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R/dynpred.R
In lcmm: Extended Mixed Models Using Latent Classes and Latent Processes
Defines functions
dynpred
Documented in
dynpred
#' Individual dynamic predictions from a joint latent class model
#'
#' This function computes individual dynamic predictions and 95\% confidence
#' bands. Given a joint latent class model, a landmark time s, a horizon time t
#' and measurements until time s, the predicted probability of event in the
#' window [s,s+t] is calculated. Confidence bands can be provided using a Monte
#' Carlo method.
#'
#'
#' @param model an object inheriting from class \code{Jointlcmm}.
#' @param newdata a data frame containing the data from which predictions are
#' computed. This data frame must contain all the model's covariates, the
#' observations of the longitudinal and survival outcomes, the subject
#' identifier and if necessary the variables specified in prior and TimeDepVar
#' argumentsfrom Jointlcmm.
#' @param event integer giving the event for which the prediction is to be
#' calculated
#' @param landmark a numeric vector containing the landmark times.
#' @param horizon a numeric vector containing the horizon times.
#' @param var.time a character indicating the time variable in \code{newdata}
#' @param fun.time an optional function. This is only required if the time
#' scales in the longitudinal part of the model and the survival part are
#' different. In that case, \code{fun.time} is the function that translates the
#' times from the longitudinal part into the time scale of the survival part.
#' The default is the identity function which means that the two time scales
#' are the same.
#' @param na.action Integer indicating how NAs are managed. The default is 1
#' for 'na.omit'. The alternative is 2 for 'na.fail'. Other options such as
#' 'na.pass' or 'na.exclude' are not implemented in the current version.
#' @param draws optional boolean specifying whether median and confidence bands
#' of the predicted values should be computed (TRUE). IF TRUE, a Monte Carlo
#' approximation of the posterior distribution of the predicted values is
#' computed and the median, 2.5\% and 97.5\% percentiles are given. Otherwise,
#' the predicted values are computed at the point estimate. By default,
#' draws=FALSE.
#' @param ndraws if draws=TRUE, ndraws specifies the number of draws that
#' should be generated to approximate the posterior distribution of the
#' predicted values. By default, ndraws=2000.
#' @return A list containing : \item{pred}{a matrix with 4 columns if
#' draws=FALSE and 6 columns if draws=TRUE, containing the subjects identifier,
#' the landmark times, the horizon times, the predicted probability (if
#' draws=FALSE) or the median, 2.5\% and 97.5 \% percentiles of the 'ndraws'
#' probabilities calculated (if draws=TRUE). If a subject has no measurement
#' before time s or if the event has already occured at time s, his probability
#' is NA.} \item{newdata}{a data frame obtained from argument newdata
#' containing time measurements and longitudinal observations used to compute
#' the predictions}
#' @author Cecile Proust-Lima, Viviane Philipps
#' @seealso
#' \code{\link{plot.dynpred}}, \code{\link{Jointlcmm}}, \code{\link{predictY}}, \code{\link{plot.predict}}
#' @references Proust-Lima, Sene, Taylor and Jacqmin-Gadda (2014). Joint latent
#' class models of longitudinal and time-to-event data: a review. Statistical
#' Methods in Medical Research 23, 74-90.
#' @examples
#'
#'
#' ## Joint latent class model with 2 classes :
#' m32 <- Jointlcmm(Ydep1~Time*X1,mixture=~Time,random=~Time,subject="ID",
#' classmb=~X3,ng=2,survival=Surv(Tevent,Event)~X1+mixture(X2),
#' hazard="3-quant-splines",hazardtype="PH",data=data_lcmm,
#' B = c(0.641, -0.6217, 0, 0, 0.5045, 0.8115, -0.4316, 0.7798, 0.1027,
#' 0.7704, -0.0479, 10.4257, 11.2972, -2.5955, -0.5234, 1.4147,
#' -0.05, 0.9124, 0.0501, 0.2138, 1.5027))
#'
#' ## Predictions at landmark 10 and 12 for horizon 3, 5 and 10 for two subjects :
#'
#' dynpred(m32,landmark=c(10,12),horizon=c(3,5,10),var.time="Time",
#' fun.time=function(x){10*x},newdata=data_lcmm[1:8,])
#' \dontrun{
#' dynpred(m32,landmark=c(10,12),horizon=c(3,5,10),var.time="Time",
#' fun.time=function(x){10*x},newdata=data_lcmm[1:8,],draws=TRUE,ndraws=2000)
#' }
#'
#' @export
#'
#'
dynpred <- function(model,newdata,event=1,landmark,horizon,var.time,
fun.time=identity,na.action=1,draws=FALSE,ndraws=2000)
{
if(missing(model)) stop("The argument model must be specified")
if(!inherits(model,"Jointlcmm")) stop("The argument model must be a 'Jointlcmm' object")
if(missing(newdata)) stop("The argument newdata should be specified")
if (!inherits(newdata, "data.frame")) stop("newdata should be a data.frame object")
if(length(event)>1) stop("Please specify only one event")
if(!(event %in% c(1:(length(model$N)-9)))) stop("Argument 'event' is not correct")
if(missing(landmark)) stop("Please specify at least one landmark time")
if(missing(horizon)) stop("Please specify at least one horizon time")
if(any(horizon<=0)) stop("horizon must be positive times")
if(missing(var.time)) stop("The argument 'var.time' should be specified")
if(!is.character(var.time)) stop("'var.time' should be a character")
if(!(var.time %in% colnames(newdata))) stop("'var.time' should be a variable included in 'newdata'")
if(!is.function(fun.time)) stop("'fun.time' should be a function")
if(model$conv==1 | model$conv==2 | model$conv==3)
{
if(model$conv==2 & draws==TRUE)
{
cat("No confidence interval will be provided since the program did not converge properly \n")
draws <- FALSE
}
if(model$conv==3 & draws==TRUE)
{
cat("No confidence interval will be provided since the program did not converge properly \n")
draws <- FALSE
}
nbland <- length(landmark)
nbhoriz <- length(horizon)
nbevt <- length(model$N)-9
idprob <- model$idprob
idea <- model$idea
idg <- model$idg
idcor <- model$idcor
idcom <- model$idcom
idspecif <- matrix(model$idspecif,nbevt,length(idg),byrow=TRUE)
idspecif2 <- model$idspecif
idsurv <- (idcom!=0)+(apply(idspecif,2,function(x) any(x!=0)))
idtdv <- model$idtdv
idiag <- model$idiag
nv <- length(idprob)
nwg <- model$N[6]
ng <- model$ng
ncor <- model$N[7]
nz <- model$hazard[[4]]
zi <- model$hazard[[3]]
typrisq <- model$hazard[[1]]
risqcom <- model$hazard[[2]]
logspecif <- model$logspecif
nvdepsurv <- length(model$Names$TimeDepVar.name)
nvarxevt <- model$N[3]
best <- model$best
npm <- length(best)
nprisq <- sapply(1:nbevt, function(k) 2*(typrisq[k]==2)+(nz[k]-1)*(typrisq[k]==1)+(nz[k]+2)*(typrisq[k]==3))
nrisq <- (risqcom %in% c(1,"Common"))*nprisq + (risqcom %in% c(0,"Specific"))*nprisq*ng + (risqcom %in% c(2,"PH"))*(nprisq+ng-1)
zitr <- model$linknodes
nbzitr <- length(zitr)
idlink <- model$linktype
epsY <- model$epsY
for(ke in 1:nbevt)
{
if(risqcom[ke]=="Specific") risqcom[ke] <- 0
if(risqcom[ke]=="Common") risqcom[ke] <- 1
if(risqcom[ke]=="PH") risqcom[ke] <- 2
}
##mettre cholesky a la place de varcov des effets aleatoires
if(model$N[5]>0)
{
best[sum(model$N[1:4])+1:model$N[5]] <- na.omit(model$cholesky)
}
call_fixed <- model$call$fixed[3]
if(is.null(model$call$random)) {call_random <- -1} else call_random <- model$call$random[2]
if(is.null(model$call$classmb)) {call_classmb <- -1} else call_classmb <- model$call$classmb[2]
if(is.null(model$call$survival)) {call_survival <- -1} else call_survival <- model$call$survival[3]
call_survival <- gsub("mixture","",call_survival)
for(ke in 1:nbevt)
{
call_survival <- gsub(paste("cause",ke,sep=""),"",call_survival)
}
call_survival <- gsub("cause","",call_survival)
call_survival <- call(call_survival)
if(!(na.action%in%c(1,2))) stop("only 1 for 'na.omit' or 2 for 'na.fail' are required in na.action argument")
if(na.action==1)
{
na.action=na.omit
}
else
{
na.action=na.fail
}
modelNames <- c(model$Names$Xnames2, # variables expl (MM et survie)
model$Names$Yname, # nom de l'outcome du MM
model$Names$ID, # identifiant sujets
model$Names$prior.name) # nom prior
#model$Names$TimeDepVar.name) # nom TimeDepVar
if(length(model$Names$Tnames)>2)
{
modelNames <- c(modelNames,model$Names$Tnames[1]) #Tentry
}
if(model$Names$Xnames[1]=="intercept")
{
newdata1 <- cbind(intercept=rep(1,length=length(newdata[,1])),newdata[,setdiff(colnames(newdata),model$Names$Xnames[1]),drop=FALSE])
colnames(newdata1) <- c("intercept",setdiff(colnames(newdata),model$Names$Xnames[1]))
newdata1 <- data.frame(newdata1)
}
##verif des variables dans newdata1 #mettre des valeurs par defaut dans prior et TimedepVar ?
if(!all(modelNames %in% colnames(newdata1))) stop(paste(c("newdata should at least include the following covariates: ","\n",modelNames),collapse=" "))
## ordonner selon numero et temps
newdata1 <- newdata1[order(newdata1[,model$Names$ID],newdata1[,var.time]),,drop=FALSE] #est ce que fun.time peut modifier l'ordre????
### pour les facteurs
Xnames2 <- model$Names$Xnames2
## transform to factor is the variable appears in levels$levelsdata
for(v in colnames(newdata1))
{
if(v %in% names(model$levels$levelsdata))
{
if(!is.null(model$levels$levelsdata[[v]]))
{
newdata1[,v] <- factor(newdata1[,v], levels=model$levels$levelsdata[[v]])
}
}
}
## ##cas ou on a factor() dans l'appel
## z <- all.names(as.formula(paste("~",call_fixed)))
## ind_factor <- which(z=="factor")
## if(length(ind_factor))
## {
## nom.factor <- z[ind_factor+1]
## for (v in nom.factor)
## {
## mod <- levels(as.factor(olddata[,v]))
## if (!all(levels(as.factor(newdata1[,v])) %in% mod)) stop(paste("invalid level in factor", v))
## newdata1[,v] <- factor(newdata1[,v], levels=mod)
## fv <- paste("factor\\(",v,"\\)",sep="")
## if(length(grep(fv,model$Names$Xnames)))
## {
## model$Names$Xnames <- gsub(fv,v,model$Names$Xnames)
## }
## }
## }
call_fixed <- gsub("factor","",call_fixed)
## z <- all.names(as.formula(paste("~",call_random)))
## ind_factor <- which(z=="factor")
## if(length(ind_factor))
## {
## nom.factor <- z[ind_factor+1]
## for (v in nom.factor)
## {
## mod <- levels(as.factor(olddata[,v]))
## if (!all(levels(as.factor(newdata1[,v])) %in% mod)) stop(paste("invalid level in factor", v))
## newdata1[,v] <- factor(newdata1[,v], levels=mod)
## fv <- paste("factor\\(",v,"\\)",sep="")
## if(length(grep(fv,model$Names$Xnames)))
## {
## model$Names$Xnames <- gsub(fv,v,model$Names$Xnames)
## }
## }
## }
call_random <- gsub("factor","",call_random)
## z <- all.names(as.formula(paste("~",call_classmb)))
## ind_factor <- which(z=="factor")
## if(length(ind_factor))
## {
## nom.factor <- z[ind_factor+1]
## for (v in nom.factor)
## {
## mod <- levels(as.factor(olddata[,v]))
## if (!all(levels(as.factor(newdata1[,v])) %in% mod)) stop(paste("invalid level in factor", v))
## newdata1[,v] <- factor(newdata1[,v], levels=mod)
## fv <- paste("factor\\(",v,"\\)",sep="")
## if(length(grep(fv,model$Names$Xnames)))
## {
## model$Names$Xnames <- gsub(fv,v,model$Names$Xnames)
## }
## }
## }
call_classmb <- gsub("factor","",call_classmb)
## z <- all.names(as.formula(paste("~",call_survival)))
## ind_factor <- which(z=="factor")
## if(length(ind_factor))
## {
## nom.factor <- z[ind_factor+1]
## for (v in nom.factor)
## {
## mod <- levels(as.factor(olddata[,v]))
## if (!all(levels(as.factor(newdata1[,v])) %in% mod)) stop(paste("invalid level in factor", v))
## newdata1[,v] <- factor(newdata1[,v], levels=mod)
## fv <- paste("factor\\(",v,"\\)",sep="")
## if(length(grep(fv,model$Names$Xnames)))
## {
## model$Names$Xnames <- gsub(fv,v,model$Names$Xnames)
## }
## }
## }
call_survival <- gsub("factor","",call_survival)
### Traitement des donnees manquantes
mcall <- match.call()[c(1,match(c("data","subset","na.action"),names(match.call()),0))]
mcall$na.action <- na.action
mcall$data <- newdata1
## fixed
m <- mcall
m$formula <- formula(paste("~",call_fixed,sep=""))
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
na.fixed <- attr(m,"na.action")
## random
if(!is.null(model$call$random))
{
id.X_random <- 1
m <- mcall
m$formula <- formula(paste("~",call_random,sep=""))
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
na.random <- attr(m,"na.action")
}
else
{
id.X_random <- 0
na.random <- NULL
}
## classmb
if(!is.null(model$call$classmb))
{
id.X_classmb <- 1
m <- mcall
m$formula <- formula(paste("~",call_classmb,sep=""))
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
na.classmb <- attr(m,"na.action")
}
else
{
id.X_classmb <- 0
na.classmb <- NULL
}
##survival
if(!is.null(model$call$survival))
{
id.X_survival <- 1
m <- mcall
m$formula <- formula(paste("~",call_survival,sep=""))
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
na.survival <- attr(m,"na.action")
}
else
{
id.X_survival <- 0
na.survival <- NULL
}
##cor
na.cor <- NULL
if(model$N[7]>0)
{
z <- which(model$idcor==1)
var.cor <- newdata1[,model$Names$Xnames[z]]
na.cor <- which(is.na(var.cor))
}
##var.time
na.time <- NULL
if(!(var.time %in% model$Names[[1]]))
{
na.time <- which(is.na(newdata1[,var.time]))
}
## toutes les variables pas var expl
dtmp <- newdata1[,setdiff(modelNames,Xnames2),drop=FALSE]
m <- model.frame(formula=formula(paste(model$Names$Yname,"~",paste(setdiff(modelNames,Xnames2),collapse="+"),sep="")),data=dtmp,na.action=na.action)
na.other <- attr(m,"na.action")
## fun.time
if(length(match.call()$fun.time))
{
if(!isTRUE(as.character(match.call()[[which(names(match.call())=="fun.time")]])=="identity"))
{
z <- match.call()$fun.time
if(isTRUE(as.character(z) %in% ls(.GlobalEnv)))
{
bodyfun <- as.expression(body(get(as.character(z))))
}
else
{
bodyfun <- as.expression(z[[3]])
}
vars <- intersect(colnames(newdata1),all.names(bodyfun))
getvars <- paste("getElement(newdata1,'",vars,"')",sep="")
if(length(vars))
{
for(i in 1:length(vars))
{
bodyfun <- sub(vars[i],getvars[i],bodyfun)
bodyfun <- sub(as.character(match.call()$newdata),"",bodyfun)
tmp <- strsplit(bodyfun,split="")[[1]]
dollard <- which(tmp=="$")
if(length(dollard))
{
tmp <- tmp[-dollard]
}
bodyfun <- paste(tmp,collapse="")
}
}
headfun <- paste("function(",names(formals(fun.time)),")",collapse="")
ff1 <- paste(headfun,bodyfun)
ff2 <- parse(text=ff1)
ff3 <- eval(ff2)
timemes <- do.call("ff3",list(newdata1[,var.time]))
}
else
{
timemes <- newdata1[,var.time]
}
}
else
{
timemes <- newdata1[,var.time]
}
na.fun <- which(is.na(timemes))
## Table sans donnees manquante: newdata1
na.action <- unique(c(na.other,na.fixed,na.random,na.classmb,na.survival,na.cor,na.time,na.fun))
if(length(na.action))
{
newdata1 <- newdata1[-na.action,]
}
## si Tevent et Devent dans newdata
Tevent <- NULL
Devent <- NULL
if(length(model$Names$Tnames)==2) # ordre : Tevent,Event
{
if(all(model$Names$Tnames[1:2] %in% colnames(newdata1)))
{
Tevent <- newdata1[,model$Names$Tnames[1]]
Devent <- newdata1[,model$Names$Tnames[2]]
if(length(na.action))
{
Tevent <- Tevent[-na.action]
Devent <- Devent[-na.action]
}
indNA <- which(is.na(Tevent) | is.na(Devent))
if(length(indNA))
{
Tevent[indNA] <- 0
Devent[indNA] <- 0
}
}
}
if(length(model$Names$Tnames)==3) # ordre : Tentry,Tevent,Event
{
if(all(model$Names$Tnames[2:3] %in% colnames(newdata1)))
{
Tevent <- newdata1[,model$Names$Tnames[2]]
Devent <- newdata1[,model$Names$Tnames[3]]
if(!is.null(na.action))
{
Tevent <- Tevent[-na.action]
Devent <- Devent[-na.action]
}
indNA <- which(is.na(Tevent) | is.na(Devent))
if(length(indNA))
{
Tevent[indNA] <- 0
Devent[indNA] <- 0
}
}
}
## nb de sujets
id.subject <- newdata1[,model$Names$ID]
if(is.factor(id.subject)) id.subject <- factor(id.subject,levels=unique(id.subject))
ns <- length(unique(id.subject))
## vecteur Y et indiceY,uniqueY,nvalSPL
Y <- newdata1[,model$Names$Yname]
uniqueY <- 0
indiceY <- 0
nvalSPL <- 0
if(idlink == 2)
{
uniqueY <- sort(unique(Y))
permut <- order(order(Y)) # sort(y)[order(order(y))] = y
indice <- rep(1:length(uniqueY), as.vector(table(Y)))
indiceY <- indice[permut]
nvalSPL <- length(uniqueY)
}
## nb d'observations
nobs <- length(Y)
## nb de mesures par sujet
nmes <- as.vector(table(id.subject))
## prior
prior <- rep(0,ns)
if(length(model$Names$prior.name))
{
prior <- newdata1[cumsum(nmes),model$Names$prior.name]
}
## age entree si type = counting
tsurv0 <- rep(0,ns)
idtrunc <- 0
if(length(model$Names$Tnames)>2)
{
idtrunc <- 1
tsurv0 <- newdata1[cumsum(nmes),model$Names$Tnames[1]]
}
## reduire Tevent et Devent au nb de sujets
if(!is.null(Tevent))
{
Tevent <- Tevent[cumsum(nmes)]
Devent <- Devent[cumsum(nmes)]
}
## create one data frame for each formula (useful with factors)
newdata1fixed <- newdata1
for(v in colnames(newdata1fixed))
{
if(v %in% names(model$levels$levelsfixed))
{
if(!is.null(model$levels$levelsfixed[[v]]))
{
newdata1fixed[,v] <- factor(newdata1fixed[,v], levels=model$levels$levelsfixed[[v]])
if(any(is.na(newdata1fixed[,v]))) stop(paste("Wrong factor level in variable",v))
}
}
}
newdata1random <- newdata1
for(v in colnames(newdata1random))
{
if(v %in% names(model$levels$levelsrandom))
{
if(!is.null(model$levels$levelsrandom[[v]]))
{
newdata1random[,v] <- factor(newdata1random[,v], levels=model$levels$levelsrandom[[v]])
if(any(is.na(newdata1random[,v]))) stop(paste("Wrong factor level in variable",v))
}
}
}
newdata1classmb <- newdata1
for(v in colnames(newdata1classmb))
{
if(v %in% names(model$levels$levelsclassmb))
{
if(!is.null(model$levels$levelsclassmb[[v]]))
{
newdata1classmb[,v] <- factor(newdata1classmb[,v], levels=model$levels$levelsclassmb[[v]])
if(any(is.na(newdata1classmb[,v]))) stop(paste("Wrong factor level in variable",v))
}
}
}
newdata1surv <- newdata1
for(v in colnames(newdata1surv))
{
if(v %in% names(model$levels$levelssurv))
{
if(!is.null(model$levels$levelssurv[[v]]))
{
newdata1surv[,v] <- factor(newdata1surv[,v], levels=model$levels$levelssurv[[v]])
if(any(is.na(newdata1surv[,v]))) stop(paste("Wrong factor level in variable",v))
}
}
}
## Construction de nouvelles var explicatives sur la nouvelle table :
X_intercept <- model.matrix(~1,data=newdata1)
colnames(X_intercept) <- "intercept"
## fixed
X_fixed <- model.matrix(formula(paste("~",call_fixed,sep="")),data=newdata1fixed)
if(colnames(X_fixed)[1]=="(Intercept)")
{
X_fixed <- X_fixed[,-1,drop=FALSE]
}
## mixture pas besoin car les variables sont dans fixed
## random
if(!is.null(model$call$random))
{
X_random <- model.matrix(formula(paste("~",call_random,sep="")),data=newdata1random)
if(colnames(X_random)[1]=="(Intercept)")
{
colnames(X_random)[1] <- "intercept"
}
}
else
{
X_random <- NULL
}
## classmb
if(!is.null(model$call$classmb))
{
X_classmb <- model.matrix(formula(paste("~",call_classmb,sep="")),data=newdata1classmb)
colnames(X_classmb)[1] <- "intercept"
}
else
{
X_classmb <- NULL
}
## survival
if(!is.null(model$call$survival))
{
X_survival <- model.matrix(formula(paste("~",call_survival,sep="")),data=newdata1surv)
if(ncol(X_survival)) colnames(X_survival)[1] <- "intercept"
}
else
{
X_survival <- NULL
}
##cor
if(model$N[7]>0) #on reprend la variable de temps de cor
{
z <- which(model$idcor==1)
X_cor <- newdata1[,model$Names$Xnames[z]]
}
else
{
X_cor <- NULL
}
## var.time
if(length(na.action)) timemes <- timemes[-na.action]
# temps deja dans la bonne echelle
## Construction de X dans le bon ordre
X <- cbind(X_intercept,X_fixed)
colX <- c("intercept",colnames(X_fixed))
if(id.X_random == 1)
{
for(i in 1:length(colnames(X_random)))
{
if((colnames(X_random)[i] %in% colnames(X))==FALSE)
{
X <- cbind(X,X_random[,i])
colnames(X) <- c(colX,colnames(X_random)[i])
colX <- colnames(X)
}
}
}
if(id.X_classmb == 1)
{
for(i in 1:length(colnames(X_classmb)))
{
if((colnames(X_classmb)[i] %in% colnames(X))==FALSE)
{
X <- cbind(X,X_classmb[,i])
colnames(X) <- c(colX,colnames(X_classmb)[i])
colX <- colnames(X)
}
}
}
if(id.X_survival == 1)
{
for(i in 1:length(colnames(X_survival)))
{
if((colnames(X_survival)[i] %in% colnames(X))==FALSE)
{
X <- cbind(X,X_survival[,i])
colnames(X) <- c(colX,colnames(X_survival)[i])
colX <- colnames(X)
}
}
}
if(model$N[7]>0)
{
idspecif <- matrix(model$idspecif,nbevt,length(model$idg),byrow=TRUE)
if(model$idea[z]==0 & model$idprob[z]==0 & model$idg[z]==0 & model$idcom[z]==0 & all(idspecif[,z]==0))
{
X <- cbind(X,X_cor)
colnames(X) <- c(colX,model$Names$Xnames[z])
colX <- colnames(X)
}
}
## X <- cbind(X_intercept,X_fixed,X_random,X_classmb,X_survival,X_cor)
## colX <- strsplit(colnames(X),split=":",fixed=TRUE)
## colX <- lapply(colX,sort)
## colX <- lapply(colX,paste,collapse=":")
## colnames(X) <- unlist(colX)
## Xnames <- strsplit(model$Names$Xnames,split=":",fixed=TRUE)
## Xnames <- lapply(Xnames,sort)
## Xnames <- lapply(Xnames,paste,collapse=":")
## Xnames <- unlist(Xnames)
## browser()
## X <- X[,Xnames,drop=FALSE]
########## fonction de risque ##########
risq0 <- function(t,evt,typrisq,brisq,zi,nz,bPH,logspecif)
{
if(typrisq[evt]==2)
{
if(logspecif==1)
{
res <- brisq[2,evt]*brisq[1,evt]*t**(brisq[2,evt]-1)
}
if(logspecif==0)
{
res <- brisq[1,evt]*brisq[2,evt]*(brisq[1,evt]*t)**(brisq[2,evt]-1)
}
}
if(typrisq[evt]==1)
{
res <- NULL
for(i in 1:length(t))
{
zz <- zi[1:nz[evt],evt]
ordtzz <- order(c(t[i],zz))
itzz <- c(1,rep(0,length(zz)))
j <- which(itzz[ordtzz]==1)-1
if(t[i]==zz[1]) j <- 1
res <- c(res,brisq[j,evt])
}
}
if(typrisq[evt]==3)
{
res <- risq_spl(t=t,z=zi[1:nz[evt],evt],b=brisq[,evt])
}
return(res*bPH[evt])
}
########## fin fonction de risque
########### fonction de risque cumule ##########
risqcum0 <- function(t,evt,typrisq,brisq,zi,nz,bPH,logspecif)
{
if(typrisq[evt]==2)
{
if(logspecif==1)
{
res <- brisq[1,evt]*t**brisq[2,evt]
}
if(logspecif==0)
{
res <- (brisq[1,evt]*t)**brisq[2,evt]
}
}
if(typrisq[evt]==1)
{
res <- NULL
for(i in 1:length(t))
{
zz <- zi[1:nz[evt],evt]
ordtzz <- order(c(t[i],zz))
itzz <- c(1,rep(0,length(zz)))
if(t[i]==zz[1])
{
j <- 1
som <- 0
}
else
{
j <- which(itzz[ordtzz]==1)-1
if(j==1) som <- 0
else som <- sum(brisq[1:(j-1),evt]*diff(zz)[1:(j-1)])
}
res <- c(res,som+brisq[j,evt]*(t[i]-zz[j]))
}
}
if(typrisq[evt]==3)
{
res <- risqcum_spl(t=t,z=zi[1:nz[evt],evt],b=brisq[,evt])
}
return(res*bPH[evt])
}
########### fin fonction de risque cumule
#### fonction a integrer pour avoir l'incidence cumulee ####
fct_incid <- function(t,evt,typrisq,brisq,zi,nz,bPH,Xevt,bevt,nbevt,bevtint,logspecif)
{
risq_tevt <- risq0(t,evt,typrisq,brisq,zi,nz,bPH,logspecif)*exp(sum(Xevt*bevt[,evt]))*exp(bevtint[evt])
somme <- 0
for(ke in 1:nbevt)
{
somme <- somme + risqcum0(t,ke,typrisq,brisq,zi,nz,bPH,logspecif)*exp(sum(Xevt*bevt[,ke]))*exp(bevtint[ke])
}
return(risq_tevt * exp(-somme) )
}
######## fin fct incid
#### matrice Xevt a ns lignes
Xevt_ns <- X[cumsum(nmes),which(idsurv!=0),drop=FALSE]
### ok differents profils reperes
## fonction d'integration
integrate2 <- function(...) return(integrate(...)$value)
if(!isTRUE(draws))
{
## calcul des proba a posteriori pour tous les temps s
ppi <- rep(0,ns*nbland*ng)
out <- .Fortran(C_postprob2,
as.double(Y),
as.double(X),
as.integer(ns),
as.integer(nmes),
as.integer(nobs),
as.integer(ng),
as.integer(nv),
as.integer(idiag),
as.integer(nwg),
as.integer(ncor),
as.integer(logspecif),
as.double(zi),
as.integer(idea),
as.integer(idg),
as.integer(idprob),
as.integer(idcor),
as.integer(idcom),
as.integer(idspecif2),
as.integer(idtdv),
as.integer(risqcom),
as.integer(nvdepsurv),
as.integer(typrisq),
as.integer(nz),
as.integer(nprisq),
as.integer(nbzitr),
as.double(zitr),
as.integer(idlink),
as.integer(indiceY),
as.double(uniqueY),
as.integer(nvalSPL),
as.double(epsY),
as.integer(nbevt),
as.double(tsurv0),
as.integer(idtrunc),
as.double(best),
as.integer(npm),
as.double(timemes),
as.double(landmark),
as.integer(nbland),
ppi=as.double(ppi))
out$ppi[which(out$ppi==-5)] <- NA
ppi <- matrix(out$ppi,ns*nbland,ng)
## incid et surv a remplir
incid <- array(NA,c(ns*nbland,ng,length(horizon)))
#surv <- array(1,c(ns*nbland,ng))
## calcul de incidcum et survie
for(ks in 1:nbland)
{
for(i in 1:nrow(Xevt_ns))
{
## si sujet a 0 mesure, suivant
if(nmes[i]==0) next
if(nvdepsurv>0)
{
itimedepvar <- which(colnames(Xevt_ns)==model$Names$TimeDepVar.name)
tint <- Xevt_ns[i,itimedepvar]
Xevt <- as.vector(Xevt_ns[i,-itimedepvar])
}
else
{
Xevt <- as.vector(Xevt_ns[i,])
tint <- max(landmark)+1
}
brisqtot <- as.vector(best[model$N[1]+1:model$N[2]])
bvarxevt <- as.vector(best[sum(model$N[1:2])+1:model$N[3]])
for (g in 1:ng)
{
## coef pour la classe g
bevt <- matrix(0,length(which(idsurv!=0))-nvdepsurv,nbevt)
bevtint <- rep(0,nbevt)
l <- 0
kcurr <- 0
for(k in 1:length(idcom))
{
if(idsurv[k]==0) next
if(idcom[k]==1 & idspecif[1,k]==1)
{
if(idtdv[k]==1)
{
bevtint[1:nbevt] <- bvarxevt[kcurr+1]
}
else
{
l <- l+1
bevt[l,1:nbevt] <- bvarxevt[kcurr+1]
}
kcurr <- kcurr+1
}
if(idcom[k]==1 & idspecif[1,k]==2)
{
if(idtdv[k]==1)
{
bevtint[1:nbevt] <- bvarxevt[kcurr+g]
}
else
{
l <- l+1
bevt[l,1:nbevt] <- bvarxevt[kcurr+g]
}
kcurr <- kcurr+ng
}
if(idcom[k]==0)
{
if(idtdv[k]==1)
{
kecurr <- 0
for(ke in 1:nbevt)
{
if(idspecif[ke,k]==0)
{
bevtint[ke] <- 0
}
if(idspecif[ke,k]==1)
{
bevtint[ke] <- bvarxevt[kcurr+kecurr+1]
kecurr <- kecurr+1
}
if(idspecif[ke,k]==2)
{
bevtint[ke] <- bvarxevt[kcurr+kecurr+g]
kecurr <- kecurr+ng
}
}
kcurr <- kcurr+kecurr
}
else
{
l <- l+1
kecurr <- 0
for(ke in 1:nbevt)
{
if(idspecif[ke,k]==0)
{
bevt[l,ke] <- 0
}
if(idspecif[ke,k]==1)
{
bevt[l,ke] <- bvarxevt[kcurr+kecurr+1]
kecurr <- kecurr+1
}
if(idspecif[ke,k]==2)
{
bevt[l,ke] <- bvarxevt[kcurr+kecurr+g]
kecurr <- kecurr+ng
}
}
kcurr <- kcurr+kecurr
}
}
}
if(nrow(bevt)==0) #pas de variables dans survie
{
Xevt <- 0
bevt <- matrix(0,1,nbevt)
}
##brisq de la classe g
brisqtmp <- sapply(1:nbevt,function(k) brisqtot[sum(nrisq[1:k])-nrisq[k] + (g-1)*ifelse(risqcom[k] %in% c(0,"Specific"),nprisq[k],0) + 1:nprisq[k]])
if(is.matrix(brisqtmp))
{
if(logspecif==1) brisq <- exp(brisqtmp)
if(logspecif==0) brisq <- brisqtmp**2
}
if(is.list(brisqtmp))
{
lmax <- max(sapply(brisqtmp,length))
if(model$logspecif==1)
{
brisq <- lapply(brisqtmp, function(l) c(exp(l),rep(0,lmax-length(l))))
brisq <- matrix(unlist(brisq),nrow=lmax,ncol=nbevt)
}
if(model$logspecif==0)
{
brisq <- lapply(brisqtmp, function(l) c(l**2,rep(0,lmax-length(l))))
brisq <- matrix(unlist(brisq),nrow=lmax,ncol=nbevt)
}
}
## coef PH de la classe g
bPH <- rep(1,nbevt)
if(any(risqcom %in% c(2,"PH")))
{
if(g<ng)
{
bPH <- sapply(1:nbevt,function(k) ifelse(risqcom[k] %in% c(2,"PH"),brisqtot[sum(nrisq[1:k])-(ng-1)+g],1))
bPH <- exp(bPH)
}
}
## incidcum entre s et s+t (ou difference des survies)
if(nbevt==1)
{
bevtint2 <- bevtint
if(nvdepsurv!=0 & tint>landmark[ks]) bevtint2 <- rep(0,nbevt)
surv_s <- exp(-risqcum0(t=landmark[ks],evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1])))
surv_tint <- exp(-risqcum0(t=tint,evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1])))
if(nvdepsurv!=0 & tint<landmark[ks])
{
incid[ns*(ks-1)+i,g,] <- sapply(landmark[ks]+horizon,function(h) (surv_tint * (surv_s-surv_tint)*exp(bevtint[1])) - surv_tint*(exp(-risqcum0(t=h,evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1]))*exp(bevtint[1]))-surv_tint*exp(bevtint[1])))
}
else
{
incid[ns*(ks-1)+i,g,] <- sapply(landmark[ks]+horizon,function(h) surv_s-exp(-risqcum0(t=h,evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1]))))
}
}
else
{
for(ke in 1:nbevt)
{
bevtint2 <- bevtint
if(nvdepsurv!=0 & tint>landmark[ks]) bevtint2 <- rep(0,nbevt)
if(ke==event)
{
incid[ns*(ks-1)+i,g,] <- sapply(landmark[ks]+horizon,integrate2,f=fct_incid,lower=landmark[ks],evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,Xevt=Xevt,bevt=bevt,nbevt=nbevt,bevtint=bevtint2,logspecif=logspecif)
}
#if(nvdepsurv!=0 & tint<landmark[ks])
# {
# surv[ns*(ks-1)+i,g] <- surv[ns*(ks-1)+i,g]*exp(-risqcum0(t=tint,evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke])))*(exp(-risqcum0(t=landmark[ks],evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke]))*exp(bevtint[ke]))-exp(-risqcum0(t=tint,evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke]))*exp(bevtint[ke])))
# }
#else
# {
# surv[ns*(ks-1)+i,g] <- surv[ns*(ks-1)+i,g]*exp(-risqcum0(t=landmark[ks],evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke])))
# }
}
}
} # fin boucle g
}# fin boucle i
} # fin boucle ks
res <- data.frame(rep(sort(unique(id.subject)),each=nbland*nbhoriz))
res <- cbind(res,rep(landmark,each=nbhoriz))
res <- cbind(res,horizon)
res <- cbind(res,NA)
colnames(res) <- c(model$Names$ID,"landmark","horizon","pred")
pred <- NULL
#pred_g <- incid*array(ppi/surv,c(ns*nbland,ng,nbhoriz))
pred_g <- incid*array(ppi,c(ns*nbland,ng,nbhoriz))
pred_evt <- apply(pred_g,c(1,3),sum) # res de ie horiz dans ie colonne
pred_evt <- pred_evt[order(rep(1:ns,nbland)),]
pred_evt <- as.vector(t(pred_evt))
pred <- c(pred,pred_evt)
res[,4] <- pred
## mettre NA si Devent!=0 et Tevent<s
if(!is.null(Tevent))
{
matevt <- cbind(rep(Tevent,each=nbland*nbhoriz),rep(Devent,each=nbland*nbhoriz))
indevt <- which( (matevt[,2]!=0) & (matevt[,1]<res[,"landmark"]) )
if(length(indevt)) res[indevt,"pred"] <- NA
}
}
else # ie avec draws
{
posfix <- eval(model$call$posfix)
Mat <- matrix(0,ncol=npm,nrow=npm)
Mat[upper.tri(Mat,diag=TRUE)]<- model$V
if(length(posfix))
{
Mat2 <- Mat[-posfix,-posfix]
Chol2 <- chol(Mat2)
Chol <- matrix(0,npm,npm)
Chol[setdiff(1:npm,posfix),setdiff(1:npm,posfix)] <- Chol2
Chol <- t(Chol)
}
else
{
Chol <- chol(Mat)
Chol <- t(Chol)
}
doOneDraw <- function()
{
bdraw <- rnorm(npm)
bdraw <- best + Chol %*% bdraw
## calcul des proba a posteriori pour tous les temps s
ppi <- rep(0,ns*nbland*ng)
out <- .Fortran(C_postprob2,
as.double(Y),
as.double(X),
as.integer(ns),
as.integer(nmes),
as.integer(nobs),
as.integer(ng),
as.integer(nv),
as.integer(idiag),
as.integer(nwg),
as.integer(ncor),
as.integer(logspecif),
as.double(zi),
as.integer(idea),
as.integer(idg),
as.integer(idprob),
as.integer(idcor),
as.integer(idcom),
as.integer(idspecif2),
as.integer(idtdv),
as.integer(risqcom),
as.integer(nvdepsurv),
as.integer(typrisq),
as.integer(nz),
as.integer(nprisq),
as.integer(nbzitr),
as.double(zitr),
as.integer(idlink),
as.integer(indiceY),
as.double(uniqueY),
as.integer(nvalSPL),
as.double(epsY),
as.integer(nbevt),
as.double(tsurv0),
as.integer(idtrunc),
as.double(bdraw),
as.integer(npm),
as.double(timemes),
as.double(landmark),
as.integer(nbland),
ppi=as.double(ppi))
out$ppi[which(out$ppi==-5)] <- NA
if(all(is.na(out$ppi))) return(rep(NA,ns*nbland*nbhoriz*nbevt))
ppi <- matrix(out$ppi,ns*nbland,ng)
## incid et surv a remplir
incid <- array(NA,c(ns*nbland,ng,length(horizon)))
#surv <- array(1,c(ns*nbland,ng))
## calcul de incidcum et survie
for(ks in 1:nbland)
{
for(i in 1:nrow(Xevt_ns))
{
## si sujet a 0 mesure,suivant
if(nmes[i]==0) next
if(nvdepsurv>0)
{
itimedepvar <- which(colnames(Xevt_ns)==model$Names$TimeDepVar.name)
tint <- Xevt_ns[i,itimedepvar]
Xevt <- as.vector(Xevt_ns[i,-itimedepvar])
}
else
{
Xevt <- as.vector(Xevt_ns[i,])
tint <- max(landmark)+1
}
brisqtot <- as.vector(bdraw[model$N[1]+1:model$N[2]])
bvarxevt <- as.vector(bdraw[sum(model$N[1:2])+1:model$N[3]])
for (g in 1:ng)
{
## coef pour la classe g
bevt <- matrix(0,length(which(idsurv!=0))-nvdepsurv,nbevt)
bevtint <- rep(0,nbevt)
l <- 0
kcurr <- 0
for(k in 1:length(idcom))
{
if(idsurv[k]==0) next
if(idcom[k]==1 & idspecif[1,k]==1)
{
if(idtdv[k]==1)
{
bevtint[1:nbevt] <- bvarxevt[kcurr+1]
}
else
{
l <- l+1
bevt[l,1:nbevt] <- bvarxevt[kcurr+1]
}
kcurr <- kcurr+1
}
if(idcom[k]==1 & idspecif[1,k]==2)
{
if(idtdv[k]==1)
{
bevtint[1:nbevt] <- bvarxevt[kcurr+g]
}
else
{
l <- l+1
bevt[l,1:nbevt] <- bvarxevt[kcurr+g]
}
kcurr <- kcurr+ng
}
if(idcom[k]==0)
{
if(idtdv[k]==1)
{
kecurr <- 0
for(ke in 1:nbevt)
{
if(idspecif[ke,k]==0)
{
bevtint[ke] <- 0
}
if(idspecif[ke,k]==1)
{
bevtint[ke] <- bvarxevt[kcurr+kecurr+1]
kecurr <- kecurr+1
}
if(idspecif[ke,k]==2)
{
bevtint[ke] <- bvarxevt[kcurr+kecurr+g]
kecurr <- kecurr+ng
}
}
kcurr <- kcurr+kecurr
}
else
{
l <- l+1
kecurr <- 0
for(ke in 1:nbevt)
{
if(idspecif[ke,k]==0)
{
bevt[l,ke] <- 0
}
if(idspecif[ke,k]==1)
{
bevt[l,ke] <- bvarxevt[kcurr+kecurr+1]
kecurr <- kecurr+1
}
if(idspecif[ke,k]==2)
{
bevt[l,ke] <- bvarxevt[kcurr+kecurr+g]
kecurr <- kecurr+ng
}
}
kcurr <- kcurr+kecurr
}
}
}
if(nrow(bevt)==0) #pas de variables dans survie
{
Xevt <- 0
bevt <- matrix(0,1,nbevt)
}
##brisq de la classe g
brisqtmp <- sapply(1:nbevt,function(k) brisqtot[sum(nrisq[1:k])-nrisq[k] + (g-1)*ifelse(risqcom[k] %in% c(0,"Specific"),nprisq[k],0) + 1:nprisq[k]])
if(is.matrix(brisqtmp))
{
if(logspecif==1) brisq <- exp(brisqtmp)
if(logspecif==0) brisq <- brisqtmp**2
}
if(is.list(brisqtmp))
{
lmax <- max(sapply(brisqtmp,length))
if(model$logspecif==1)
{
brisq <- lapply(brisqtmp, function(l) c(exp(l),rep(0,lmax-length(l))))
brisq <- matrix(unlist(brisq),nrow=lmax,ncol=nbevt)
}
if(model$logspecif==0)
{
brisq <- lapply(brisqtmp, function(l) c(l**2,rep(0,lmax-length(l))))
brisq <- matrix(unlist(brisq),nrow=lmax,ncol=nbevt)
}
}
## coef PH de la classe g
bPH <- rep(1,nbevt)
if(any(risqcom %in% c(2,"PH")))
{
if(g<ng)
{
bPH <- sapply(1:nbevt,function(k) ifelse(risqcom[k] %in% c(2,"PH"),brisqtot[sum(nrisq[1:k])-(ng-1)+g],1))
bPH <- exp(bPH)
}
}
## incidcum entre s et s+t et survie en s
if(nbevt==1)
{
bevtint2 <- bevtint
if(nvdepsurv!=0 & tint>landmark[ks]) bevtint2 <- rep(0,nbevt)
surv_s <- exp(-risqcum0(t=landmark[ks],evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1])))
surv_tint <- exp(-risqcum0(t=tint,evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1])))
if(nvdepsurv!=0 & tint<landmark[ks])
{
incid[ns*(ks-1)+i,g,] <- sapply(landmark[ks]+horizon,function(h) (surv_tint * (surv_s-surv_tint)*exp(bevtint[1])) - surv_tint*(exp(-risqcum0(t=h,evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1]))*exp(bevtint[1]))-surv_tint*exp(bevtint[1])))
}
else
{
incid[ns*(ks-1)+i,g,] <- sapply(landmark[ks]+horizon,function(h) surv_s-exp(-risqcum0(t=h,evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1]))))
}
}
else
{
for(ke in 1:nbevt)
{
bevtint2 <- bevtint
if(nvdepsurv!=0 & tint>landmark[ks]) bevtint2 <- rep(0,nbevt)
if(ke==event)
{
incid[ns*(ks-1)+i,g,] <- sapply(landmark[ks]+horizon,integrate2,f=fct_incid,lower=landmark[ks],evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,Xevt=Xevt,bevt=bevt,nbevt=nbevt,bevtint=bevtint2,logspecif=logspecif)
}
#if(nvdepsurv!=0 & tint<landmark[ks])
# {
# surv[ns*(ks-1)+i,g] <- surv[ns*(ks-1)+i,g]*exp(-risqcum0(t=tint,evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke])))*(exp(-risqcum0(t=landmark[ks],evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke]))*exp(bevtint[ke]))-exp(-risqcum0(t=tint,evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke]))*exp(bevtint[ke])))
# }
#else
# {
# surv[ns*(ks-1)+i,g] <- surv[ns*(ks-1)+i,g]*exp(-risqcum0(t=landmark[ks],evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke])))
# }
}
}
} # fin boucle g
}# fin boucle i
} # fin boucle ks
res <- data.frame(rep(sort(unique(id.subject)),each=nbland*nbhoriz))
res <- cbind(res,rep(landmark,each=nbhoriz))
res <- cbind(res,horizon)
res <- cbind(res,NA)
colnames(res) <- c(model$Names$ID,"landmark","horizon","pred")
pred <- NULL
#pred_g <- incid*array(ppi/surv,c(ns*nbland,ng,nbhoriz))
pred_g <- incid*array(ppi,c(ns*nbland,ng,nbhoriz))
pred_evt <- apply(pred_g,c(1,3),sum) # res de ie horiz dans ie colonne
pred_evt <- pred_evt[order(rep(1:ns,nbland)),]
pred_evt <- as.vector(t(pred_evt))
pred <- c(pred,pred_evt)
res[,4] <- pred
## mettre NA si Devent!=0 et Tevent<s
if(!is.null(Tevent))
{
matevt <- cbind(rep(Tevent,each=nbland*nbhoriz),rep(Devent,each=nbland*nbhoriz))
indevt <- which( (matevt[,2]!=0) & (matevt[,1]<res[,"landmark"]) )
if(length(indevt)) res[indevt,"pred"] <- NA
}
return(res[,"pred"])
}
ndraws <- as.integer(ndraws)
resdraws <- replicate(ndraws,doOneDraw())
if(is.vector(resdraws))
{
resdraws <- matrix(resdraws,nrow=1)
}
#probs <- resdraws[-1,,drop=FALSE]
#pb <- sum(resdraws[1,])
#if(pb>0) warning("Infinite probabilities have been found. Confidence intervals are based on ", ndraws-pb ," simulations.")
med <- apply(resdraws,1,median,na.rm=TRUE)
qmin <- apply(resdraws,1,quantile,prob=0.025,na.rm=TRUE)
qmax <- apply(resdraws,1,quantile,prob=0.975,na.rm=TRUE)
res <- data.frame(rep(sort(unique(id.subject)),each=nbland*nbhoriz))
res <- cbind(res,rep(landmark,each=nbhoriz))
res <- cbind(res,horizon)
res <- cbind(res,med,qmin,qmax)
colnames(res) <- c(model$Names$ID,"landmark","horizon","pred_50","pred_2.5","pred_97.5")
}
}
else # ie conv != 1 ou 2 ou 3
{
cat("Output can not be produced since the program stopped abnormally. \n")
res <- NA
id.subject <- NA
nmes <- NA
Y <- NA
timemes <- NA
}
prmatrix(res,quote=FALSE)
res.list <- list(pred=res,newdata=data.frame(id=rep(unique(id.subject),nmes)[which(timemes<=max(landmark))],y=Y[which(timemes<=max(landmark))],time=timemes[which(timemes<=max(landmark))]))
class(res.list) <- "dynpred"
return(invisible(res.list))
}
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Defines functions dynpred
Documented in dynpred
#' Individual dynamic predictions from a joint latent class model
#'
#' This function computes individual dynamic predictions and 95\% confidence
#' bands. Given a joint latent class model, a landmark time s, a horizon time t
#' and measurements until time s, the predicted probability of event in the
#' window [s,s+t] is calculated. Confidence bands can be provided using a Monte
#' Carlo method.
#'
#'
#' @param model an object inheriting from class \code{Jointlcmm}.
#' @param newdata a data frame containing the data from which predictions are
#' computed. This data frame must contain all the model's covariates, the
#' observations of the longitudinal and survival outcomes, the subject
#' identifier and if necessary the variables specified in prior and TimeDepVar
#' argumentsfrom Jointlcmm.
#' @param event integer giving the event for which the prediction is to be
#' calculated
#' @param landmark a numeric vector containing the landmark times.
#' @param horizon a numeric vector containing the horizon times.
#' @param var.time a character indicating the time variable in \code{newdata}
#' @param fun.time an optional function. This is only required if the time
#' scales in the longitudinal part of the model and the survival part are
#' different. In that case, \code{fun.time} is the function that translates the
#' times from the longitudinal part into the time scale of the survival part.
#' The default is the identity function which means that the two time scales
#' are the same.
#' @param na.action Integer indicating how NAs are managed. The default is 1
#' for 'na.omit'. The alternative is 2 for 'na.fail'. Other options such as
#' 'na.pass' or 'na.exclude' are not implemented in the current version.
#' @param draws optional boolean specifying whether median and confidence bands
#' of the predicted values should be computed (TRUE). IF TRUE, a Monte Carlo
#' approximation of the posterior distribution of the predicted values is
#' computed and the median, 2.5\% and 97.5\% percentiles are given. Otherwise,
#' the predicted values are computed at the point estimate. By default,
#' draws=FALSE.
#' @param ndraws if draws=TRUE, ndraws specifies the number of draws that
#' should be generated to approximate the posterior distribution of the
#' predicted values. By default, ndraws=2000.
#' @return A list containing : \item{pred}{a matrix with 4 columns if
#' draws=FALSE and 6 columns if draws=TRUE, containing the subjects identifier,
#' the landmark times, the horizon times, the predicted probability (if
#' draws=FALSE) or the median, 2.5\% and 97.5 \% percentiles of the 'ndraws'
#' probabilities calculated (if draws=TRUE). If a subject has no measurement
#' before time s or if the event has already occured at time s, his probability
#' is NA.} \item{newdata}{a data frame obtained from argument newdata
#' containing time measurements and longitudinal observations used to compute
#' the predictions}
#' @author Cecile Proust-Lima, Viviane Philipps
#' @seealso
#' \code{\link{plot.dynpred}}, \code{\link{Jointlcmm}}, \code{\link{predictY}}, \code{\link{plot.predict}}
#' @references Proust-Lima, Sene, Taylor and Jacqmin-Gadda (2014). Joint latent
#' class models of longitudinal and time-to-event data: a review. Statistical
#' Methods in Medical Research 23, 74-90.
#' @examples
#'
#'
#' ## Joint latent class model with 2 classes :
#' m32 <- Jointlcmm(Ydep1~Time*X1,mixture=~Time,random=~Time,subject="ID",
#' classmb=~X3,ng=2,survival=Surv(Tevent,Event)~X1+mixture(X2),
#' hazard="3-quant-splines",hazardtype="PH",data=data_lcmm,
#' B = c(0.641, -0.6217, 0, 0, 0.5045, 0.8115, -0.4316, 0.7798, 0.1027,
#' 0.7704, -0.0479, 10.4257, 11.2972, -2.5955, -0.5234, 1.4147,
#' -0.05, 0.9124, 0.0501, 0.2138, 1.5027))
#'
#' ## Predictions at landmark 10 and 12 for horizon 3, 5 and 10 for two subjects :
#'
#' dynpred(m32,landmark=c(10,12),horizon=c(3,5,10),var.time="Time",
#' fun.time=function(x){10*x},newdata=data_lcmm[1:8,])
#' \dontrun{
#' dynpred(m32,landmark=c(10,12),horizon=c(3,5,10),var.time="Time",
#' fun.time=function(x){10*x},newdata=data_lcmm[1:8,],draws=TRUE,ndraws=2000)
#' }
#'
#' @export
#'
#'
dynpred <- function(model,newdata,event=1,landmark,horizon,var.time,
fun.time=identity,na.action=1,draws=FALSE,ndraws=2000)
{
if(missing(model)) stop("The argument model must be specified")
if(!inherits(model,"Jointlcmm")) stop("The argument model must be a 'Jointlcmm' object")
if(missing(newdata)) stop("The argument newdata should be specified")
if (!inherits(newdata, "data.frame")) stop("newdata should be a data.frame object")
if(length(event)>1) stop("Please specify only one event")
if(!(event %in% c(1:(length(model$N)-9)))) stop("Argument 'event' is not correct")
if(missing(landmark)) stop("Please specify at least one landmark time")
if(missing(horizon)) stop("Please specify at least one horizon time")
if(any(horizon<=0)) stop("horizon must be positive times")
if(missing(var.time)) stop("The argument 'var.time' should be specified")
if(!is.character(var.time)) stop("'var.time' should be a character")
if(!(var.time %in% colnames(newdata))) stop("'var.time' should be a variable included in 'newdata'")
if(!is.function(fun.time)) stop("'fun.time' should be a function")
if(model$conv==1 | model$conv==2 | model$conv==3)
{
if(model$conv==2 & draws==TRUE)
{
cat("No confidence interval will be provided since the program did not converge properly \n")
draws <- FALSE
}
if(model$conv==3 & draws==TRUE)
{
cat("No confidence interval will be provided since the program did not converge properly \n")
draws <- FALSE
}
nbland <- length(landmark)
nbhoriz <- length(horizon)
nbevt <- length(model$N)-9
idprob <- model$idprob
idea <- model$idea
idg <- model$idg
idcor <- model$idcor
idcom <- model$idcom
idspecif <- matrix(model$idspecif,nbevt,length(idg),byrow=TRUE)
idspecif2 <- model$idspecif
idsurv <- (idcom!=0)+(apply(idspecif,2,function(x) any(x!=0)))
idtdv <- model$idtdv
idiag <- model$idiag
nv <- length(idprob)
nwg <- model$N[6]
ng <- model$ng
ncor <- model$N[7]
nz <- model$hazard[[4]]
zi <- model$hazard[[3]]
typrisq <- model$hazard[[1]]
risqcom <- model$hazard[[2]]
logspecif <- model$logspecif
nvdepsurv <- length(model$Names$TimeDepVar.name)
nvarxevt <- model$N[3]
best <- model$best
npm <- length(best)
nprisq <- sapply(1:nbevt, function(k) 2*(typrisq[k]==2)+(nz[k]-1)*(typrisq[k]==1)+(nz[k]+2)*(typrisq[k]==3))
nrisq <- (risqcom %in% c(1,"Common"))*nprisq + (risqcom %in% c(0,"Specific"))*nprisq*ng + (risqcom %in% c(2,"PH"))*(nprisq+ng-1)
zitr <- model$linknodes
nbzitr <- length(zitr)
idlink <- model$linktype
epsY <- model$epsY
for(ke in 1:nbevt)
{
if(risqcom[ke]=="Specific") risqcom[ke] <- 0
if(risqcom[ke]=="Common") risqcom[ke] <- 1
if(risqcom[ke]=="PH") risqcom[ke] <- 2
}
##mettre cholesky a la place de varcov des effets aleatoires
if(model$N[5]>0)
{
best[sum(model$N[1:4])+1:model$N[5]] <- na.omit(model$cholesky)
}
call_fixed <- model$call$fixed[3]
if(is.null(model$call$random)) {call_random <- -1} else call_random <- model$call$random[2]
if(is.null(model$call$classmb)) {call_classmb <- -1} else call_classmb <- model$call$classmb[2]
if(is.null(model$call$survival)) {call_survival <- -1} else call_survival <- model$call$survival[3]
call_survival <- gsub("mixture","",call_survival)
for(ke in 1:nbevt)
{
call_survival <- gsub(paste("cause",ke,sep=""),"",call_survival)
}
call_survival <- gsub("cause","",call_survival)
call_survival <- call(call_survival)
if(!(na.action%in%c(1,2))) stop("only 1 for 'na.omit' or 2 for 'na.fail' are required in na.action argument")
if(na.action==1)
{
na.action=na.omit
}
else
{
na.action=na.fail
}
modelNames <- c(model$Names$Xnames2, # variables expl (MM et survie)
model$Names$Yname, # nom de l'outcome du MM
model$Names$ID, # identifiant sujets
model$Names$prior.name) # nom prior
#model$Names$TimeDepVar.name) # nom TimeDepVar
if(length(model$Names$Tnames)>2)
{
modelNames <- c(modelNames,model$Names$Tnames[1]) #Tentry
}
if(model$Names$Xnames[1]=="intercept")
{
newdata1 <- cbind(intercept=rep(1,length=length(newdata[,1])),newdata[,setdiff(colnames(newdata),model$Names$Xnames[1]),drop=FALSE])
colnames(newdata1) <- c("intercept",setdiff(colnames(newdata),model$Names$Xnames[1]))
newdata1 <- data.frame(newdata1)
}
##verif des variables dans newdata1 #mettre des valeurs par defaut dans prior et TimedepVar ?
if(!all(modelNames %in% colnames(newdata1))) stop(paste(c("newdata should at least include the following covariates: ","\n",modelNames),collapse=" "))
## ordonner selon numero et temps
newdata1 <- newdata1[order(newdata1[,model$Names$ID],newdata1[,var.time]),,drop=FALSE] #est ce que fun.time peut modifier l'ordre????
### pour les facteurs
Xnames2 <- model$Names$Xnames2
## transform to factor is the variable appears in levels$levelsdata
for(v in colnames(newdata1))
{
if(v %in% names(model$levels$levelsdata))
{
if(!is.null(model$levels$levelsdata[[v]]))
{
newdata1[,v] <- factor(newdata1[,v], levels=model$levels$levelsdata[[v]])
}
}
}
## ##cas ou on a factor() dans l'appel
## z <- all.names(as.formula(paste("~",call_fixed)))
## ind_factor <- which(z=="factor")
## if(length(ind_factor))
## {
## nom.factor <- z[ind_factor+1]
## for (v in nom.factor)
## {
## mod <- levels(as.factor(olddata[,v]))
## if (!all(levels(as.factor(newdata1[,v])) %in% mod)) stop(paste("invalid level in factor", v))
## newdata1[,v] <- factor(newdata1[,v], levels=mod)
## fv <- paste("factor\\(",v,"\\)",sep="")
## if(length(grep(fv,model$Names$Xnames)))
## {
## model$Names$Xnames <- gsub(fv,v,model$Names$Xnames)
## }
## }
## }
call_fixed <- gsub("factor","",call_fixed)
## z <- all.names(as.formula(paste("~",call_random)))
## ind_factor <- which(z=="factor")
## if(length(ind_factor))
## {
## nom.factor <- z[ind_factor+1]
## for (v in nom.factor)
## {
## mod <- levels(as.factor(olddata[,v]))
## if (!all(levels(as.factor(newdata1[,v])) %in% mod)) stop(paste("invalid level in factor", v))
## newdata1[,v] <- factor(newdata1[,v], levels=mod)
## fv <- paste("factor\\(",v,"\\)",sep="")
## if(length(grep(fv,model$Names$Xnames)))
## {
## model$Names$Xnames <- gsub(fv,v,model$Names$Xnames)
## }
## }
## }
call_random <- gsub("factor","",call_random)
## z <- all.names(as.formula(paste("~",call_classmb)))
## ind_factor <- which(z=="factor")
## if(length(ind_factor))
## {
## nom.factor <- z[ind_factor+1]
## for (v in nom.factor)
## {
## mod <- levels(as.factor(olddata[,v]))
## if (!all(levels(as.factor(newdata1[,v])) %in% mod)) stop(paste("invalid level in factor", v))
## newdata1[,v] <- factor(newdata1[,v], levels=mod)
## fv <- paste("factor\\(",v,"\\)",sep="")
## if(length(grep(fv,model$Names$Xnames)))
## {
## model$Names$Xnames <- gsub(fv,v,model$Names$Xnames)
## }
## }
## }
call_classmb <- gsub("factor","",call_classmb)
## z <- all.names(as.formula(paste("~",call_survival)))
## ind_factor <- which(z=="factor")
## if(length(ind_factor))
## {
## nom.factor <- z[ind_factor+1]
## for (v in nom.factor)
## {
## mod <- levels(as.factor(olddata[,v]))
## if (!all(levels(as.factor(newdata1[,v])) %in% mod)) stop(paste("invalid level in factor", v))
## newdata1[,v] <- factor(newdata1[,v], levels=mod)
## fv <- paste("factor\\(",v,"\\)",sep="")
## if(length(grep(fv,model$Names$Xnames)))
## {
## model$Names$Xnames <- gsub(fv,v,model$Names$Xnames)
## }
## }
## }
call_survival <- gsub("factor","",call_survival)
### Traitement des donnees manquantes
mcall <- match.call()[c(1,match(c("data","subset","na.action"),names(match.call()),0))]
mcall$na.action <- na.action
mcall$data <- newdata1
## fixed
m <- mcall
m$formula <- formula(paste("~",call_fixed,sep=""))
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
na.fixed <- attr(m,"na.action")
## random
if(!is.null(model$call$random))
{
id.X_random <- 1
m <- mcall
m$formula <- formula(paste("~",call_random,sep=""))
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
na.random <- attr(m,"na.action")
}
else
{
id.X_random <- 0
na.random <- NULL
}
## classmb
if(!is.null(model$call$classmb))
{
id.X_classmb <- 1
m <- mcall
m$formula <- formula(paste("~",call_classmb,sep=""))
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
na.classmb <- attr(m,"na.action")
}
else
{
id.X_classmb <- 0
na.classmb <- NULL
}
##survival
if(!is.null(model$call$survival))
{
id.X_survival <- 1
m <- mcall
m$formula <- formula(paste("~",call_survival,sep=""))
m[[1]] <- as.name("model.frame")
m <- eval(m, sys.parent())
na.survival <- attr(m,"na.action")
}
else
{
id.X_survival <- 0
na.survival <- NULL
}
##cor
na.cor <- NULL
if(model$N[7]>0)
{
z <- which(model$idcor==1)
var.cor <- newdata1[,model$Names$Xnames[z]]
na.cor <- which(is.na(var.cor))
}
##var.time
na.time <- NULL
if(!(var.time %in% model$Names[[1]]))
{
na.time <- which(is.na(newdata1[,var.time]))
}
## toutes les variables pas var expl
dtmp <- newdata1[,setdiff(modelNames,Xnames2),drop=FALSE]
m <- model.frame(formula=formula(paste(model$Names$Yname,"~",paste(setdiff(modelNames,Xnames2),collapse="+"),sep="")),data=dtmp,na.action=na.action)
na.other <- attr(m,"na.action")
## fun.time
if(length(match.call()$fun.time))
{
if(!isTRUE(as.character(match.call()[[which(names(match.call())=="fun.time")]])=="identity"))
{
z <- match.call()$fun.time
if(isTRUE(as.character(z) %in% ls(.GlobalEnv)))
{
bodyfun <- as.expression(body(get(as.character(z))))
}
else
{
bodyfun <- as.expression(z[[3]])
}
vars <- intersect(colnames(newdata1),all.names(bodyfun))
getvars <- paste("getElement(newdata1,'",vars,"')",sep="")
if(length(vars))
{
for(i in 1:length(vars))
{
bodyfun <- sub(vars[i],getvars[i],bodyfun)
bodyfun <- sub(as.character(match.call()$newdata),"",bodyfun)
tmp <- strsplit(bodyfun,split="")[[1]]
dollard <- which(tmp=="$")
if(length(dollard))
{
tmp <- tmp[-dollard]
}
bodyfun <- paste(tmp,collapse="")
}
}
headfun <- paste("function(",names(formals(fun.time)),")",collapse="")
ff1 <- paste(headfun,bodyfun)
ff2 <- parse(text=ff1)
ff3 <- eval(ff2)
timemes <- do.call("ff3",list(newdata1[,var.time]))
}
else
{
timemes <- newdata1[,var.time]
}
}
else
{
timemes <- newdata1[,var.time]
}
na.fun <- which(is.na(timemes))
## Table sans donnees manquante: newdata1
na.action <- unique(c(na.other,na.fixed,na.random,na.classmb,na.survival,na.cor,na.time,na.fun))
if(length(na.action))
{
newdata1 <- newdata1[-na.action,]
}
## si Tevent et Devent dans newdata
Tevent <- NULL
Devent <- NULL
if(length(model$Names$Tnames)==2) # ordre : Tevent,Event
{
if(all(model$Names$Tnames[1:2] %in% colnames(newdata1)))
{
Tevent <- newdata1[,model$Names$Tnames[1]]
Devent <- newdata1[,model$Names$Tnames[2]]
if(length(na.action))
{
Tevent <- Tevent[-na.action]
Devent <- Devent[-na.action]
}
indNA <- which(is.na(Tevent) | is.na(Devent))
if(length(indNA))
{
Tevent[indNA] <- 0
Devent[indNA] <- 0
}
}
}
if(length(model$Names$Tnames)==3) # ordre : Tentry,Tevent,Event
{
if(all(model$Names$Tnames[2:3] %in% colnames(newdata1)))
{
Tevent <- newdata1[,model$Names$Tnames[2]]
Devent <- newdata1[,model$Names$Tnames[3]]
if(!is.null(na.action))
{
Tevent <- Tevent[-na.action]
Devent <- Devent[-na.action]
}
indNA <- which(is.na(Tevent) | is.na(Devent))
if(length(indNA))
{
Tevent[indNA] <- 0
Devent[indNA] <- 0
}
}
}
## nb de sujets
id.subject <- newdata1[,model$Names$ID]
if(is.factor(id.subject)) id.subject <- factor(id.subject,levels=unique(id.subject))
ns <- length(unique(id.subject))
## vecteur Y et indiceY,uniqueY,nvalSPL
Y <- newdata1[,model$Names$Yname]
uniqueY <- 0
indiceY <- 0
nvalSPL <- 0
if(idlink == 2)
{
uniqueY <- sort(unique(Y))
permut <- order(order(Y)) # sort(y)[order(order(y))] = y
indice <- rep(1:length(uniqueY), as.vector(table(Y)))
indiceY <- indice[permut]
nvalSPL <- length(uniqueY)
}
## nb d'observations
nobs <- length(Y)
## nb de mesures par sujet
nmes <- as.vector(table(id.subject))
## prior
prior <- rep(0,ns)
if(length(model$Names$prior.name))
{
prior <- newdata1[cumsum(nmes),model$Names$prior.name]
}
## age entree si type = counting
tsurv0 <- rep(0,ns)
idtrunc <- 0
if(length(model$Names$Tnames)>2)
{
idtrunc <- 1
tsurv0 <- newdata1[cumsum(nmes),model$Names$Tnames[1]]
}
## reduire Tevent et Devent au nb de sujets
if(!is.null(Tevent))
{
Tevent <- Tevent[cumsum(nmes)]
Devent <- Devent[cumsum(nmes)]
}
## create one data frame for each formula (useful with factors)
newdata1fixed <- newdata1
for(v in colnames(newdata1fixed))
{
if(v %in% names(model$levels$levelsfixed))
{
if(!is.null(model$levels$levelsfixed[[v]]))
{
newdata1fixed[,v] <- factor(newdata1fixed[,v], levels=model$levels$levelsfixed[[v]])
if(any(is.na(newdata1fixed[,v]))) stop(paste("Wrong factor level in variable",v))
}
}
}
newdata1random <- newdata1
for(v in colnames(newdata1random))
{
if(v %in% names(model$levels$levelsrandom))
{
if(!is.null(model$levels$levelsrandom[[v]]))
{
newdata1random[,v] <- factor(newdata1random[,v], levels=model$levels$levelsrandom[[v]])
if(any(is.na(newdata1random[,v]))) stop(paste("Wrong factor level in variable",v))
}
}
}
newdata1classmb <- newdata1
for(v in colnames(newdata1classmb))
{
if(v %in% names(model$levels$levelsclassmb))
{
if(!is.null(model$levels$levelsclassmb[[v]]))
{
newdata1classmb[,v] <- factor(newdata1classmb[,v], levels=model$levels$levelsclassmb[[v]])
if(any(is.na(newdata1classmb[,v]))) stop(paste("Wrong factor level in variable",v))
}
}
}
newdata1surv <- newdata1
for(v in colnames(newdata1surv))
{
if(v %in% names(model$levels$levelssurv))
{
if(!is.null(model$levels$levelssurv[[v]]))
{
newdata1surv[,v] <- factor(newdata1surv[,v], levels=model$levels$levelssurv[[v]])
if(any(is.na(newdata1surv[,v]))) stop(paste("Wrong factor level in variable",v))
}
}
}
## Construction de nouvelles var explicatives sur la nouvelle table :
X_intercept <- model.matrix(~1,data=newdata1)
colnames(X_intercept) <- "intercept"
## fixed
X_fixed <- model.matrix(formula(paste("~",call_fixed,sep="")),data=newdata1fixed)
if(colnames(X_fixed)[1]=="(Intercept)")
{
X_fixed <- X_fixed[,-1,drop=FALSE]
}
## mixture pas besoin car les variables sont dans fixed
## random
if(!is.null(model$call$random))
{
X_random <- model.matrix(formula(paste("~",call_random,sep="")),data=newdata1random)
if(colnames(X_random)[1]=="(Intercept)")
{
colnames(X_random)[1] <- "intercept"
}
}
else
{
X_random <- NULL
}
## classmb
if(!is.null(model$call$classmb))
{
X_classmb <- model.matrix(formula(paste("~",call_classmb,sep="")),data=newdata1classmb)
colnames(X_classmb)[1] <- "intercept"
}
else
{
X_classmb <- NULL
}
## survival
if(!is.null(model$call$survival))
{
X_survival <- model.matrix(formula(paste("~",call_survival,sep="")),data=newdata1surv)
if(ncol(X_survival)) colnames(X_survival)[1] <- "intercept"
}
else
{
X_survival <- NULL
}
##cor
if(model$N[7]>0) #on reprend la variable de temps de cor
{
z <- which(model$idcor==1)
X_cor <- newdata1[,model$Names$Xnames[z]]
}
else
{
X_cor <- NULL
}
## var.time
if(length(na.action)) timemes <- timemes[-na.action]
# temps deja dans la bonne echelle
## Construction de X dans le bon ordre
X <- cbind(X_intercept,X_fixed)
colX <- c("intercept",colnames(X_fixed))
if(id.X_random == 1)
{
for(i in 1:length(colnames(X_random)))
{
if((colnames(X_random)[i] %in% colnames(X))==FALSE)
{
X <- cbind(X,X_random[,i])
colnames(X) <- c(colX,colnames(X_random)[i])
colX <- colnames(X)
}
}
}
if(id.X_classmb == 1)
{
for(i in 1:length(colnames(X_classmb)))
{
if((colnames(X_classmb)[i] %in% colnames(X))==FALSE)
{
X <- cbind(X,X_classmb[,i])
colnames(X) <- c(colX,colnames(X_classmb)[i])
colX <- colnames(X)
}
}
}
if(id.X_survival == 1)
{
for(i in 1:length(colnames(X_survival)))
{
if((colnames(X_survival)[i] %in% colnames(X))==FALSE)
{
X <- cbind(X,X_survival[,i])
colnames(X) <- c(colX,colnames(X_survival)[i])
colX <- colnames(X)
}
}
}
if(model$N[7]>0)
{
idspecif <- matrix(model$idspecif,nbevt,length(model$idg),byrow=TRUE)
if(model$idea[z]==0 & model$idprob[z]==0 & model$idg[z]==0 & model$idcom[z]==0 & all(idspecif[,z]==0))
{
X <- cbind(X,X_cor)
colnames(X) <- c(colX,model$Names$Xnames[z])
colX <- colnames(X)
}
}
## X <- cbind(X_intercept,X_fixed,X_random,X_classmb,X_survival,X_cor)
## colX <- strsplit(colnames(X),split=":",fixed=TRUE)
## colX <- lapply(colX,sort)
## colX <- lapply(colX,paste,collapse=":")
## colnames(X) <- unlist(colX)
## Xnames <- strsplit(model$Names$Xnames,split=":",fixed=TRUE)
## Xnames <- lapply(Xnames,sort)
## Xnames <- lapply(Xnames,paste,collapse=":")
## Xnames <- unlist(Xnames)
## browser()
## X <- X[,Xnames,drop=FALSE]
########## fonction de risque ##########
risq0 <- function(t,evt,typrisq,brisq,zi,nz,bPH,logspecif)
{
if(typrisq[evt]==2)
{
if(logspecif==1)
{
res <- brisq[2,evt]*brisq[1,evt]*t**(brisq[2,evt]-1)
}
if(logspecif==0)
{
res <- brisq[1,evt]*brisq[2,evt]*(brisq[1,evt]*t)**(brisq[2,evt]-1)
}
}
if(typrisq[evt]==1)
{
res <- NULL
for(i in 1:length(t))
{
zz <- zi[1:nz[evt],evt]
ordtzz <- order(c(t[i],zz))
itzz <- c(1,rep(0,length(zz)))
j <- which(itzz[ordtzz]==1)-1
if(t[i]==zz[1]) j <- 1
res <- c(res,brisq[j,evt])
}
}
if(typrisq[evt]==3)
{
res <- risq_spl(t=t,z=zi[1:nz[evt],evt],b=brisq[,evt])
}
return(res*bPH[evt])
}
########## fin fonction de risque
########### fonction de risque cumule ##########
risqcum0 <- function(t,evt,typrisq,brisq,zi,nz,bPH,logspecif)
{
if(typrisq[evt]==2)
{
if(logspecif==1)
{
res <- brisq[1,evt]*t**brisq[2,evt]
}
if(logspecif==0)
{
res <- (brisq[1,evt]*t)**brisq[2,evt]
}
}
if(typrisq[evt]==1)
{
res <- NULL
for(i in 1:length(t))
{
zz <- zi[1:nz[evt],evt]
ordtzz <- order(c(t[i],zz))
itzz <- c(1,rep(0,length(zz)))
if(t[i]==zz[1])
{
j <- 1
som <- 0
}
else
{
j <- which(itzz[ordtzz]==1)-1
if(j==1) som <- 0
else som <- sum(brisq[1:(j-1),evt]*diff(zz)[1:(j-1)])
}
res <- c(res,som+brisq[j,evt]*(t[i]-zz[j]))
}
}
if(typrisq[evt]==3)
{
res <- risqcum_spl(t=t,z=zi[1:nz[evt],evt],b=brisq[,evt])
}
return(res*bPH[evt])
}
########### fin fonction de risque cumule
#### fonction a integrer pour avoir l'incidence cumulee ####
fct_incid <- function(t,evt,typrisq,brisq,zi,nz,bPH,Xevt,bevt,nbevt,bevtint,logspecif)
{
risq_tevt <- risq0(t,evt,typrisq,brisq,zi,nz,bPH,logspecif)*exp(sum(Xevt*bevt[,evt]))*exp(bevtint[evt])
somme <- 0
for(ke in 1:nbevt)
{
somme <- somme + risqcum0(t,ke,typrisq,brisq,zi,nz,bPH,logspecif)*exp(sum(Xevt*bevt[,ke]))*exp(bevtint[ke])
}
return(risq_tevt * exp(-somme) )
}
######## fin fct incid
#### matrice Xevt a ns lignes
Xevt_ns <- X[cumsum(nmes),which(idsurv!=0),drop=FALSE]
### ok differents profils reperes
## fonction d'integration
integrate2 <- function(...) return(integrate(...)$value)
if(!isTRUE(draws))
{
## calcul des proba a posteriori pour tous les temps s
ppi <- rep(0,ns*nbland*ng)
out <- .Fortran(C_postprob2,
as.double(Y),
as.double(X),
as.integer(ns),
as.integer(nmes),
as.integer(nobs),
as.integer(ng),
as.integer(nv),
as.integer(idiag),
as.integer(nwg),
as.integer(ncor),
as.integer(logspecif),
as.double(zi),
as.integer(idea),
as.integer(idg),
as.integer(idprob),
as.integer(idcor),
as.integer(idcom),
as.integer(idspecif2),
as.integer(idtdv),
as.integer(risqcom),
as.integer(nvdepsurv),
as.integer(typrisq),
as.integer(nz),
as.integer(nprisq),
as.integer(nbzitr),
as.double(zitr),
as.integer(idlink),
as.integer(indiceY),
as.double(uniqueY),
as.integer(nvalSPL),
as.double(epsY),
as.integer(nbevt),
as.double(tsurv0),
as.integer(idtrunc),
as.double(best),
as.integer(npm),
as.double(timemes),
as.double(landmark),
as.integer(nbland),
ppi=as.double(ppi))
out$ppi[which(out$ppi==-5)] <- NA
ppi <- matrix(out$ppi,ns*nbland,ng)
## incid et surv a remplir
incid <- array(NA,c(ns*nbland,ng,length(horizon)))
#surv <- array(1,c(ns*nbland,ng))
## calcul de incidcum et survie
for(ks in 1:nbland)
{
for(i in 1:nrow(Xevt_ns))
{
## si sujet a 0 mesure, suivant
if(nmes[i]==0) next
if(nvdepsurv>0)
{
itimedepvar <- which(colnames(Xevt_ns)==model$Names$TimeDepVar.name)
tint <- Xevt_ns[i,itimedepvar]
Xevt <- as.vector(Xevt_ns[i,-itimedepvar])
}
else
{
Xevt <- as.vector(Xevt_ns[i,])
tint <- max(landmark)+1
}
brisqtot <- as.vector(best[model$N[1]+1:model$N[2]])
bvarxevt <- as.vector(best[sum(model$N[1:2])+1:model$N[3]])
for (g in 1:ng)
{
## coef pour la classe g
bevt <- matrix(0,length(which(idsurv!=0))-nvdepsurv,nbevt)
bevtint <- rep(0,nbevt)
l <- 0
kcurr <- 0
for(k in 1:length(idcom))
{
if(idsurv[k]==0) next
if(idcom[k]==1 & idspecif[1,k]==1)
{
if(idtdv[k]==1)
{
bevtint[1:nbevt] <- bvarxevt[kcurr+1]
}
else
{
l <- l+1
bevt[l,1:nbevt] <- bvarxevt[kcurr+1]
}
kcurr <- kcurr+1
}
if(idcom[k]==1 & idspecif[1,k]==2)
{
if(idtdv[k]==1)
{
bevtint[1:nbevt] <- bvarxevt[kcurr+g]
}
else
{
l <- l+1
bevt[l,1:nbevt] <- bvarxevt[kcurr+g]
}
kcurr <- kcurr+ng
}
if(idcom[k]==0)
{
if(idtdv[k]==1)
{
kecurr <- 0
for(ke in 1:nbevt)
{
if(idspecif[ke,k]==0)
{
bevtint[ke] <- 0
}
if(idspecif[ke,k]==1)
{
bevtint[ke] <- bvarxevt[kcurr+kecurr+1]
kecurr <- kecurr+1
}
if(idspecif[ke,k]==2)
{
bevtint[ke] <- bvarxevt[kcurr+kecurr+g]
kecurr <- kecurr+ng
}
}
kcurr <- kcurr+kecurr
}
else
{
l <- l+1
kecurr <- 0
for(ke in 1:nbevt)
{
if(idspecif[ke,k]==0)
{
bevt[l,ke] <- 0
}
if(idspecif[ke,k]==1)
{
bevt[l,ke] <- bvarxevt[kcurr+kecurr+1]
kecurr <- kecurr+1
}
if(idspecif[ke,k]==2)
{
bevt[l,ke] <- bvarxevt[kcurr+kecurr+g]
kecurr <- kecurr+ng
}
}
kcurr <- kcurr+kecurr
}
}
}
if(nrow(bevt)==0) #pas de variables dans survie
{
Xevt <- 0
bevt <- matrix(0,1,nbevt)
}
##brisq de la classe g
brisqtmp <- sapply(1:nbevt,function(k) brisqtot[sum(nrisq[1:k])-nrisq[k] + (g-1)*ifelse(risqcom[k] %in% c(0,"Specific"),nprisq[k],0) + 1:nprisq[k]])
if(is.matrix(brisqtmp))
{
if(logspecif==1) brisq <- exp(brisqtmp)
if(logspecif==0) brisq <- brisqtmp**2
}
if(is.list(brisqtmp))
{
lmax <- max(sapply(brisqtmp,length))
if(model$logspecif==1)
{
brisq <- lapply(brisqtmp, function(l) c(exp(l),rep(0,lmax-length(l))))
brisq <- matrix(unlist(brisq),nrow=lmax,ncol=nbevt)
}
if(model$logspecif==0)
{
brisq <- lapply(brisqtmp, function(l) c(l**2,rep(0,lmax-length(l))))
brisq <- matrix(unlist(brisq),nrow=lmax,ncol=nbevt)
}
}
## coef PH de la classe g
bPH <- rep(1,nbevt)
if(any(risqcom %in% c(2,"PH")))
{
if(g<ng)
{
bPH <- sapply(1:nbevt,function(k) ifelse(risqcom[k] %in% c(2,"PH"),brisqtot[sum(nrisq[1:k])-(ng-1)+g],1))
bPH <- exp(bPH)
}
}
## incidcum entre s et s+t (ou difference des survies)
if(nbevt==1)
{
bevtint2 <- bevtint
if(nvdepsurv!=0 & tint>landmark[ks]) bevtint2 <- rep(0,nbevt)
surv_s <- exp(-risqcum0(t=landmark[ks],evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1])))
surv_tint <- exp(-risqcum0(t=tint,evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1])))
if(nvdepsurv!=0 & tint<landmark[ks])
{
incid[ns*(ks-1)+i,g,] <- sapply(landmark[ks]+horizon,function(h) (surv_tint * (surv_s-surv_tint)*exp(bevtint[1])) - surv_tint*(exp(-risqcum0(t=h,evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1]))*exp(bevtint[1]))-surv_tint*exp(bevtint[1])))
}
else
{
incid[ns*(ks-1)+i,g,] <- sapply(landmark[ks]+horizon,function(h) surv_s-exp(-risqcum0(t=h,evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1]))))
}
}
else
{
for(ke in 1:nbevt)
{
bevtint2 <- bevtint
if(nvdepsurv!=0 & tint>landmark[ks]) bevtint2 <- rep(0,nbevt)
if(ke==event)
{
incid[ns*(ks-1)+i,g,] <- sapply(landmark[ks]+horizon,integrate2,f=fct_incid,lower=landmark[ks],evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,Xevt=Xevt,bevt=bevt,nbevt=nbevt,bevtint=bevtint2,logspecif=logspecif)
}
#if(nvdepsurv!=0 & tint<landmark[ks])
# {
# surv[ns*(ks-1)+i,g] <- surv[ns*(ks-1)+i,g]*exp(-risqcum0(t=tint,evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke])))*(exp(-risqcum0(t=landmark[ks],evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke]))*exp(bevtint[ke]))-exp(-risqcum0(t=tint,evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke]))*exp(bevtint[ke])))
# }
#else
# {
# surv[ns*(ks-1)+i,g] <- surv[ns*(ks-1)+i,g]*exp(-risqcum0(t=landmark[ks],evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke])))
# }
}
}
} # fin boucle g
}# fin boucle i
} # fin boucle ks
res <- data.frame(rep(sort(unique(id.subject)),each=nbland*nbhoriz))
res <- cbind(res,rep(landmark,each=nbhoriz))
res <- cbind(res,horizon)
res <- cbind(res,NA)
colnames(res) <- c(model$Names$ID,"landmark","horizon","pred")
pred <- NULL
#pred_g <- incid*array(ppi/surv,c(ns*nbland,ng,nbhoriz))
pred_g <- incid*array(ppi,c(ns*nbland,ng,nbhoriz))
pred_evt <- apply(pred_g,c(1,3),sum) # res de ie horiz dans ie colonne
pred_evt <- pred_evt[order(rep(1:ns,nbland)),]
pred_evt <- as.vector(t(pred_evt))
pred <- c(pred,pred_evt)
res[,4] <- pred
## mettre NA si Devent!=0 et Tevent<s
if(!is.null(Tevent))
{
matevt <- cbind(rep(Tevent,each=nbland*nbhoriz),rep(Devent,each=nbland*nbhoriz))
indevt <- which( (matevt[,2]!=0) & (matevt[,1]<res[,"landmark"]) )
if(length(indevt)) res[indevt,"pred"] <- NA
}
}
else # ie avec draws
{
posfix <- eval(model$call$posfix)
Mat <- matrix(0,ncol=npm,nrow=npm)
Mat[upper.tri(Mat,diag=TRUE)]<- model$V
if(length(posfix))
{
Mat2 <- Mat[-posfix,-posfix]
Chol2 <- chol(Mat2)
Chol <- matrix(0,npm,npm)
Chol[setdiff(1:npm,posfix),setdiff(1:npm,posfix)] <- Chol2
Chol <- t(Chol)
}
else
{
Chol <- chol(Mat)
Chol <- t(Chol)
}
doOneDraw <- function()
{
bdraw <- rnorm(npm)
bdraw <- best + Chol %*% bdraw
## calcul des proba a posteriori pour tous les temps s
ppi <- rep(0,ns*nbland*ng)
out <- .Fortran(C_postprob2,
as.double(Y),
as.double(X),
as.integer(ns),
as.integer(nmes),
as.integer(nobs),
as.integer(ng),
as.integer(nv),
as.integer(idiag),
as.integer(nwg),
as.integer(ncor),
as.integer(logspecif),
as.double(zi),
as.integer(idea),
as.integer(idg),
as.integer(idprob),
as.integer(idcor),
as.integer(idcom),
as.integer(idspecif2),
as.integer(idtdv),
as.integer(risqcom),
as.integer(nvdepsurv),
as.integer(typrisq),
as.integer(nz),
as.integer(nprisq),
as.integer(nbzitr),
as.double(zitr),
as.integer(idlink),
as.integer(indiceY),
as.double(uniqueY),
as.integer(nvalSPL),
as.double(epsY),
as.integer(nbevt),
as.double(tsurv0),
as.integer(idtrunc),
as.double(bdraw),
as.integer(npm),
as.double(timemes),
as.double(landmark),
as.integer(nbland),
ppi=as.double(ppi))
out$ppi[which(out$ppi==-5)] <- NA
if(all(is.na(out$ppi))) return(rep(NA,ns*nbland*nbhoriz*nbevt))
ppi <- matrix(out$ppi,ns*nbland,ng)
## incid et surv a remplir
incid <- array(NA,c(ns*nbland,ng,length(horizon)))
#surv <- array(1,c(ns*nbland,ng))
## calcul de incidcum et survie
for(ks in 1:nbland)
{
for(i in 1:nrow(Xevt_ns))
{
## si sujet a 0 mesure,suivant
if(nmes[i]==0) next
if(nvdepsurv>0)
{
itimedepvar <- which(colnames(Xevt_ns)==model$Names$TimeDepVar.name)
tint <- Xevt_ns[i,itimedepvar]
Xevt <- as.vector(Xevt_ns[i,-itimedepvar])
}
else
{
Xevt <- as.vector(Xevt_ns[i,])
tint <- max(landmark)+1
}
brisqtot <- as.vector(bdraw[model$N[1]+1:model$N[2]])
bvarxevt <- as.vector(bdraw[sum(model$N[1:2])+1:model$N[3]])
for (g in 1:ng)
{
## coef pour la classe g
bevt <- matrix(0,length(which(idsurv!=0))-nvdepsurv,nbevt)
bevtint <- rep(0,nbevt)
l <- 0
kcurr <- 0
for(k in 1:length(idcom))
{
if(idsurv[k]==0) next
if(idcom[k]==1 & idspecif[1,k]==1)
{
if(idtdv[k]==1)
{
bevtint[1:nbevt] <- bvarxevt[kcurr+1]
}
else
{
l <- l+1
bevt[l,1:nbevt] <- bvarxevt[kcurr+1]
}
kcurr <- kcurr+1
}
if(idcom[k]==1 & idspecif[1,k]==2)
{
if(idtdv[k]==1)
{
bevtint[1:nbevt] <- bvarxevt[kcurr+g]
}
else
{
l <- l+1
bevt[l,1:nbevt] <- bvarxevt[kcurr+g]
}
kcurr <- kcurr+ng
}
if(idcom[k]==0)
{
if(idtdv[k]==1)
{
kecurr <- 0
for(ke in 1:nbevt)
{
if(idspecif[ke,k]==0)
{
bevtint[ke] <- 0
}
if(idspecif[ke,k]==1)
{
bevtint[ke] <- bvarxevt[kcurr+kecurr+1]
kecurr <- kecurr+1
}
if(idspecif[ke,k]==2)
{
bevtint[ke] <- bvarxevt[kcurr+kecurr+g]
kecurr <- kecurr+ng
}
}
kcurr <- kcurr+kecurr
}
else
{
l <- l+1
kecurr <- 0
for(ke in 1:nbevt)
{
if(idspecif[ke,k]==0)
{
bevt[l,ke] <- 0
}
if(idspecif[ke,k]==1)
{
bevt[l,ke] <- bvarxevt[kcurr+kecurr+1]
kecurr <- kecurr+1
}
if(idspecif[ke,k]==2)
{
bevt[l,ke] <- bvarxevt[kcurr+kecurr+g]
kecurr <- kecurr+ng
}
}
kcurr <- kcurr+kecurr
}
}
}
if(nrow(bevt)==0) #pas de variables dans survie
{
Xevt <- 0
bevt <- matrix(0,1,nbevt)
}
##brisq de la classe g
brisqtmp <- sapply(1:nbevt,function(k) brisqtot[sum(nrisq[1:k])-nrisq[k] + (g-1)*ifelse(risqcom[k] %in% c(0,"Specific"),nprisq[k],0) + 1:nprisq[k]])
if(is.matrix(brisqtmp))
{
if(logspecif==1) brisq <- exp(brisqtmp)
if(logspecif==0) brisq <- brisqtmp**2
}
if(is.list(brisqtmp))
{
lmax <- max(sapply(brisqtmp,length))
if(model$logspecif==1)
{
brisq <- lapply(brisqtmp, function(l) c(exp(l),rep(0,lmax-length(l))))
brisq <- matrix(unlist(brisq),nrow=lmax,ncol=nbevt)
}
if(model$logspecif==0)
{
brisq <- lapply(brisqtmp, function(l) c(l**2,rep(0,lmax-length(l))))
brisq <- matrix(unlist(brisq),nrow=lmax,ncol=nbevt)
}
}
## coef PH de la classe g
bPH <- rep(1,nbevt)
if(any(risqcom %in% c(2,"PH")))
{
if(g<ng)
{
bPH <- sapply(1:nbevt,function(k) ifelse(risqcom[k] %in% c(2,"PH"),brisqtot[sum(nrisq[1:k])-(ng-1)+g],1))
bPH <- exp(bPH)
}
}
## incidcum entre s et s+t et survie en s
if(nbevt==1)
{
bevtint2 <- bevtint
if(nvdepsurv!=0 & tint>landmark[ks]) bevtint2 <- rep(0,nbevt)
surv_s <- exp(-risqcum0(t=landmark[ks],evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1])))
surv_tint <- exp(-risqcum0(t=tint,evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1])))
if(nvdepsurv!=0 & tint<landmark[ks])
{
incid[ns*(ks-1)+i,g,] <- sapply(landmark[ks]+horizon,function(h) (surv_tint * (surv_s-surv_tint)*exp(bevtint[1])) - surv_tint*(exp(-risqcum0(t=h,evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1]))*exp(bevtint[1]))-surv_tint*exp(bevtint[1])))
}
else
{
incid[ns*(ks-1)+i,g,] <- sapply(landmark[ks]+horizon,function(h) surv_s-exp(-risqcum0(t=h,evt=1,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,logspecif=logspecif)*exp(sum(Xevt*bevt[,1]))))
}
}
else
{
for(ke in 1:nbevt)
{
bevtint2 <- bevtint
if(nvdepsurv!=0 & tint>landmark[ks]) bevtint2 <- rep(0,nbevt)
if(ke==event)
{
incid[ns*(ks-1)+i,g,] <- sapply(landmark[ks]+horizon,integrate2,f=fct_incid,lower=landmark[ks],evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH,Xevt=Xevt,bevt=bevt,nbevt=nbevt,bevtint=bevtint2,logspecif=logspecif)
}
#if(nvdepsurv!=0 & tint<landmark[ks])
# {
# surv[ns*(ks-1)+i,g] <- surv[ns*(ks-1)+i,g]*exp(-risqcum0(t=tint,evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke])))*(exp(-risqcum0(t=landmark[ks],evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke]))*exp(bevtint[ke]))-exp(-risqcum0(t=tint,evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke]))*exp(bevtint[ke])))
# }
#else
# {
# surv[ns*(ks-1)+i,g] <- surv[ns*(ks-1)+i,g]*exp(-risqcum0(t=landmark[ks],evt=ke,typrisq=typrisq,brisq=brisq,zi=zi,nz=nz,bPH=bPH)*exp(sum(Xevt*bevt[,ke])))
# }
}
}
} # fin boucle g
}# fin boucle i
} # fin boucle ks
res <- data.frame(rep(sort(unique(id.subject)),each=nbland*nbhoriz))
res <- cbind(res,rep(landmark,each=nbhoriz))
res <- cbind(res,horizon)
res <- cbind(res,NA)
colnames(res) <- c(model$Names$ID,"landmark","horizon","pred")
pred <- NULL
#pred_g <- incid*array(ppi/surv,c(ns*nbland,ng,nbhoriz))
pred_g <- incid*array(ppi,c(ns*nbland,ng,nbhoriz))
pred_evt <- apply(pred_g,c(1,3),sum) # res de ie horiz dans ie colonne
pred_evt <- pred_evt[order(rep(1:ns,nbland)),]
pred_evt <- as.vector(t(pred_evt))
pred <- c(pred,pred_evt)
res[,4] <- pred
## mettre NA si Devent!=0 et Tevent<s
if(!is.null(Tevent))
{
matevt <- cbind(rep(Tevent,each=nbland*nbhoriz),rep(Devent,each=nbland*nbhoriz))
indevt <- which( (matevt[,2]!=0) & (matevt[,1]<res[,"landmark"]) )
if(length(indevt)) res[indevt,"pred"] <- NA
}
return(res[,"pred"])
}
ndraws <- as.integer(ndraws)
resdraws <- replicate(ndraws,doOneDraw())
if(is.vector(resdraws))
{
resdraws <- matrix(resdraws,nrow=1)
}
#probs <- resdraws[-1,,drop=FALSE]
#pb <- sum(resdraws[1,])
#if(pb>0) warning("Infinite probabilities have been found. Confidence intervals are based on ", ndraws-pb ," simulations.")
med <- apply(resdraws,1,median,na.rm=TRUE)
qmin <- apply(resdraws,1,quantile,prob=0.025,na.rm=TRUE)
qmax <- apply(resdraws,1,quantile,prob=0.975,na.rm=TRUE)
res <- data.frame(rep(sort(unique(id.subject)),each=nbland*nbhoriz))
res <- cbind(res,rep(landmark,each=nbhoriz))
res <- cbind(res,horizon)
res <- cbind(res,med,qmin,qmax)
colnames(res) <- c(model$Names$ID,"landmark","horizon","pred_50","pred_2.5","pred_97.5")
}
}
else # ie conv != 1 ou 2 ou 3
{
cat("Output can not be produced since the program stopped abnormally. \n")
res <- NA
id.subject <- NA
nmes <- NA
Y <- NA
timemes <- NA
}
prmatrix(res,quote=FALSE)
res.list <- list(pred=res,newdata=data.frame(id=rep(unique(id.subject),nmes)[which(timemes<=max(landmark))],y=Y[which(timemes<=max(landmark))],time=timemes[which(timemes<=max(landmark))]))
class(res.list) <- "dynpred"
return(invisible(res.list))
}
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