R/OutcomeImputationCOXREJECTION.R
In lbeesleyBIOSTAT/MultiCure: EM Algorithms for Fitting Multistate Cure Models
Defines functions
UNEQUALCENSIMPUTECOXREJECTION
Documented in
UNEQUALCENSIMPUTECOXREJECTION
#' UNEQUALCENSIMPUTECOXREJECTION
#' @description The function UNEQUALCENSIMPUTECOXREJECTION will perform an imputation algorithm to handle unequal follow-up for recurrence and death. This function can be applied when we assume COX baseline hazards. This function performs imputation through rejection sampling.
#' @param datWIDE defined as in MultiCure
#' @param beta A vector containing the most recent estimates of beta
#' @param alpha A vector containing the most recent estimates of alpha
#' @param ImputeDat This is a list with the following elements:
#' \itemize{
#' \item UnequalCens: A vector taking value 1 if the subject has unequal follow-up. Note: If subject is assumed cured in datWIDE, they are listed as UnequalCens = 0.
#' \item CovMissing: A matrix indicating which elements of Cov are missing. Not needed for this imputation.
#' \item CovImp: A list containing a single imputation of Cov
#' \item GImp: A vector with a recent single imputation of G
#' \item YRImp: A vector with a recent single imputation of Y_R
#' \item deltaRImp: A vector with a recent single imputation of delta_R
#' \item y: The integral of the target kernel over Yr0 to Yd
#' \item Basehaz13: A matrix containing the estimate of the baseline hazard function for the 1->3 transition specified intervals
#' \item Basehaz24: A matrix containing the estimate of the baseline hazard function for the 2->4 transition specified intervals
#' \item Basehaz14: A matrix containing the estimate of the baseline hazard function for the 1->4 transition specified intervals
#' \item Basehaz34: A matrix containing the estimate of the baseline hazard function for the 3->4 transition specified intervals
#' }
#' @param TransCov defined as in MultiCure
#'
#' @return a list containing
#' \itemize{
#' \item [[1]]: deltaRImp, A single imputation of delta_R
#' \item [[2]]: YRImp, A single imputation of Y_R
#'}
#' @details This function cannot be applied when 'T_R' is included in the model for Recurrence -> Death. In this case, use one of the following functions instead UNEQUALCENSIMPUTECOXNESTEDWEIBULL, UNEQUALCENSIMPUTECOXMH or UNEQUALCENSIMPUTECOXINVERSION.
#' @export
### This function should only be used when T_R is not in Trans34
UNEQUALCENSIMPUTECOXREJECTION = function(datWIDE, beta, alpha, ImputeDat, TransCov){
##################
### Initialize ###
##################
UnequalCens = ImputeDat[[1]]
CovImp = as.data.frame(ImputeDat[[3]])
GImp = ImputeDat[[4]]
YRImp = ImputeDat[[5]]
deltaRImp = ImputeDat[[6]]
y = ImputeDat[[7]]
Basehaz13 = ImputeDat[[8]]
Basehaz24 = ImputeDat[[9]]
Basehaz14 = ImputeDat[[10]]
Basehaz34 = ImputeDat[[11]]
Nobs = length(datWIDE[,1])
A1 = length(TransCov$Trans13)
A2 = length(TransCov$Trans24)
A3 = length(TransCov$Trans14)
A4 = length(TransCov$Trans34)
TRANS = c(rep(1,A1), rep(2,A2), rep(3,A3), rep(4,A4))
XB_beta13 = as.numeric(beta[TRANS==1] %*% t(cbind(CovImp[,TransCov$Trans13])))
XB_beta24 = as.numeric(beta[TRANS==2] %*% t(cbind(CovImp[,TransCov$Trans24])))
XB_beta14 = as.numeric(beta[TRANS==3] %*% t(cbind(CovImp[,TransCov$Trans14])))
XB_beta34 = as.numeric(beta[TRANS==4] %*% t(cbind(CovImp[,TransCov$Trans34])))
sum(diff(Basehaz34[,1])>0.01)
BasehazFun_13 = stepfun(x= Basehaz13[,2], y = c(Basehaz13[,3],0), right = F)
BasehazFun_24 = stepfun(x= Basehaz24[,2], y = c(Basehaz24[,3],0), right = F)
BasehazFun_14 = stepfun(x= Basehaz14[,2], y = c(Basehaz14[,3],0), right = F)
BasehazFun_34 = stepfun(x= Basehaz34[,2], y = c(Basehaz34[,3],0), right = F)
S1_D = exp(-as.numeric(sapply(datWIDE$Y_D,Baseline_Hazard, Basehaz13))*exp(XB_beta13))*
exp(-as.numeric(sapply(datWIDE$Y_D,Baseline_Hazard, Basehaz14))*exp(XB_beta14))
S2_D = exp(-as.numeric(sapply(datWIDE$Y_D,Baseline_Hazard, Basehaz24))*exp(XB_beta24))
h24_D = BasehazFun_24(datWIDE$Y_D)*exp(XB_beta24)
h14_D = BasehazFun_14(datWIDE$Y_D)*exp(XB_beta14)
H13_R = exp(XB_beta13)*as.numeric(sapply(datWIDE$Y_R,Baseline_Hazard, Basehaz13))
H13_D = exp(XB_beta13)*as.numeric(sapply(datWIDE$Y_D,Baseline_Hazard, Basehaz13))
H34_R = exp(XB_beta34)*as.numeric(sapply(datWIDE$Y_D-datWIDE$Y_R,Baseline_Hazard, Basehaz34))
H34_D = rep(0,Nobs)
### For use with uniroot (SLOW!)
Draw_Trunc13 = function(TIME, U,YR, YD){
S_R = exp(-exp(XB_beta13[m])* Baseline_Hazard(YR, Basehaz13))
S_D = exp(-exp(XB_beta13[m])* Baseline_Hazard(min(YD,TAU), Basehaz13))
ZERO =Baseline_Hazard(TIME, Basehaz13) + (log( U*(S_R - S_D) + S_D)*(1/exp(XB_beta13[m])))
return(ZERO)
}
Draw_Trunc34 = function(TIME, U,YR, YD){
S_R = exp(-exp(XB_beta34[m])* Baseline_Hazard(YD-YR, Basehaz34))
S_D = exp(-exp(XB_beta34[m])* Baseline_Hazard(YD - min(YD,TAU), Basehaz34))
ZERO =Baseline_Hazard(YD-TIME, Basehaz34) + (log( U*(S_D - S_R) + S_R)*(1/exp(XB_beta34[m])))
return(ZERO)
}
TAU = max(datWIDE$Y_R[datWIDE$delta_R==1])
YRImp = ifelse(GImp==0,datWIDE$Y_D, ifelse(GImp==1 & UnequalCens == 0,datWIDE$Y_R,rep(NA,Nobs) ))
deltaRImp = ifelse(GImp==0,rep(0,Nobs), ifelse(GImp==1 & UnequalCens == 0,datWIDE$delta_R,rep(NA,Nobs) ))
######################
### Impute Delta R ###
######################
num = y
denom = (h14_D^datWIDE$delta_D)*S1_D
ratio = ifelse(num==0,num,num/(num + denom)) [GImp==1 & UnequalCens == 1]
deltaRImp[GImp==1 & UnequalCens == 1] = apply(matrix(ratio), 1,mSample)
YRImp[GImp==1 & UnequalCens == 1 & deltaRImp==0] = datWIDE$Y_D[GImp==1 & UnequalCens == 1 & deltaRImp==0]
INDICES = which(is.na(YRImp))
##################
##################
### Impute T_R ### (Using Rejection Sampling)
##################
##################
DrawVALWRAPPER = function(s){
m = INDICES[s]
if(datWIDE$delta_D[m]==0){
ACCEPT = FALSE
counter = 1
###################
### Determine K ### (Gives dominating function)
###################
#This will give an approximate K (should even be an over-estimate)
timesLOWER = sort(unique(c(Basehaz14[,1], datWIDE$Y_D[m]-Basehaz34[,1], datWIDE$Y_R[m], datWIDE$Y_D[m]) ))
timesLOWER = timesLOWER[timesLOWER>=datWIDE$Y_R[m] & timesLOWER <=datWIDE$Y_D[m] ]
K = max(((exp(XB_beta34[m])*BasehazFun_34(datWIDE$Y_D[m]-timesLOWER))^datWIDE$delta_D[m])*
exp(-exp(XB_beta34[m])*as.numeric(sapply(datWIDE$Y_D[m]-timesLOWER,Baseline_Hazard, Basehaz34 )))*
exp(-exp(XB_beta14[m])*as.numeric(sapply(timesLOWER,Baseline_Hazard, Basehaz14 ))))
while(ACCEPT == FALSE){
########################################
### Draw from truncated distribution ###
########################################
U1 = runif(n=1, min = 0, max = 1)
draw = stats::uniroot(Draw_Trunc13, interval = c(datWIDE$Y_R[m], min(datWIDE$Y_D[m],TAU)),
U1,datWIDE$Y_R[m], min(datWIDE$Y_D[m],TAU))$root
if(abs(draw-datWIDE$Y_D[m]) < 0.001){draw = draw - 0.001}
H14_draw = exp(XB_beta14[m])*as.numeric(sapply(draw,Baseline_Hazard, Basehaz14 )) #14
H34_draw = exp(XB_beta34[m])*as.numeric(sapply(datWIDE$Y_D[m]-draw,Baseline_Hazard, Basehaz34 ))
h34_draw = exp(XB_beta34[m])*BasehazFun_34(datWIDE$Y_D[m]-draw) #34
Surv3_draw = exp(-H34_draw) #34
##########################
### Accept/Reject Draw ###
##########################
U2 = runif(n=1, min = 0, max = 1)
if(U2 <= ((1/K)*exp(-H14_draw))*Surv3_draw*(h34_draw^datWIDE$delta_D[m])){
ACCEPT = TRUE
}
counter = counter + 1
print(counter)
####################################
### Guard Against Infinite Loops ###
####################################
if(counter == 1000){
#print('High counter')
ACCEPT = TRUE
}
}
}else if(datWIDE$delta_D[m]==1){
ACCEPT = FALSE
counter = 1
###################
### Determine K ### (Gives dominating function)
###################
#This will give an approximate K (should even be an over-estimate)
timesLOWER = sort(unique(c(Basehaz14[,1], Basehaz13[,1], datWIDE$Y_R[m], datWIDE$Y_D[m]) ))
timesLOWER = timesLOWER[timesLOWER>=datWIDE$Y_R[m] & timesLOWER <=datWIDE$Y_D[m] ]
K = max((exp(XB_beta13[m])*BasehazFun_13(timesLOWER))*exp(-exp(XB_beta13[m])*as.numeric(sapply(timesLOWER,Baseline_Hazard, Basehaz13 )))*
exp(-exp(XB_beta14[m])*as.numeric(sapply(timesLOWER,Baseline_Hazard, Basehaz14 ))))
while(ACCEPT == FALSE){
########################################
### Draw from truncated distribution ###
########################################
U1 = runif(n=1, min = 0, max = 1)
draw = stats::uniroot(Draw_Trunc34, interval = c(datWIDE$Y_R[m],min(datWIDE$Y_D[m],TAU)),
U1,datWIDE$Y_R[m], min(datWIDE$Y_D[m],TAU))$root
if(abs(draw-datWIDE$Y_D[m]) < 0.001){draw = draw - 0.001}
H14_draw = exp(XB_beta14[m])*as.numeric(sapply(draw,Baseline_Hazard, Basehaz14 )) #14
H13_draw = exp(XB_beta13[m])*as.numeric(sapply(draw,Baseline_Hazard, Basehaz13 )) #13
h13_draw = exp(XB_beta13[m])*BasehazFun_13(draw) #13
Surv13_draw = exp(-H13_draw) #13
##########################
### Accept/Reject Draw ###
##########################
U2 = runif(n=1, min = 0, max = 1)
if(U2 <= ((1/K)*exp(-H14_draw))*Surv13_draw*h13_draw){
ACCEPT = TRUE
}
counter = counter + 1
print(counter)
####################################
### Guard Against Infinite Loops ###
####################################
if(counter == 1000){
#print('High counter')
ACCEPT = TRUE
}
}
}#End of ifelse
if(draw >= datWIDE$Y_D[m] ){draw = datWIDE$Y_D[m] - (datWIDE$Y_D[m]/1000)}
if(draw <= datWIDE$Y_R[m] ){draw = datWIDE$Y_R[m] + (datWIDE$Y_R[m]/1000)}
#print(m)
return(draw)
}
DRAWS = sapply(as.numeric(c(1:length(INDICES))), DrawVALWRAPPER)
YRImp[INDICES] = DRAWS
return(list(deltaRImp, YRImp))
}
lbeesleyBIOSTAT/MultiCure documentation built on July 10, 2019, 5:27 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions UNEQUALCENSIMPUTECOXREJECTION
Documented in UNEQUALCENSIMPUTECOXREJECTION
#' UNEQUALCENSIMPUTECOXREJECTION
#' @description The function UNEQUALCENSIMPUTECOXREJECTION will perform an imputation algorithm to handle unequal follow-up for recurrence and death. This function can be applied when we assume COX baseline hazards. This function performs imputation through rejection sampling.
#' @param datWIDE defined as in MultiCure
#' @param beta A vector containing the most recent estimates of beta
#' @param alpha A vector containing the most recent estimates of alpha
#' @param ImputeDat This is a list with the following elements:
#' \itemize{
#' \item UnequalCens: A vector taking value 1 if the subject has unequal follow-up. Note: If subject is assumed cured in datWIDE, they are listed as UnequalCens = 0.
#' \item CovMissing: A matrix indicating which elements of Cov are missing. Not needed for this imputation.
#' \item CovImp: A list containing a single imputation of Cov
#' \item GImp: A vector with a recent single imputation of G
#' \item YRImp: A vector with a recent single imputation of Y_R
#' \item deltaRImp: A vector with a recent single imputation of delta_R
#' \item y: The integral of the target kernel over Yr0 to Yd
#' \item Basehaz13: A matrix containing the estimate of the baseline hazard function for the 1->3 transition specified intervals
#' \item Basehaz24: A matrix containing the estimate of the baseline hazard function for the 2->4 transition specified intervals
#' \item Basehaz14: A matrix containing the estimate of the baseline hazard function for the 1->4 transition specified intervals
#' \item Basehaz34: A matrix containing the estimate of the baseline hazard function for the 3->4 transition specified intervals
#' }
#' @param TransCov defined as in MultiCure
#'
#' @return a list containing
#' \itemize{
#' \item [[1]]: deltaRImp, A single imputation of delta_R
#' \item [[2]]: YRImp, A single imputation of Y_R
#'}
#' @details This function cannot be applied when 'T_R' is included in the model for Recurrence -> Death. In this case, use one of the following functions instead UNEQUALCENSIMPUTECOXNESTEDWEIBULL, UNEQUALCENSIMPUTECOXMH or UNEQUALCENSIMPUTECOXINVERSION.
#' @export
### This function should only be used when T_R is not in Trans34
UNEQUALCENSIMPUTECOXREJECTION = function(datWIDE, beta, alpha, ImputeDat, TransCov){
##################
### Initialize ###
##################
UnequalCens = ImputeDat[[1]]
CovImp = as.data.frame(ImputeDat[[3]])
GImp = ImputeDat[[4]]
YRImp = ImputeDat[[5]]
deltaRImp = ImputeDat[[6]]
y = ImputeDat[[7]]
Basehaz13 = ImputeDat[[8]]
Basehaz24 = ImputeDat[[9]]
Basehaz14 = ImputeDat[[10]]
Basehaz34 = ImputeDat[[11]]
Nobs = length(datWIDE[,1])
A1 = length(TransCov$Trans13)
A2 = length(TransCov$Trans24)
A3 = length(TransCov$Trans14)
A4 = length(TransCov$Trans34)
TRANS = c(rep(1,A1), rep(2,A2), rep(3,A3), rep(4,A4))
XB_beta13 = as.numeric(beta[TRANS==1] %*% t(cbind(CovImp[,TransCov$Trans13])))
XB_beta24 = as.numeric(beta[TRANS==2] %*% t(cbind(CovImp[,TransCov$Trans24])))
XB_beta14 = as.numeric(beta[TRANS==3] %*% t(cbind(CovImp[,TransCov$Trans14])))
XB_beta34 = as.numeric(beta[TRANS==4] %*% t(cbind(CovImp[,TransCov$Trans34])))
sum(diff(Basehaz34[,1])>0.01)
BasehazFun_13 = stepfun(x= Basehaz13[,2], y = c(Basehaz13[,3],0), right = F)
BasehazFun_24 = stepfun(x= Basehaz24[,2], y = c(Basehaz24[,3],0), right = F)
BasehazFun_14 = stepfun(x= Basehaz14[,2], y = c(Basehaz14[,3],0), right = F)
BasehazFun_34 = stepfun(x= Basehaz34[,2], y = c(Basehaz34[,3],0), right = F)
S1_D = exp(-as.numeric(sapply(datWIDE$Y_D,Baseline_Hazard, Basehaz13))*exp(XB_beta13))*
exp(-as.numeric(sapply(datWIDE$Y_D,Baseline_Hazard, Basehaz14))*exp(XB_beta14))
S2_D = exp(-as.numeric(sapply(datWIDE$Y_D,Baseline_Hazard, Basehaz24))*exp(XB_beta24))
h24_D = BasehazFun_24(datWIDE$Y_D)*exp(XB_beta24)
h14_D = BasehazFun_14(datWIDE$Y_D)*exp(XB_beta14)
H13_R = exp(XB_beta13)*as.numeric(sapply(datWIDE$Y_R,Baseline_Hazard, Basehaz13))
H13_D = exp(XB_beta13)*as.numeric(sapply(datWIDE$Y_D,Baseline_Hazard, Basehaz13))
H34_R = exp(XB_beta34)*as.numeric(sapply(datWIDE$Y_D-datWIDE$Y_R,Baseline_Hazard, Basehaz34))
H34_D = rep(0,Nobs)
### For use with uniroot (SLOW!)
Draw_Trunc13 = function(TIME, U,YR, YD){
S_R = exp(-exp(XB_beta13[m])* Baseline_Hazard(YR, Basehaz13))
S_D = exp(-exp(XB_beta13[m])* Baseline_Hazard(min(YD,TAU), Basehaz13))
ZERO =Baseline_Hazard(TIME, Basehaz13) + (log( U*(S_R - S_D) + S_D)*(1/exp(XB_beta13[m])))
return(ZERO)
}
Draw_Trunc34 = function(TIME, U,YR, YD){
S_R = exp(-exp(XB_beta34[m])* Baseline_Hazard(YD-YR, Basehaz34))
S_D = exp(-exp(XB_beta34[m])* Baseline_Hazard(YD - min(YD,TAU), Basehaz34))
ZERO =Baseline_Hazard(YD-TIME, Basehaz34) + (log( U*(S_D - S_R) + S_R)*(1/exp(XB_beta34[m])))
return(ZERO)
}
TAU = max(datWIDE$Y_R[datWIDE$delta_R==1])
YRImp = ifelse(GImp==0,datWIDE$Y_D, ifelse(GImp==1 & UnequalCens == 0,datWIDE$Y_R,rep(NA,Nobs) ))
deltaRImp = ifelse(GImp==0,rep(0,Nobs), ifelse(GImp==1 & UnequalCens == 0,datWIDE$delta_R,rep(NA,Nobs) ))
######################
### Impute Delta R ###
######################
num = y
denom = (h14_D^datWIDE$delta_D)*S1_D
ratio = ifelse(num==0,num,num/(num + denom)) [GImp==1 & UnequalCens == 1]
deltaRImp[GImp==1 & UnequalCens == 1] = apply(matrix(ratio), 1,mSample)
YRImp[GImp==1 & UnequalCens == 1 & deltaRImp==0] = datWIDE$Y_D[GImp==1 & UnequalCens == 1 & deltaRImp==0]
INDICES = which(is.na(YRImp))
##################
##################
### Impute T_R ### (Using Rejection Sampling)
##################
##################
DrawVALWRAPPER = function(s){
m = INDICES[s]
if(datWIDE$delta_D[m]==0){
ACCEPT = FALSE
counter = 1
###################
### Determine K ### (Gives dominating function)
###################
#This will give an approximate K (should even be an over-estimate)
timesLOWER = sort(unique(c(Basehaz14[,1], datWIDE$Y_D[m]-Basehaz34[,1], datWIDE$Y_R[m], datWIDE$Y_D[m]) ))
timesLOWER = timesLOWER[timesLOWER>=datWIDE$Y_R[m] & timesLOWER <=datWIDE$Y_D[m] ]
K = max(((exp(XB_beta34[m])*BasehazFun_34(datWIDE$Y_D[m]-timesLOWER))^datWIDE$delta_D[m])*
exp(-exp(XB_beta34[m])*as.numeric(sapply(datWIDE$Y_D[m]-timesLOWER,Baseline_Hazard, Basehaz34 )))*
exp(-exp(XB_beta14[m])*as.numeric(sapply(timesLOWER,Baseline_Hazard, Basehaz14 ))))
while(ACCEPT == FALSE){
########################################
### Draw from truncated distribution ###
########################################
U1 = runif(n=1, min = 0, max = 1)
draw = stats::uniroot(Draw_Trunc13, interval = c(datWIDE$Y_R[m], min(datWIDE$Y_D[m],TAU)),
U1,datWIDE$Y_R[m], min(datWIDE$Y_D[m],TAU))$root
if(abs(draw-datWIDE$Y_D[m]) < 0.001){draw = draw - 0.001}
H14_draw = exp(XB_beta14[m])*as.numeric(sapply(draw,Baseline_Hazard, Basehaz14 )) #14
H34_draw = exp(XB_beta34[m])*as.numeric(sapply(datWIDE$Y_D[m]-draw,Baseline_Hazard, Basehaz34 ))
h34_draw = exp(XB_beta34[m])*BasehazFun_34(datWIDE$Y_D[m]-draw) #34
Surv3_draw = exp(-H34_draw) #34
##########################
### Accept/Reject Draw ###
##########################
U2 = runif(n=1, min = 0, max = 1)
if(U2 <= ((1/K)*exp(-H14_draw))*Surv3_draw*(h34_draw^datWIDE$delta_D[m])){
ACCEPT = TRUE
}
counter = counter + 1
print(counter)
####################################
### Guard Against Infinite Loops ###
####################################
if(counter == 1000){
#print('High counter')
ACCEPT = TRUE
}
}
}else if(datWIDE$delta_D[m]==1){
ACCEPT = FALSE
counter = 1
###################
### Determine K ### (Gives dominating function)
###################
#This will give an approximate K (should even be an over-estimate)
timesLOWER = sort(unique(c(Basehaz14[,1], Basehaz13[,1], datWIDE$Y_R[m], datWIDE$Y_D[m]) ))
timesLOWER = timesLOWER[timesLOWER>=datWIDE$Y_R[m] & timesLOWER <=datWIDE$Y_D[m] ]
K = max((exp(XB_beta13[m])*BasehazFun_13(timesLOWER))*exp(-exp(XB_beta13[m])*as.numeric(sapply(timesLOWER,Baseline_Hazard, Basehaz13 )))*
exp(-exp(XB_beta14[m])*as.numeric(sapply(timesLOWER,Baseline_Hazard, Basehaz14 ))))
while(ACCEPT == FALSE){
########################################
### Draw from truncated distribution ###
########################################
U1 = runif(n=1, min = 0, max = 1)
draw = stats::uniroot(Draw_Trunc34, interval = c(datWIDE$Y_R[m],min(datWIDE$Y_D[m],TAU)),
U1,datWIDE$Y_R[m], min(datWIDE$Y_D[m],TAU))$root
if(abs(draw-datWIDE$Y_D[m]) < 0.001){draw = draw - 0.001}
H14_draw = exp(XB_beta14[m])*as.numeric(sapply(draw,Baseline_Hazard, Basehaz14 )) #14
H13_draw = exp(XB_beta13[m])*as.numeric(sapply(draw,Baseline_Hazard, Basehaz13 )) #13
h13_draw = exp(XB_beta13[m])*BasehazFun_13(draw) #13
Surv13_draw = exp(-H13_draw) #13
##########################
### Accept/Reject Draw ###
##########################
U2 = runif(n=1, min = 0, max = 1)
if(U2 <= ((1/K)*exp(-H14_draw))*Surv13_draw*h13_draw){
ACCEPT = TRUE
}
counter = counter + 1
print(counter)
####################################
### Guard Against Infinite Loops ###
####################################
if(counter == 1000){
#print('High counter')
ACCEPT = TRUE
}
}
}#End of ifelse
if(draw >= datWIDE$Y_D[m] ){draw = datWIDE$Y_D[m] - (datWIDE$Y_D[m]/1000)}
if(draw <= datWIDE$Y_R[m] ){draw = datWIDE$Y_R[m] + (datWIDE$Y_R[m]/1000)}
#print(m)
return(draw)
}
DRAWS = sapply(as.numeric(c(1:length(INDICES))), DrawVALWRAPPER)
YRImp[INDICES] = DRAWS
return(list(deltaRImp, YRImp))
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.