R/dgCopula.R
In gsoutinho/survCopula: Multivariate Dependence Modeling with Copulas for Survival Data
Defines functions
dgCopula
Documented in
dgCopula
#' Random generator for multivariate survival data via copulas.
#'
#' @description This function can be used to generate multivariate
#' survival data in a variety of scenarios including competing risks, recurrent event and multi-state models.
#'
#' @param typeCopula Type of copula. Possible options are \code{"clayton"}, \code{"frank"} \code{"FGM"},
#' \code{"AMH"}, \code{"gumbel-hougaard"} and \code{"joe"}. Defaults to \code{"clayton"}.
#' @param theta A numeric value for the space parameter.
#' @param typeX Type of marginal distribution. Possible options are \code{"Exp"}, \code{"Norm"} \code{"Unif"}
#' and \code{"Gamma"}. Defaults to \code{"Exp"}.
#' @param num1_X A numeric value for the first parameter of the first marginal distribution.
#' @param num2_X A numeric value for the second parameter of the first marginal distribution.
#' Only required for two parameter distributions.
#' @param typeY Type of marginal distribution. Possible options are \code{"Exp"}, \code{"Norm"},
#' \code{"Unif"} and \code{"Gamma"}. Defaults to \code{"Exp"}.
#' @param num1_Y A numeric value for the first parameter of the second marginal distribution.
#' @param num2_Y A numeric value for the second parameter of the second marginal distribution.
#' Only required for two parameter distributions.
#' @param typeCens Type of censuring distribution. Possible options are \code{"None"}, \code{"Unif"},
#' \code{"Exp"} and \code{"Wei"}. Defaults to \code{"None"}.
#' @param num1_Cens A numeric value for the first parameter of the censoring distribution.
#' @param num2_Cens A numeric value for the second parameter of the censoring distribution.
#' Only required for two parameter distributions.
#' @param typeSurvData Type of survival data. Possible options are \code{"time-to-event"},
#' \code{"recurrent"}, #' \code{"competing-risks"} and \code{"illness-death"}. Defaults to
#' \code{"illness-death"}.
#' @param state2.prob Probability of a individual move to the intermediate state in illness-death model.
#' Only required if typeSurvData =”illness-death”. Default to 0.7.
#' @param nsim Number of observations to be generated.
#' @return
#' A numeric vector with the random multivariate survival data.
#' Meaning of the colums for the type of survival data:
#' Time-to-event:
#' \item{T}{Surival time. T = min(Y,Z).}
#' \item{Z}{Censoring time variable.}
#' \item{Delta}{Indicator status for censoring. Delta takes 1 when Y <= Z.}
#'
#' Recurrent:
#' \item{T1}{First gap time.}
#' \item{Delta1}{Censoring indicator variable for the first gap time.}
#' \item{T2}{Second gap time.}
#' \item{Delta2}{Censoring indicator variable for the second gap time.}
#' \item{Z}{Censoring time variable.}
#'
#' Competing risks:
#' \item{T}{Survival time.}
#' \item{Z}{Censoring time variable.}
#' \item{Delta}{Indicator status for censoring. Delta takes 0 if the competing risk process
#' does not move from the initial state at the survival time T, or the value 1 and 2 for the
#' possible causes of death 1 and 2.}
#'
#' Illness-death:
#' \item{T1}{Sojourn time in the initial state.}
#' \item{Delta1}{Indicator status. Delta1 takes 1 when T1<T and T1<Z.}
#' \item{T}{Total survival time.}
#' \item{Delta}{Indicator status for censoring. Delta takes 1 when T<Z.}
#' \item{Z}{Censoring time variable.}
#'
#' @examples
#'
#' sim.data<-dgCopula(typeCopula ='clayton', theta=1, typeX='Unif', num1_X=0, num2_X=5,
#' typeY='Unif', num1_Y=0, num2_Y=5, typeCens='Unif', num1_Cens=0,
#' num2_Cens=7, nsim=250,typeSurvData='time-to-event')
#'
#' head(sim.data)
#'
#'sim.data2<-dgCopula(typeCopula ='frank', theta=10, typeX='Exp', num1_X=0.5,
#' typeY='Exp', num1_Y=1.5, nsim=250,typeSurvData='illness-death',
#' typeCens='Unif', num1_Cens=0,
#' num2_Cens=4, state2.prob=0.6)
#'
#' @references
#' Meira-Machado, L.; de Una-Alvarez, J.; Cadarso-Suarez, C. and Andersen, P.K. Multi-state models for
#' the analysis of time to event data. Statistical Methods in Medical Research, 2009, 18, 195-222.
#'
#' Meira-Machado, L., Sestelo, M.; Gonlcalves, A. Nonparametric estimation of the survival function
#' for ordered multivariate failure time data: A comparative study. Biometrical Journal, 2016, 58, 623-634.
#'
#' L Meira-Machado, S Faria, A simulation study comparing modeling approaches in an illness-death multi-state model,
#' Communications in Statistics-Simulation and Computation, 2014, 43 (5), 929-946
#'
#' A Moreira, J de Una-Alvarez, L Machado Presmoothing the Aalen-Johansen estimator in the illness-death model,
#' Electronic Journal of Statistics, 2013, 7, 1491-1516
#'
#' A Moreira, L Meira-Machado, survivalBIV: Estimation of the bivariate distribution function for sequentially
#' ordered events under univariate censoring, J Stat Softw, 2012, 46 (13), 1-16
#'
#' @author Gustavo Soutinho, Luis Meira-Machado
#'
#' @importFrom "stats" "qgamma" "qnorm" "qunif" "rbinom" "rexp" "runif" "rweibull" "uniroot"
#' @export invF
#' @export copula
#' @export rcopula
#' @export dgCopula
dgCopula<- function(typeCopula = 'clayton', theta = 1, typeX='Exp', num1_X=1, num2_X=NULL,
typeY='Exp', num1_Y=1, num2_Y=NULL, typeCens='None', num1_Cens=NULL, num2_Cens=NULL,
typeSurvData='illness-death',
state2.prob=0.7, nsim=250){
TAB<-NULL
for(i in 1:nsim){
#i<-1
v1<-runif(1,0,1)
v2<-runif(1,0,1)
res<-copula(v1, v2, theta=theta,type=typeCopula,typeX=typeX, num1_X=num1_X, num2_X=num2_X,
typeY=typeY, num1_Y=num1_Y, num2_Y=num2_Y)
x<-res[1]
y<-res[2]
if(typeCens=='None'){
z<-10000000000000000000000000000
}else{
if(typeCens=='Unif'){
z<-runif(1,num1_Cens,num2_Cens)
}
if(typeCens=='Exp'){
z<-rexp(1,num1_Cens)
}
if(typeCens=='Wei'){
z<-rweibull(1,num1_Cens,num2_Cens )
}
}
if(typeSurvData=='time-to-event'){
t<-min(y, z)
delta<-ifelse(y<z, 1,0)
TAB<-rbind(TAB,cbind(t,z, delta))
}
if(typeSurvData=='recurrent'){
t1<-min(x, z)
delta1<-ifelse(x<z, 1,0)
if(x>z){
t2<-0
delta2<-0
}else{
t2<-min(y, z-x)
#delta2<-ifelse(t1+t2<=z, 1,0) estava isto
delta2<-ifelse(x+y<=z,1,0)
}
TAB<-rbind(TAB,cbind(t1, delta1, t2, delta2, z))
}
if(typeSurvData=='competing-risks'){
D<-ifelse(x<=y, 1, 2)
t<-min(x,y,z)
delta<-(ifelse(min(x,y)<=z, 1, 0)) * D
TAB<-rbind(TAB,cbind(t,z, delta))
}
if(typeSurvData=='illness-death'){
prob <- rbinom(1, 1, state2.prob)
if(prob==1){
t1<-min(x, z)
delta1<-ifelse(x<z, 1, 0)
t<-min(x+y,z)
delta<-ifelse(x+y<z, 1, 0)
TAB<-rbind(TAB,cbind(t1, delta1, t, delta, z))
}else{
w<-runif(1,0,2) #exemplo de distribuicao
t1<-min(w, z)
delta1<-ifelse(w<z, 1, 0)
t<-t1
delta<-delta1
TAB<-rbind(TAB,cbind(t1, delta1, t, delta, z))
}
}
}#fim simulacao
if(typeSurvData=='time-to-event'){
TAB<-as.data.frame(TAB)
colnames(TAB)<-c('T','Z', 'Delta')
#head(TAB)
}
if(typeSurvData=='recurrent'){
TAB<-as.data.frame(TAB)
colnames(TAB)<-c('T1', 'Delta1', 'T2', 'Delta2', 'Z')
#head(TAB)
}
if(typeSurvData=='competing-risks'){
TAB<-as.data.frame(TAB)
colnames(TAB)<-c('T','Z', 'Delta')
#head(TAB)
}
if(typeSurvData=='illness-death'){
TAB<-as.data.frame(TAB)
colnames(TAB)<-c('T1','Delta1', 'T','Delta', 'Z')
#head(TAB)
#TAB[1:10,]
}
#res<-list(tab=TAB, typeCopula=typeCopula,teta=teta,nsim=nsim)
res<-TAB
return(res)
}
gsoutinho/survCopula documentation built on Sept. 4, 2020, 3:54 a.m.
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Defines functions dgCopula
Documented in dgCopula
#' Random generator for multivariate survival data via copulas.
#'
#' @description This function can be used to generate multivariate
#' survival data in a variety of scenarios including competing risks, recurrent event and multi-state models.
#'
#' @param typeCopula Type of copula. Possible options are \code{"clayton"}, \code{"frank"} \code{"FGM"},
#' \code{"AMH"}, \code{"gumbel-hougaard"} and \code{"joe"}. Defaults to \code{"clayton"}.
#' @param theta A numeric value for the space parameter.
#' @param typeX Type of marginal distribution. Possible options are \code{"Exp"}, \code{"Norm"} \code{"Unif"}
#' and \code{"Gamma"}. Defaults to \code{"Exp"}.
#' @param num1_X A numeric value for the first parameter of the first marginal distribution.
#' @param num2_X A numeric value for the second parameter of the first marginal distribution.
#' Only required for two parameter distributions.
#' @param typeY Type of marginal distribution. Possible options are \code{"Exp"}, \code{"Norm"},
#' \code{"Unif"} and \code{"Gamma"}. Defaults to \code{"Exp"}.
#' @param num1_Y A numeric value for the first parameter of the second marginal distribution.
#' @param num2_Y A numeric value for the second parameter of the second marginal distribution.
#' Only required for two parameter distributions.
#' @param typeCens Type of censuring distribution. Possible options are \code{"None"}, \code{"Unif"},
#' \code{"Exp"} and \code{"Wei"}. Defaults to \code{"None"}.
#' @param num1_Cens A numeric value for the first parameter of the censoring distribution.
#' @param num2_Cens A numeric value for the second parameter of the censoring distribution.
#' Only required for two parameter distributions.
#' @param typeSurvData Type of survival data. Possible options are \code{"time-to-event"},
#' \code{"recurrent"}, #' \code{"competing-risks"} and \code{"illness-death"}. Defaults to
#' \code{"illness-death"}.
#' @param state2.prob Probability of a individual move to the intermediate state in illness-death model.
#' Only required if typeSurvData =”illness-death”. Default to 0.7.
#' @param nsim Number of observations to be generated.
#' @return
#' A numeric vector with the random multivariate survival data.
#' Meaning of the colums for the type of survival data:
#' Time-to-event:
#' \item{T}{Surival time. T = min(Y,Z).}
#' \item{Z}{Censoring time variable.}
#' \item{Delta}{Indicator status for censoring. Delta takes 1 when Y <= Z.}
#'
#' Recurrent:
#' \item{T1}{First gap time.}
#' \item{Delta1}{Censoring indicator variable for the first gap time.}
#' \item{T2}{Second gap time.}
#' \item{Delta2}{Censoring indicator variable for the second gap time.}
#' \item{Z}{Censoring time variable.}
#'
#' Competing risks:
#' \item{T}{Survival time.}
#' \item{Z}{Censoring time variable.}
#' \item{Delta}{Indicator status for censoring. Delta takes 0 if the competing risk process
#' does not move from the initial state at the survival time T, or the value 1 and 2 for the
#' possible causes of death 1 and 2.}
#'
#' Illness-death:
#' \item{T1}{Sojourn time in the initial state.}
#' \item{Delta1}{Indicator status. Delta1 takes 1 when T1<T and T1<Z.}
#' \item{T}{Total survival time.}
#' \item{Delta}{Indicator status for censoring. Delta takes 1 when T<Z.}
#' \item{Z}{Censoring time variable.}
#'
#' @examples
#'
#' sim.data<-dgCopula(typeCopula ='clayton', theta=1, typeX='Unif', num1_X=0, num2_X=5,
#' typeY='Unif', num1_Y=0, num2_Y=5, typeCens='Unif', num1_Cens=0,
#' num2_Cens=7, nsim=250,typeSurvData='time-to-event')
#'
#' head(sim.data)
#'
#'sim.data2<-dgCopula(typeCopula ='frank', theta=10, typeX='Exp', num1_X=0.5,
#' typeY='Exp', num1_Y=1.5, nsim=250,typeSurvData='illness-death',
#' typeCens='Unif', num1_Cens=0,
#' num2_Cens=4, state2.prob=0.6)
#'
#' @references
#' Meira-Machado, L.; de Una-Alvarez, J.; Cadarso-Suarez, C. and Andersen, P.K. Multi-state models for
#' the analysis of time to event data. Statistical Methods in Medical Research, 2009, 18, 195-222.
#'
#' Meira-Machado, L., Sestelo, M.; Gonlcalves, A. Nonparametric estimation of the survival function
#' for ordered multivariate failure time data: A comparative study. Biometrical Journal, 2016, 58, 623-634.
#'
#' L Meira-Machado, S Faria, A simulation study comparing modeling approaches in an illness-death multi-state model,
#' Communications in Statistics-Simulation and Computation, 2014, 43 (5), 929-946
#'
#' A Moreira, J de Una-Alvarez, L Machado Presmoothing the Aalen-Johansen estimator in the illness-death model,
#' Electronic Journal of Statistics, 2013, 7, 1491-1516
#'
#' A Moreira, L Meira-Machado, survivalBIV: Estimation of the bivariate distribution function for sequentially
#' ordered events under univariate censoring, J Stat Softw, 2012, 46 (13), 1-16
#'
#' @author Gustavo Soutinho, Luis Meira-Machado
#'
#' @importFrom "stats" "qgamma" "qnorm" "qunif" "rbinom" "rexp" "runif" "rweibull" "uniroot"
#' @export invF
#' @export copula
#' @export rcopula
#' @export dgCopula
dgCopula<- function(typeCopula = 'clayton', theta = 1, typeX='Exp', num1_X=1, num2_X=NULL,
typeY='Exp', num1_Y=1, num2_Y=NULL, typeCens='None', num1_Cens=NULL, num2_Cens=NULL,
typeSurvData='illness-death',
state2.prob=0.7, nsim=250){
TAB<-NULL
for(i in 1:nsim){
#i<-1
v1<-runif(1,0,1)
v2<-runif(1,0,1)
res<-copula(v1, v2, theta=theta,type=typeCopula,typeX=typeX, num1_X=num1_X, num2_X=num2_X,
typeY=typeY, num1_Y=num1_Y, num2_Y=num2_Y)
x<-res[1]
y<-res[2]
if(typeCens=='None'){
z<-10000000000000000000000000000
}else{
if(typeCens=='Unif'){
z<-runif(1,num1_Cens,num2_Cens)
}
if(typeCens=='Exp'){
z<-rexp(1,num1_Cens)
}
if(typeCens=='Wei'){
z<-rweibull(1,num1_Cens,num2_Cens )
}
}
if(typeSurvData=='time-to-event'){
t<-min(y, z)
delta<-ifelse(y<z, 1,0)
TAB<-rbind(TAB,cbind(t,z, delta))
}
if(typeSurvData=='recurrent'){
t1<-min(x, z)
delta1<-ifelse(x<z, 1,0)
if(x>z){
t2<-0
delta2<-0
}else{
t2<-min(y, z-x)
#delta2<-ifelse(t1+t2<=z, 1,0) estava isto
delta2<-ifelse(x+y<=z,1,0)
}
TAB<-rbind(TAB,cbind(t1, delta1, t2, delta2, z))
}
if(typeSurvData=='competing-risks'){
D<-ifelse(x<=y, 1, 2)
t<-min(x,y,z)
delta<-(ifelse(min(x,y)<=z, 1, 0)) * D
TAB<-rbind(TAB,cbind(t,z, delta))
}
if(typeSurvData=='illness-death'){
prob <- rbinom(1, 1, state2.prob)
if(prob==1){
t1<-min(x, z)
delta1<-ifelse(x<z, 1, 0)
t<-min(x+y,z)
delta<-ifelse(x+y<z, 1, 0)
TAB<-rbind(TAB,cbind(t1, delta1, t, delta, z))
}else{
w<-runif(1,0,2) #exemplo de distribuicao
t1<-min(w, z)
delta1<-ifelse(w<z, 1, 0)
t<-t1
delta<-delta1
TAB<-rbind(TAB,cbind(t1, delta1, t, delta, z))
}
}
}#fim simulacao
if(typeSurvData=='time-to-event'){
TAB<-as.data.frame(TAB)
colnames(TAB)<-c('T','Z', 'Delta')
#head(TAB)
}
if(typeSurvData=='recurrent'){
TAB<-as.data.frame(TAB)
colnames(TAB)<-c('T1', 'Delta1', 'T2', 'Delta2', 'Z')
#head(TAB)
}
if(typeSurvData=='competing-risks'){
TAB<-as.data.frame(TAB)
colnames(TAB)<-c('T','Z', 'Delta')
#head(TAB)
}
if(typeSurvData=='illness-death'){
TAB<-as.data.frame(TAB)
colnames(TAB)<-c('T1','Delta1', 'T','Delta', 'Z')
#head(TAB)
#TAB[1:10,]
}
#res<-list(tab=TAB, typeCopula=typeCopula,teta=teta,nsim=nsim)
res<-TAB
return(res)
}
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