Nothing
# when dim(Y)==2, Y[,1] is timeToEvent, Y[,2] is event
.computeCumulativeHazard_GA0B0AB<-function(allparam, Y, X0, X, Z,
step, Nstep,
intTD=intTD_NC, intweightsfunc=intweights_CAV_SIM,
nT0basis,
Spline_t0=BSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE), Intercept_t0=TRUE,
ialpha0, nX0,
ibeta0, nX,
ialpha, ibeta,
nTbasis,
Spline_t =BSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE),
Intercept_t_NPH=rep(TRUE, nX),
debug=FALSE, ...){
# compute the cumulative hazard frm 0 to Y[,1]
#################################o################################################################################
#################################################################################################################
# the coef of the first t-basis is constraint to 1 for nat-spline, and n-sum(other beta) if bs using expand() method
#################################################################################################################
#################################################################################################################
#################################################################################################################
# allparam ; vector of all coefs
# gamma0 = allparam[1:nY0basis]
# alpha0= allparam[ialpha0]
# beta0= matrix(allparam[ibeta0], ncol=nX, nrow=nTbasis)
# alpha= diag(allparam[ialpha])
# beta= expand(matrix(allparam[ibeta], ncol=Z@nZ, nrow=nTbasis-1))
# beta does not contains coef for the first t-basis
#################################################################################################################
# Y : object of class Surv
# X0 : non-time dependante variable (may contain spline bases expended for non-loglinear terms)
# X : log lineair but time dependante variable
# Z : object of class "DesignMatrixNPHNLL" time dependent variables (spline basis expended)
# expected_rate : expected rate at event time T
# step : object of class "NCLagParam" or "GLMLagParam"
# intTD : function to perform numerical integration
# intweightfunc : function to compute weightsfor numerical integration
# nT0basis : number of spline basis
# Spline_t0, spline object for baseline hazard, with evaluate() method
# Intercept_t0=FALSE, option for evaluate, = TRUE all the basis, =FALSE all but first basis
# nTbasis : number of time spline basis for NPH or NLL effects
# nX0 : nb of PH variables dim(X0)=c(nobs, nX0)
# nX : nb of NPHLIN variables dim(X)=c(nobs, nX)
# Spline_t, spline object for time dependant effects, with evaluate() method
# Intercept_t_NPH vector of intercept option for NPH spline (=FALSE when X is NLL too, ie in case of remontet additif NLLNPH)
# ... not used args
# the function do not check the concorcance between length of parameter vectors and the number of knots and the Z.signature
# returned value : the log liikelihood of the model
if(is.null(Z)){
nZ <- 0
} else {
nZ <- Z@nZ
}
if(is.null(Spline_t0)){
igamma0 <- NULL
Spt0g <- NULL
}
else {
igamma0 <- 1:nT0basis
if(Intercept_t0){
tmpgamma0 <- allparam[1:nT0basis]
}
else {
tmpgamma0 <- c(0, allparam[1:nT0basis])
}
# baseline hazard at the end of the interval
Spt0g <- Spline_t0*tmpgamma0
}
# contribution of time d?pendant effect
# parenthesis are important for efficiency
if(nZ) {
# add a row of one for the first T-basis
Beta <- t(ExpandAllCoefBasis(allparam[ibeta], ncol=nZ, value=1))
# parenthesis important for speed ?
Zalphabeta <- Z@DM %*%( diag(allparam[ialpha]) %*% Z@signature %*% Beta )
if(nX) {
# add a row of 0 for the first T-basis when !Intercept_T_NPH
Zalphabeta <- Zalphabeta + X %*% t(ExpandCoefBasis(allparam[ibeta0],
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
} else {
if(nX) {
Zalphabeta <- X %*% t(ExpandCoefBasis(allparam[ibeta0],
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
}
if(nX + nZ) {
NPHterm <- intTD(rateTD_gamma0alphabeta, intTo=Y[,1], intToStatus=Y[,2],
step=step, Nstep=Nstep,
intweightsfunc=intweightsfunc,
gamma0=allparam[igamma0], Zalphabeta=Zalphabeta,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
Spline_t = Spline_t, Intercept_t=TRUE)
} else {
NPHterm <- intTD(rateTD_gamma0, intTo=Y[,1], intToStatus=Y[,2],
step=step, Nstep=Nstep, intweightsfunc=intweightsfunc,
gamma0=allparam[igamma0],
Spline_t0=Spt0g, Intercept_t0=Intercept_t0)
}
# contribution of non time dependant variables
if( nX0){
ret <- exp(X0 %*% allparam[ialpha0]) * NPHterm
} else {
ret <- NPHterm
}
ret
}
.computeCumulativeHazard_GA0B0AB_bh<-function(allparam, Y, X0, X, Z,
step, Nstep,
intTD=intTD_NC, intweightsfunc=intweights_CAV_SIM,
nT0basis,
Spline_t0=BSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE), Intercept_t0=TRUE,
ialpha0, nX0,
ibeta0, nX,
ialpha, ibeta,
nTbasis,
Spline_t =BSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE),
Intercept_t_NPH=rep(TRUE, nX),
debug=FALSE, ...){
# compute the cumulative hazard frm 0 to Y[,1]
# rate = (f(t)%*%gamma) * exp( X0%*%alpha0 + X%*%beta0(t) + sum( alphai(zi)betai(t) ))
#################################o################################################################################
#################################################################################################################
#################################################################################################################
#################################################################################################################
#################################################################################################################
# allparam ; vector of all coefs
# gamma0 = allparam[1:nY0basis]
# alpha0= allparam[ialpha0]
# beta0= matrix(allparam[ibeta0], ncol=nX, nrow=nTbasis)
# alpha= diag(allparam[ialpha])
# beta= expand(matrix(allparam[ibeta], ncol=Z@nZ, nrow=nTbasis-1))
# beta does not contains coef for the first t-basis
#################################################################################################################
# Y : object of class Surv
# X0 : non-time dependante variable (may contain spline bases expended for non-loglinear terms)
# X : log lineair but time dependante variable
# Z : object of class "DesignMatrixNPHNLL" time dependent variables (spline basis expended)
# expected_rate : expected rate at event time T
# step : object of class "NCLagParam" or "GLMLagParam"
# intTD : function to perform numerical integration
# intweightfunc : function to compute weightsfor numerical integration
# nT0basis : number of spline basis
# Spline_t0, spline object for baseline hazard, with evaluate() method
# Intercept_t0=FALSE, option for evaluate, = TRUE all the basis, =FALSE all but first basis
# nTbasis : number of time spline basis for NPH or NLL effects
# nX0 : nb of PH variables dim(X0)=c(nobs, nX0)
# nX : nb of NPHLIN variables dim(X)=c(nobs, nX)
# Spline_t, spline object for time dependant effects, with evaluate() method
# Intercept_t_NPH vector of intercept option for NPH spline (=FALSE when X is NLL too, ie in case of remontet additif NLLNPH)
# ... not used args
# the function do not check the concorcance between length of parameter vectors and the number of knots and the Z.signature
# returned value : the log liikelihood of the model
if(is.null(Z)){
nZ <- 0
} else {
nZ <- Z@nZ
}
if(is.null(Spline_t0)){
igamma0 <- NULL
Spt0g <- NULL
}
else {
igamma0 <- 1:nT0basis
if(Intercept_t0){
tmpgamma0 <- allparam[1:nT0basis]
}
else {
tmpgamma0 <- c(0, allparam[1:nT0basis])
}
# baseline hazard at the end of the interval
Spt0g <- Spline_t0*tmpgamma0
}
# contribution of time d?pendant effect
# parenthesis are important for efficiency
if(nZ) {
# add a row of one for the first T-basis
Beta <- t(ExpandAllCoefBasis(allparam[ibeta], ncol=nZ, value=1))
# parenthesis important for speed ?
Zalphabeta <- Z@DM %*%( diag(allparam[ialpha]) %*% Z@signature %*% Beta )
if(nX) {
# add a row of 0 for the first T-basis when !Intercept_T_NPH
Zalphabeta <- Zalphabeta + X %*% t(ExpandCoefBasis(allparam[ibeta0],
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
} else {
if(nX) {
Zalphabeta <- X %*% t(ExpandCoefBasis(allparam[ibeta0],
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
}
if(nX + nZ) {
NPHterm <- intTD(rateTD_bh_alphabeta, intTo=Y[,1], intToStatus=Y[,2],
step=step, Nstep=Nstep,
intweightsfunc=intweightsfunc,
gamma0=allparam[igamma0], Zalphabeta=Zalphabeta,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
Spline_t = Spline_t, Intercept_t=TRUE)
} else {
# NPHterm <- intTD(rateTD_bh_gamma0, intTo=Y[,1], intToStatus=Y[,2],
# step=step, Nstep=Nstep, intweightsfunc=intweightsfunc,
# gamma0=allparam[1:nT0basis],
# Spline_t0=Spline_t0*tmpgamma0, Intercept_t0=Intercept_t0)
if(is.null(Spline_t0)){
# if no baseline hazard, and non NPH and no NPHNLL, it is assuemd nX0>0
NPHterm <- rep(1.0, dim(Y)[1])
}
else {
NPHterm <- predict(integrate(Spline_t0*tmpgamma0), Y[,1], intercep=Intercept_t0)
}
}
# contribution of non time dependant variables
if( nX0){
ret <- exp(X0 %*% allparam[ialpha0]) * NPHterm
} else {
ret <- NPHterm
}
ret
}
# when dim(Y)==3, Y[,1] is beginTime, Y[,2] is endTime, Y[,3] is event
.computeCumulativeHazard_fromto_GA0B0AB<-function(allparam, Y, X0, X, Z,
step, Nstep,
intTD=intTDft_NC, intweightsfunc=intweights_CAV_SIM,
nT0basis,
Spline_t0=BSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE), Intercept_t0=TRUE,
ialpha0, nX0,
ibeta0, nX,
ialpha, ibeta,
nTbasis,
Spline_t =BSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE),
Intercept_t_NPH=rep(TRUE, nX),
debug=FALSE, ...){
# compute the cumulative hazard frm Y[,1] to Y[,2]
# rate = exp( f(t)%*%gamma + X0%*%alpha0 + X%*%beta0(t) + sum( alphai(zi)betai(t) ))
#################################################################################################################
#################################################################################################################
# the coef of the first t-basis is constraint to 1 for nat-spline, and n-sum(other beta) if bs using expand() method
#################################################################################################################
#################################################################################################################
#################################################################################################################
# allparam ; vector of all coefs
# gamma0 = allparam[1:nY0basis]
# alpha0= allparam[ialpha0]
# beta0= matrix(allparam[ibeta0], ncol=nX, nrow=nTbasis)
# alpha= diag(allparam[ialpha])
# beta= expand(matrix(allparam[ibeta], ncol=Z@nZ, nrow=nTbasis-1))
# beta does not contains coef for the first t-basis
#################################################################################################################
# Y : object of class Surv with beginning and end of interval
#
# X0 : non-time dependante variable (may contain spline bases expended for non-loglinear terms)
# X : log lineair but time dependante variable
# Z : object of class "DesignMatrixNPHNLL" time dependent variables (spline basis expended)
# expected_rate : expected rate at event time T
# step : object of class "NCLagParam" or "GLMLagParam"
# intTD : function to perform numerical integration
# intweightfunc : function to compute weightsfor numerical integration
# nT0basis : number of spline basis
# Spline_t0, spline object for baseline hazard, with evaluate() method
# Intercept_t0=FALSE, option for evaluate, = TRUE all the basis, =FALSE all but first basis
# nTbasis : number of time spline basis for NPH or NLL effects
# nX0 : nb of PH variables dim(X0)=c(nobs, nX0)
# nX : nb of NPHLIN variables dim(X)=c(nobs, nX)
# Spline_t, spline object for time dependant effects, with evaluate() method
# Intercept_t_NPH vector of intercept option for NPH spline (=FALSE when X is NLL too, ie in case of remontet additif NLLNPH)
# ... not used args
# the function do not check the concorcance between length of parameter vectors and the number of knots and the Z.signature
# returned value : the log liikelihood of the model
if(is.null(Z)){
nZ <- 0
} else {
nZ <- Z@nZ
}
if(is.null(Spline_t0)){
igamma0 <- NULL
Spt0g <- NULL
}
else {
igamma0 <- 1:nT0basis
if(Intercept_t0){
tmpgamma0 <- allparam[1:nT0basis]
}
else {
tmpgamma0 <- c(0, allparam[1:nT0basis])
}
# baseline hazard at the end of the interval
Spt0g <- Spline_t0*tmpgamma0
}
# contribution of time d?pendant effect
# parenthesis are important for efficiency
if(nZ) {
# add a row of one for the first T-basis
Beta <- t(ExpandAllCoefBasis(allparam[ibeta], ncol=nZ, value=1))
# parenthesis important for speed ?
Zalphabeta <- Z@DM %*%( diag(allparam[ialpha]) %*% Z@signature %*% Beta )
if(nX) {
# add a row of 0 for the first T-basis when !Intercept_T_NPH
Zalphabeta <- Zalphabeta + X %*% t(ExpandCoefBasis(allparam[ibeta0],
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
} else {
if(nX) {
Zalphabeta <- X %*% t(ExpandCoefBasis(allparam[ibeta0],
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
}
if(nX + nZ) {
NPHterm <- intTD(rateTD_gamma0alphabeta, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
step=step, Nstep=Nstep,
intweightsfunc=intweightsfunc,
gamma0=allparam[igamma0], Zalphabeta=Zalphabeta,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
Spline_t = Spline_t, Intercept_t=TRUE)
} else {
NPHterm <- intTD(rateTD_gamma0, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
step=step, Nstep=Nstep, intweightsfunc=intweightsfunc,
gamma0=allparam[igamma0],
Spline_t0=Spt0g, Intercept_t0=Intercept_t0)
}
# contribution of non time dependant variables
if( nX0){
ret <- exp(X0 %*% allparam[ialpha0]) * NPHterm
} else {
ret <- NPHterm
}
ret
}
.computeCumulativeHazard_fromto_GA0B0AB_bh<-function(allparam, Y, X0, X, Z,
step, Nstep,
intTD=intTDft_NC, intweightsfunc=intweights_CAV_SIM,
nT0basis,
Spline_t0=BSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE), Intercept_t0=TRUE,
ialpha0, nX0,
ibeta0, nX,
ialpha, ibeta,
nTbasis,
Spline_t =BSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE),
Intercept_t_NPH=rep(TRUE, nX),
debug=FALSE, ...){
# compute the cumulative hazard frm Y[,1] to Y[,2]
# rate = (f(t)%*%gamma) * exp( X0%*%alpha0 + X%*%beta0(t) + sum( alphai(zi)betai(t) ))
#################################o################################################################################
#################################################################################################################
#################################################################################################################
#################################################################################################################
#################################################################################################################
# allparam ; vector of all coefs
# gamma0 = allparam[1:nY0basis]
# alpha0= allparam[ialpha0]
# beta0= matrix(allparam[ibeta0], ncol=nX, nrow=nTbasis)
# alpha= diag(allparam[ialpha])
# beta= expand(matrix(allparam[ibeta], ncol=Z@nZ, nrow=nTbasis-1))
# beta does not contains coef for the first t-basis
#################################################################################################################
# Y : object of class Surv
# X0 : non-time dependante variable (may contain spline bases expended for non-loglinear terms)
# X : log lineair but time dependante variable
# Z : object of class "DesignMatrixNPHNLL" time dependent variables (spline basis expended)
# expected_rate : expected rate at event time T
# step : object of class "NCLagParam" or "GLMLagParam"
# intTD : function to perform numerical integration
# intweightfunc : function to compute weightsfor numerical integration
# nT0basis : number of spline basis
# Spline_t0, spline object for baseline hazard, with evaluate() method
# Intercept_t0=FALSE, option for evaluate, = TRUE all the basis, =FALSE all but first basis
# nTbasis : number of time spline basis for NPH or NLL effects
# nX0 : nb of PH variables dim(X0)=c(nobs, nX0)
# nX : nb of NPHLIN variables dim(X)=c(nobs, nX)
# Spline_t, spline object for time dependant effects, with evaluate() method
# Intercept_t_NPH vector of intercept option for NPH spline (=FALSE when X is NLL too, ie in case of remontet additif NLLNPH)
# ... not used args
# the function do not check the concorcance between length of parameter vectors and the number of knots and the Z.signature
# returned value : the log liikelihood of the model
if(is.null(Z)){
nZ <- 0
} else {
nZ <- Z@nZ
}
if(is.null(Spline_t0)){
igamma0 <- NULL
Spt0g <- NULL
}
else {
igamma0 <- 1:nT0basis
if(Intercept_t0){
tmpgamma0 <- allparam[1:nT0basis]
}
else {
tmpgamma0 <- c(0, allparam[1:nT0basis])
}
# baseline hazard at the end of the interval
Spt0g <- Spline_t0*tmpgamma0
}
# contribution of time d?pendant effect
# parenthesis are important for efficiency
if(nZ) {
# add a row of one for the first T-basis
Beta <- t(ExpandAllCoefBasis(allparam[ibeta], ncol=nZ, value=1))
# parenthesis important for speed ?
Zalphabeta <- Z@DM %*%( diag(allparam[ialpha]) %*% Z@signature %*% Beta )
if(nX) {
# add a row of 0 for the first T-basis when !Intercept_T_NPH
Zalphabeta <- Zalphabeta + X %*% t(ExpandCoefBasis(allparam[ibeta0],
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
} else {
if(nX) {
Zalphabeta <- X %*% t(ExpandCoefBasis(allparam[ibeta0],
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
}
if(nX + nZ) {
NPHterm <- intTD(rateTD_bh_alphabeta, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
step=step, Nstep=Nstep,
intweightsfunc=intweightsfunc,
gamma0=allparam[igamma0], Zalphabeta=Zalphabeta,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
Spline_t = Spline_t, Intercept_t=TRUE)
} else {
# NPHterm <- intTD(rateTD_gamma0_bh, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
# step=step, Nstep=Nstep, intweightsfunc=intweightsfunc,
# gamma0=allparam[1:nT0basis],
# Spline_t0=Spline_t0*tmpgamma0, Intercept_t0=Intercept_t0)
if(is.null(Spline_t0)){
# if no baseline hazard, and non NPH and no NPHNLL, it is assuemd nX0>0
NPHterm <- rep(1.0, dim(Y)[1])
}
else {
NPHterm <- predict(integrate(Spline_t0*tmpgamma0), Y[,2], intercep=Intercept_t0) -
predict(integrate(Spline_t0*tmpgamma0), Y[,1], intercep=Intercept_t0)
}
}
# contribution of non time dependant variables
if( nX0){
ret <- exp(X0 %*% allparam[ialpha0]) * NPHterm
} else {
ret <- NPHterm
}
ret
}
# with WCE effect
# when dim(Y)==3, Y[,1] is beginTime, Y[,2] is endTime, Y[,3] is event
.computeCumulativeHazard_fromto_GA0B0ABE0<-function(allparam, Y, X0, X, Z, W,
Id, FirstId, LastId,
step, Nstep,
intTD=intTDft_NC, intweightsfunc=intweights_CAV_SIM,
nT0basis,
Spline_t0=BSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE), Intercept_t0=TRUE,
ialpha0, nX0,
ibeta0, nX,
ialpha, ibeta,
nTbasis,
ieta0, iWbeg, iWend, nW,
Spline_t =BSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE),
Intercept_t_NPH=rep(TRUE, nX),
ISpline_W =MSplineBasis(knots=NULL, degree=3, keep.duplicates=TRUE),
Intercept_W=TRUE,
debug=FALSE, ...){
# compute the cumulative hazard frm Y[,1] to Y[,2]
# rate = exp( f(t)%*%gamma + X0%*%alpha0 + X%*%beta0(t) + sum( alphai(zi)betai(t) ))+ sum ( wce(Wi , eta0i)(t))
#################################################################################################################
#################################################################################################################
# the coef of the first t-basis is constraint to 1 for nat-spline, and n-sum(other beta) if bs using expand() method
#################################################################################################################
#################################################################################################################
#################################################################################################################
# allparam ; vector of all coefs
# gamma0 = allparam[1:nY0basis]
# alpha0= allparam[ialpha0]
# beta0= matrix(allparam[ibeta0], ncol=nX, nrow=nTbasis)
# alpha= diag(allparam[ialpha])
# beta= expand(matrix(allparam[ibeta], ncol=Z@nZ, nrow=nTbasis-1))
# beta does not contains coef for the first t-basis
# eta0 = allparamE0[ieta0]
#################################################################################################################
# Y : object of class Surv but the matrix has 4 columns :
# Y[,1] beginning(1) , fromT
# Y[,2] end(2), toT,
# Y[,3] status(3) intToStatus
# Y[,4] end of followup(4)
# end of followup is assumed constant by Id
# X0 : non-time dependante variable (may contain spline bases expended for non-loglinear terms)
# X : log lineair but time dependante variable
# Z : object of class "DesignMatrixNPHNLL" time dependent variables (spline basis expended)
# W : Exposure variables used in Weighted Cumulative Exposure Models
# Id : varibale indicating individuals Id, lines with the same Id are considered to be from the same individual
# FirstId : all lines in FirstId[iT]:iT in the data comes from the same individual
# expected_rate : expected rate at event time T
# step : object of class "NCLagParam" or "GLMLagParam"
# intTD : function to perform numerical integration
# intweightfunc : function to compute weightsfor numerical integration
# nT0basis : number of spline basis
# Spline_t0, spline object for baseline hazard, with evaluate() method
# Intercept_t0=FALSE, option for evaluate, = TRUE all the basis, =FALSE all but first basis
# nTbasis : number of time spline basis for NPH or NLL effects
# nX0 : nb of PH variables dim(X0)=c(nobs, nX0)
# nX : nb of NPHLIN variables dim(X)=c(nobs, nX)
# Spline_t, spline object for time dependant effects, with evaluate() method
# Intercept_t_NPH vector of intercept option for NPH spline (=FALSE when X is NLL too, ie in case of remontet additif NLLNPH)
# nW : nb of WCE variables dim(W)=c(nobs, nW)
# iWbeg, iWend : coef of the ith WCE variable is eta0[iWbeg[i]:iWend[i]]
# ISpline_W, list of nW spline object for WCE effects, with evaluate() method
# ... not used args
# the function do not check the concorcance between length of parameter vectors and the number of knots and the Z.signature
# returned value : the log liikelihood of the model
if(is.null(Z)){
nZ <- 0
} else {
nZ <- Z@nZ
}
if(is.null(Spline_t0)){
igamma0 <- NULL
Spt0g <- NULL
}
else {
igamma0 <- 1:nT0basis
if(Intercept_t0){
tmpgamma0 <- allparam[1:nT0basis]
}
else {
tmpgamma0 <- c(0, allparam[1:nT0basis])
}
# baseline hazard at the end of the interval
Spt0g <- Spline_t0*tmpgamma0
}
# contribution of time d?pendant effect
# parenthesis are important for efficiency
if(nZ) {
# add a row of one for the first T-basis
Beta <- t(ExpandAllCoefBasis(allparam[ibeta], ncol=nZ, value=1))
# parenthesis important for speed ?
Zalphabeta <- Z@DM %*%( diag(allparam[ialpha]) %*% Z@signature %*% Beta )
if(nX) {
# add a row of 0 for the first T-basis when !Intercept_T_NPH
Zalphabeta <- Zalphabeta + X %*% t(ExpandCoefBasis(allparam[ibeta0],
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
} else {
if(nX) {
Zalphabeta <- X %*% t(ExpandCoefBasis(allparam[ibeta0],
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
}
if(nW){
IS_W<- ISpline_W
eta0 <- allparam[ieta0]
for(iW in 1:nW){
if(Intercept_W[[iW]]){
IS_W[[iW]] <- ISpline_W[[iW]] * eta0[iWbeg[iW]:iWend[iW]]
}
else {
IS_W[[iW]]<- ISpline_W[[iW]] * c(0, eta0[iWbeg[iW]:iWend[iW]])
}
}
if(nX + nZ) {
NPHterm <- intTD(rateTD_gamma0alphabetaeta0, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
step=step, Nstep=Nstep,
intweightsfunc=intweightsfunc,
fromT=Y[,1], toT=Y[,2], FirstId=FirstId, LastId=LastId,
gamma0=allparam[igamma0], Zalphabeta=Zalphabeta,
nW = nW, W = W, eta0=allparam[ieta0], iWbeg=iWbeg, iWend=iWend,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
Spline_t = Spline_t, Intercept_t=TRUE,
ISpline_W = IS_W, Intercept_W=Intercept_W)
} else {
NPHterm <- intTD(rateTD_gamma0eta0, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
step=step, Nstep=Nstep,
intweightsfunc=intweightsfunc,
fromT=Y[,1], toT=Y[,2], FirstId=FirstId, LastId=LastId,
nW = nW, W = W, eta0=allparam[ieta0], iWbeg=iWbeg, iWend=iWend,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
ISpline_W = IS_W, Intercept_W=Intercept_W)
}
}
else {
# no VCE effect, same NPH term than ll_flexrsurv_fromto_GA0B0ABE0
if(nX + nZ) {
NPHterm <- intTD(rateTD_gamma0alphabeta, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
step=step, Nstep=Nstep,
intweightsfunc=intweightsfunc,
gamma0=allparam[igamma0], Zalphabeta=Zalphabeta,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
Spline_t = Spline_t, Intercept_t=TRUE)
} else {
NPHterm <- intTD(rateTD_gamma0, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
step=step, Nstep=Nstep, intweightsfunc=intweightsfunc,
gamma0=allparam[igamma0],
Spline_t0=Spt0g, Intercept_t0=Intercept_t0)
}
}
# contribution of non time dependant variables
if( nX0){
ret <- exp(X0 %*% allparam[ialpha0]) * NPHterm
} else {
ret <- NPHterm
}
ret
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.