Nothing
gr_cumhaz_flexrsurv_fromto_GA0B0AB<-function(allparam,
Y, X0, X, Z,
step, Nstep,
intTD=intTD_NC, intweightsfunc=intweights_CAV_SIM,
intTD_base=intTD_base_NC,
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 gradient of the cumulative hazard of the relatice survival model
# 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=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 : objesct of class DeSignMatrixLPHNLL of time dependent variables (spline basis expended)
# step : lag of subinterval for numerical integration fr each observation
# Nstep : number of lag for each observation
# intTD : function to perform numerical integration
# intweightfunc : function to compute weightsfor numerical integration
# Knots_t0=NULL,Intercept_t0=FALSE, degree_t0=3, Boundary.knots_t0 time spline parameters for baseline hazard
# Knots_t=NULL,Intercept_t=FALSE, degree_t0=, Boundary.knots_t time spline parameters for time-dependant effects (same basis for each TD variable)
# nT0basis : number of spline basis for NPHLIN effects
# nX0 : nb of PH variables dim(X0)=c(nobs, nX0)
# nX : nb of NPHLIN variables dim(X)=c(nobs, nX)
# nTbasis : number of time spline basis
# ... 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 (debug) cat("# computing gradient of the cumulative hazard: gr_cumhaz_flexrsurv_fromto_GA0B0AB\n")
if(is.null(Z)){
nZ <- 0
} else {
nZ <- Z@nZ
}
if(is.null(Spline_t0)){
YT0 <- NULL
YT0Gamma0 <- 0.0
Spt0g <- NULL
igamma0 <- NULL
tmpgamma0 <- NULL
}
else {
igamma0 <- 1:nT0basis
if(Intercept_t0){
tmpgamma0 <- allparam[igamma0]
}
else {
tmpgamma0 <- c(0, allparam[igamma0])
}
# baseline hazard at the end of the interval
Spt0g <- Spline_t0*tmpgamma0
YT0Gamma0 <- predictSpline(Spt0g, Y[,2])
YT0 <- fevaluate(Spline_t0, Y[,2], intercept=Intercept_t0)
}
# contribution of non time dependant variables
if( nX0){
PHterm <-exp(X0 %*% allparam[ialpha0])
} else {
PHterm <- 1
}
# contribution of time d?pendant effect
# parenthesis are important for efficiency
if(nZ) {
# add a row for the first basis
tBeta <- t(ExpandAllCoefBasis(allparam[ibeta], ncol=nZ, value=1))
# Zalpha est la matrice des alpha(Z)
# parenthesis important for speed ?
Zalpha <- Z@DM %*%( diag(allparam[ialpha]) %*% Z@signature )
Zalphabeta <- Zalpha %*% tBeta
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))
}
else {
Zalphabeta <- NULL
}
}
if(nX + nZ) {
NPHterm <- intTD(rateTD_gamma0alphabeta, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
step=step, Nstep=Nstep,
intweightsfunc=intweightsfunc,
gamma0=tmpgamma0, Zalphabeta=Zalphabeta,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
Spline_t = Spline_t, Intercept_t=TRUE)
if(is.null(Spline_t0)){
Intb0 <- rep(0.0, dim(Y)[1])
}
else {
Intb0 <- intTD_base(func=rateTD_gamma0alphabeta, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
Spline=Spline_t0,
step=step, Nstep=Nstep,
intweightsfunc=intweightsfunc,
gamma0=tmpgamma0, Zalphabeta=Zalphabeta,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
Spline_t = Spline_t, Intercept_t=TRUE,
debug=debug)
}
if( identical(Spline_t0, Spline_t)){
Intb <- Intb0
}
else {
Intb <- intTD_base(func=rateTD_gamma0alphabeta, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
Spline=Spline_t,
step=step, Nstep=Nstep,
intweightsfunc=intweightsfunc,
gamma0=tmpgamma0, Zalphabeta=Zalphabeta,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
Spline_t = Spline_t, Intercept_t=TRUE)
}
if(!Intercept_t0 & !is.null(Spline_t0)){
Intb0<- Intb0[,-1]
}
indx_without_intercept <- 2:getNBases(Spline_t)
}
else {
NPHterm <- intTD(rateTD_gamma0, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
step=step, Nstep=Nstep,
intweightsfunc=intweightsfunc,
gamma0=tmpgamma0,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0)
if(is.null(Spline_t0)){
NPHterm <- rep(0.0, dim(Y)[1])
Intb0 <- NPHterm
}
else {
Intb0 <- intTD_base(func=rateTD_gamma0, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
Spline=Spline_t0,
step=step, Nstep=Nstep, intweightsfunc=intweightsfunc,
gamma0=tmpgamma0,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
debug=debug)
if(!Intercept_t0){
Intb0<- Intb0[,-1]
}
}
Intb <- NULL
}
if(nX + nZ) {
if(nX0>0) {
Intb <- Intb * c(PHterm)
}
}
Intb0 <- Intb0 * c(PHterm)
#####################################################################"
# now computes the mean score
# d<dgamma0
if(is.null(Spline_t0)){
dLdgamma0 <- NULL
}
else {
dLdgamma0 <- Intb0
}
if (nX0) {
dLdalpha0 <- X0 * c(PHterm * NPHterm)
}
else {
dLdalpha0 <- NULL
}
if (nX){
# traiter les Intercept_t_NPH
dLdbeta0 <- NULL
for(i in 1:nX){
if ( Intercept_t_NPH[i] ){
dLdbeta0 <- cbind(dLdbeta0, X[,i] * Intb)
}
else {
dLdbeta0 <- cbind(dLdbeta0, X[,i] * Intb[,indx_without_intercept])
}
}
}
else {
dLdbeta0 <- NULL
}
if (nZ) {
baseIntb <- Intb %*% t(tBeta)
indZ <- getIndex(Z)
dLdalpha <- NULL
dLdbeta <- NULL
for(iZ in 1:nZ){
dLdalpha <- cbind(dLdalpha, Z@DM[,indZ[iZ,1]:indZ[iZ,2]] * baseIntb[,iZ])
dLdbeta <- cbind(dLdbeta, Intb[,-1, drop=FALSE] * Zalpha[,iZ])
}
}
else {
dLdalpha <- NULL
dLdbeta <- NULL
}
rep <- cbind(dLdgamma0,
dLdalpha0,
dLdbeta0,
dLdalpha,
dLdbeta )
if(debug){
attr(rep, "intb0") <- Intb0
attr(rep, "intb") <- Intb
}
rep
}
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