)>0){
stop("Some observations have a negative follow-up time...")
}
Idx.Time.0 <- which(time.obs==0)
if (length(Idx.Time.0)>0){
warning("Some observations had a follow-up time of length 0. A value of 1/730.5 (half a day) was substituted. Please check to see if it is appropriate or deal with 0 follow-up time values before using the mexhaz function.")
}
time.obs[Idx.Time.0] <- 1/730.5
}
if (Survtype=="counting"){
time.obs.0 <- time.obs[,1]
time.obs <- time.obs[,2]
Idx.Time.Neg1 <- which(time.obs.0<0)
if (length(Idx.Time.Neg1)>0){
stop("Some observations have a negative entry time...")
}
Idx.Time.Neg2 <- which(time.obs<0)
if (length(Idx.Time.Neg2)>0){
stop("Some observations have a negative exit time...")
}
}
max.time <- max(time.obs)
TestBo <- 0
if (!is.null(bound) & base%in%c("exp.bs","exp.ns")){
if (!is.numeric(bound) | length(bound)!=2 | bound[1]==bound[2]){
warning("The 'bound' argument should be a vector of two non-equal numeric values. Consequently, the boundary knots were assigned the default values (0,max.time).")
TestBo <- 1
}
else {
if (bound[2]<bound[1]){
bound <- bound[order(bound)]
warning("The 'bound' argument should be a vector of two ordered numeric values. Consequently, the values given were re-ordered.")
}
if (base=="exp.bs" & (bound[1]>0 | bound[2]<max.time)){
warning("When used with the 'exp.bs' hazard, bound[1] should not be greater than 0 and bound[2] should not be lower than max.time. Consequently, the boundary knots were assigned the default values (0,max.time).")
TestBo <- 1
}
}
}
else {
TestBo <- 1
}
if (TestBo==1){
BoI <- 0
BoS <- max.time
}
else {
BoI <- bound[1]
BoS <- bound[2]
}
status.obs <- status.obs[Idx.Non.NA]
n.events <- sum(status.obs)
data.fix <- data.fix[Idx.Non.NA,,drop=FALSE]
fix.obs <- model.matrix(FormulaF,data=data.fix,drop.unused.levels=TRUE)
names.fix <- colnames(fix.obs)[-1]
data.nph <- data.nph[Idx.Non.NA,,drop=FALSE]
nph.obs <- model.matrix(FormulaN,data=data.nph,drop.unused.levels=TRUE)
nbtd <- dim(nph.obs)[2]
names.nph <- colnames(nph.obs)[-1]
if (sum(!names.nph%in%names.fix)){
stop("Some variables in the 'nph()' sub-formula are not included in the formula...")
}
lambda.pop <- lambda.pop[Idx.Non.NA]
## Remove unnecessary objects
rm(TotData,data.fix,data.nph)
## Creation of the different objects necessary to compute the hazards
## Weibull hazard
if (base=="weibull"){
time.cat.0 <- NULL
time.cat <- NULL
degree <- NA
vec.knots <- NULL
int.knots <- NULL
MatK <- NULL
NsAdj <- list(NULL,NULL)
n.td.base <- 1
n.ntd <- dim(fix.obs)[2]
if (n.ntd>1){
which.ntd <- c(1,2+1:(n.ntd-1))
}
else {
which.ntd <- 1
}
n.td.nph <- length(names.nph)
if (n.td.nph>0){
names.nph <- paste("Rho",names.nph,sep="*")
which.td <- c(2,(2+(n.ntd-1))+1:n.td.nph)
}
else {
which.td <- 2
}
fix.obs <- fix.obs[,-1,drop=FALSE]
nph.obs <- nph.obs[,-1,drop=FALSE]
param.names <- c("logLambda","logRho",names.fix,names.nph)
n.par.fix <- n.td.base+n.ntd+n.td.nph
param.init <- rep(0,n.par.fix)
param.init[1:2] <- 0.1
}
## Hazard modelled by the exponential of a B-spline / restricted cubic B-spline / Piecewise constant
else if (base%in%c("exp.bs","exp.ns","pw.cst")){
## Baseline hazard-related objects
if (!is.null(knots)){
if (min(knots,na.rm=TRUE)<=0 | max(knots,na.rm=TRUE)>=max.time | is.na(sum(knots))){
stop("The 'knots' argument should be a vector of values strictly between 0 and max.time, the maximum follow-up time observed in the dataset")
}
else {
if (sum(abs(knots-knots[order(knots)]))>0){
warning("The knots were not given in increasing order. They were consequently re-ordered.")
knots <- knots[order(knots)]
}
if (length(unique(knots))<length(knots)){
warning("There were duplicate values in the vector of knots. Duplicate values were removed.")
knots <- unique(knots)
}
}
}
cuts <- c(0,knots,max.time)
time.cat.lab <- cut(time.obs,breaks=cuts,include.lowest=TRUE)
time.cat <- as.numeric(time.cat.lab)-1
if (base=="pw.cst"){
degree <- 0
time.obs <- time.obs-cuts[time.cat+1]
if (Survtype=="counting"){
time.cat.0 <- as.numeric(cut(time.obs.0,breaks=cuts,include.lowest=TRUE))-1
time.obs.0 <- time.obs.0-cuts[time.cat.0+1]
}
else if (Survtype=="right"){
time.cat.0 <- NULL
}
vec.knots <- NULL
int.knots <- NULL
MatK <- c(knots,BoS)-c(BoI,knots)
NsAdj <- list(NULL,NULL)
n.td.base <- length(knots)+1
names.base <- levels(time.cat.lab)
## For non time-dependent effects
fix.obs <- fix.obs[,-which(colnames(fix.obs)%in%colnames(nph.obs)),drop=FALSE]
names.fix <- colnames(fix.obs)
if (!is.null(names.fix)){
n.ntd <- dim(fix.obs)[2]
}
else {
n.ntd <- 0
fix.obs <- 0
}
if (n.ntd>0){
which.ntd <- c(n.td.base+1:n.ntd)
}
else {
which.ntd <- NULL
}
intercept <- NULL
n.inter <- 0
}
else if (base%in%c("exp.bs","exp.ns")){
if (Survtype=="counting"){
time.cat.0 <- as.numeric(cut(time.obs.0,breaks=cuts,include.lowest=TRUE))-1
}
else if (Survtype=="right"){
time.cat.0 <- NULL
}
vec.knots <- c(rep(BoI,degree),knots,rep(BoS,degree))
ltk <- length(vec.knots)
MatK <- TransfDeg(vec.knots,degree)
NsAdj <- NsAdjust(c(BoI,vec.knots,BoS),BoI,BoS)
n.td.base <- dbase + length(knots)
names.base <- paste(ifelse(base=="exp.bs","BS","NS"),degree,".",1:n.td.base,sep = "")
if (base=="exp.bs") {
int.knots <- cuts
}
else {
BOI <- NULL
BOS <- NULL
firstK <- 1
if (BoI>0){
BOI <- BoI
MatK <- cbind(0,MatK)
vec.knots <- c(BoI,vec.knots)
firstK <- 0
}
if (BoS<max.time){
BOS <- BoS
MatK <- cbind(MatK,0)
vec.knots <- c(vec.knots,BoS)
}
int.knots <- c(0,BOI,knots,BOS,max.time)
time.cat.lab <- cut(time.obs,breaks=int.knots,include.lowest=TRUE)
time.cat <- as.numeric(time.cat.lab)-1
degree <- c(degree,ltk,firstK) # ltk and firstK are passed to the HazardInt function through the 'degree' argument which is not used with NS
}
## For non time-dependent effects
n.ntd <- dim(fix.obs)[2]
if (n.ntd>1){
which.ntd <- c(1,(n.td.base+1)+1:(n.ntd-1))
}
else {
which.ntd <- 1
}
intercept <- "Intercept"
n.inter <- 1
}
## For time-dependent effects
n.td.nph <- length(names.nph)*n.td.base
if (n.td.nph>0){
which.td <- c(n.inter+1:n.td.base,(n.td.base+n.ntd)+1:n.td.nph)
}
else {
which.td <- c(n.inter+1:n.td.base)
}
names.nph <- unlist(sapply(names.nph,function(x){paste(x,names.base,sep="*")}))
param.names <- c(intercept,names.base,names.fix,names.nph)
n.par.fix <- n.td.base+n.ntd+n.td.nph
param.init <- rep(0,n.par.fix)
param.init[1:(n.td.base+n.inter)] <- -1
}
fix.obs <- t(fix.obs) # Matrix of fixed effects has to be transposed for use by the Int/Delta functions
nph.obs <- t(nph.obs) # Matrix of time-dependent effects has to be transposed for use by the Int/Delta functions
## Creation of objects related to the random effect
n.clust <- 1
n.rand <- 0
n.par <- n.td.base + n.ntd + n.td.nph
which.rdm <- NULL
if (!is.null(random)){
n.rand <- 1
n.par <- n.par + 1
which.rdm <- n.par
random.obs <- random.obs[Idx.Non.NA]
status.one <- which(status.obs==1)
lambda.pop.delta <- lambda.pop[status.one]
n.clust <- length(clust) # Number of clusters
n.by.clust <- as.vector(table(random.obs)) # Size of each cluster
n.by.clust.delta <- as.vector(table(random.obs[status.one]))
Idx.clust.no.obs <- which(n.by.clust==0)
if (length(Idx.clust.no.obs)>0){
warning("Some clusters had no non-missing values for some covariables. These observations were consequently removed...")
cat("The following clusters had no non-missing values for some covariables:\n")
print(names(table(random.obs))[Idx.clust.no.obs])
n.by.clust <- n.by.clust[-Idx.clust.no.obs]
n.by.clust.delta <- n.by.clust.delta[-Idx.clust.no.obs]
clust <- clust[-Idx.clust.no.obs]
n.clust <- length(clust)
}
parent.cst.adj <- rep(0,n.clust)
if (Survtype=="counting"){
parent.cst.adj0 <- rep(0,n.clust)
}
else {
parent.cst.adj0 <- NULL
}
param.names <- c(param.names,paste(random," [log(sd)]",sep=""))
## Creation of the points and weights of Gauss-Hermite quadrature
if (n.aghq<=0 | round(n.aghq,0)!=n.aghq){
stop("The 'n.aghq' argument must be a strictly positive integer.")
}
gq <- gauss.quad(n=n.aghq,kind="hermite")
x.H <- gq$nodes
x.H.2 <- x.H^2
log.rho.H <- log(gq$weights)
log.rho.H[log.rho.H==-Inf] <- -.Machine$double.xmax
}
## Initial values
if (!is.null(init)){
if (length(init)!=n.par){
print(data.frame(Time.Dep.baseline=n.td.base,Non.TD.Effects=n.ntd,TD.Effects=n.td.nph,Random.Effect=n.rand))
stop("Wrong number of init parameters",call.=FALSE)
}
}
else {
init <- c(param.init,rep(0.1,n.rand))
}
## Creation of variables that will be modified by the Hazard and LL.Tot functions
parent.neval <- 0
parent.param <- init
parent.logLik <- -.Machine$double.xmax
## Function that actually computes the log-likelihood
LL.Tot <- function(p.LT,mu.hat.LT=0,env=FALCenv){
p.td <- p.LT[which.td]
p.ntd <- p.LT[which.ntd]
temp.H <- HazardInt(x0=time.obs.0,x=time.obs,nph=nph.obs,timecat0=time.cat.0,timecat=time.cat,fixobs=fix.obs,param=p.td,paramf=p.ntd,deg=degree,n=gln,lw=lglw,matk=MatK,totk=vec.knots,intk=int.knots,nsadj1=NsAdj[[1]],nsadj2=NsAdj[[2]])
if (temp.H$Test){
res.LT <- .Machine$double.xmax
}
else {
if (!is.null(random)){
MH0 <- 0
var.w <- exp(2*p.LT[which.rdm])
if (Survtype=="counting"){
MH0 <- -lambertW0(unlist(lapply(listIdx,function(x){sum(temp.H$HazCum0[x])*var.w})))
}
temp.LT <- Frailty.Adapt(nodes=x.H, nodessquare=x.H.2, logweights=log.rho.H, clust=n.by.clust, clustd=n.by.clust.delta, expect=lambda.pop.delta, betal=temp.H$LogHaz[status.one], betaL0=temp.H$HazCum0, betaL=temp.H$HazCum, A0=parent.cst.adj0, A=parent.cst.adj, var=var.w, mh0=MH0, muhatcond=mu.hat.LT)
if (mu.hat.LT==1)
res.LT <- temp.LT$MuHat
else if (mu.hat.LT==2)
res.LT <- temp.LT$SigmaHat
else {
env$parent.cst.adj0 <- temp.LT$CstAdj0
env$parent.cst.adj <- temp.LT$CstAdj
res.LT <- temp.LT$LogLik
res.LT[is.nan(res.LT) | abs(res.LT)==Inf] <- .Machine$double.xmax
}
}
else {
if (withExp==1){
temp.l <- exp(temp.H$LogHaz)+lambda.pop
if (sum(temp.l-lambda.pop)==0){
res.LT <- .Machine$double.xmax
} # Prevents a kind of catastrophic cancellation
else {
log.lambda <- log(temp.l)
log.lambda[log.lambda==Inf] <- .Machine$double.xmax
res.LT <- sum(temp.H$HazCum - status.obs*log.lambda)
res.LT <- min(res.LT,.Machine$double.xmax)
}
}
else {
log.lambda <- temp.H$LogHaz
log.lambda[log.lambda==Inf] <- .Machine$double.xmax
res.LT <- sum(temp.H$HazCum - status.obs*log.lambda)
res.LT <- min(res.LT,.Machine$double.xmax)
}
}
if (mu.hat.LT==0){
if (sum(p.LT-parent.param)){
verbose.ll()
env$parent.neval <- parent.neval + 1
}
env$parent.param <- p.LT + 0 # Necessary...
env$parent.logLik <- res.LT
}
}
return(res.LT)
}
if (mode=="fit"){
## Launching the optimisation procedure
if (fnoptim=="nlm"){
mod.lik <- nlm(LL.Tot,init,iterlim=iterlim,hessian=TRUE,print.level=print.level,...)
param.fin <- mod.lik$estimate
code.fin <- mod.lik$code
loglik <- -mod.lik$minimum
gradient <- mod.lik$gradient
iterations <- mod.lik$iterations
}
else if (fnoptim=="optim"){
mod.lik <- optim(init,LL.Tot,method=method,hessian=TRUE,...)
param.fin <- mod.lik$par
code.fin <- mod.lik$convergence
loglik <- -mod.lik$value
gradient <- rep(NA,n.par)
iterations <- "---"
}
names(param.fin) <- param.names
## Hessian matrix
verbose.ll <- function(){} # Suppresses iteration printing
if (numHess){
cat("Computation of the Hessian\n")
hessian <- hessian(LL.Tot,param.fin,mu.hat.LT=0,method.args=list(r=4))
}
else {
hessian <- mod.lik$hessian
}
vcov <- try(solve(hessian),silent=TRUE)
if (is.character(vcov)){
vcov <- matrix(NA,n.par,n.par)
warning("Unable to invert the Hessian matrix...")
}
colnames(vcov) <- rownames(vcov) <- param.names
## Empirical Bayes Estimates
if (!is.null(random)) {
cat("Computation of the covariance matrix of the shrinkage estimators\n")
mu.hat.fin <- LL.Tot(param.fin,mu.hat.LT=1)
sigma.hat.fin <- LL.Tot(param.fin,mu.hat.LT=2)
deriv.mu.hat.fin <- jacobian(LL.Tot,param.fin,mu.hat.LT=1,method.args=list(r=4))
vcov.par.mu.hat <- vcov%*%t(deriv.mu.hat.fin)
var.mu.hat <- diag(sigma.hat.fin^2)+(deriv.mu.hat.fin%*%vcov.par.mu.hat)
mu.hat.df <- data.frame(cluster=clust,mu.hat=mu.hat.fin,sigma.hat=sigma.hat.fin)
vcov.fix.mu.hat <- vcov.par.mu.hat[-which.rdm,,drop=FALSE]
}
else {
mu.hat.df <- NA
var.mu.hat <- NA
vcov.fix.mu.hat <- NA
}
}
else if (mode=="eval"){
loglik <- LL.Tot(init)
gradient <- grad(LL.Tot,init,method.args=list(r=4))
hessian <- hessian(LL.Tot,init,mu.hat.LT=0,method.args=list(r=4))
param.fin <- init
names(param.fin) <- param.names
code.fin <- 9
iterations <- 0
vcov <- matrix(NA,n.par,n.par)
colnames(vcov) <- rownames(vcov) <- param.names
mu.hat.df <- NA
var.mu.hat <- NA
vcov.fix.mu.hat <- NA
if (!is.null(random)) {
mu.hat.fin <- LL.Tot(init,mu.hat.LT=1)
sigma.hat.fin <- LL.Tot(init,mu.hat.LT=2)
mu.hat.df <- data.frame(cluster=clust,mu.hat=mu.hat.fin,sigma.hat=sigma.hat.fin)
}
}
time1 <- as.numeric(proc.time()[3])
PrintData <- data.frame(Name=name.data,N.Obs.Tot=n.obs.tot,N.Obs=n.obs,N.Events=n.events,N.Clust=n.clust,row.names="")
PrintDetails <- data.frame(Iter=iterations,Eval=parent.neval+1,Base=base,Nb.Leg=n.gleg,
Nb.Aghq=n.aghq,Optim=fnoptim,Method=ifelse(fnoptim=="optim",method,"---"),
Code=code.fin,LogLik=loglik,Total.Time=(time1-time0),row.names="")
## Part of the results printed on screen
cat("\nData\n")
print(PrintData)
cat("\nDetails\n")
print(PrintDetails)
res.FAR <- list(dataset=name.data,
call=call,
formula=formula,
expected=ifelse(!is.null(expected),expected,NA),
xlevels=Xlevels,
n.obs.tot=n.obs.tot,
n.obs=n.obs,
n.events=n.events,
n.clust=ifelse(!is.null(random),n.clust,1),
n.time.0=ifelse(Survtype=="right",length(Idx.Time.0),0),
base=base,
max.time=max.time,
boundary.knots=c(BoI,BoS),
degree=degree[1],
knots=knots,
names.ph=names.fix,
random=ifelse(!is.null(random),random,NA),
init=init,
coefficients=param.fin,
std.errors=sqrt(diag(vcov)),
vcov=vcov,
gradient=gradient,
hessian=hessian,
mu.hat=mu.hat.df,
var.mu.hat=var.mu.hat,
vcov.fix.mu.hat=t(vcov.fix.mu.hat),
n.par=n.par,
n.gleg=n.gleg,
n.aghq=n.aghq,
fnoptim=fnoptim,
method=ifelse(fnoptim=="optim",method,NA),
code=code.fin,
loglik=loglik,
iter=iterations,
eval=parent.neval,
time.elapsed=time1-time0)
class(res.FAR) <- "mexhaz"
res.FAR
}
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