Nothing
opg_flexrsurv_fromto_G0A0B0AB<-function(allparam, Y, X0, X, Z,
expected_rate,
weights=NULL,
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.gr=FALSE, ...){
# compute the outer product of the gradient to estimate Fisher Information (expected informataion matrix) for Type I censoring
# I(G0A0B0AB) = -E(H(L)) = E(grad(L) t(grad(L))
# compute gradient of the log likelihood of the mahaboubi model for each data
# rate = exp( f(t)%*%gamma + X0%*%alpha0 + X%*%beta0(t) + sum( alphai(zi)betai(t) ))
# then compute the mean of grad(L) t(grad(L)
#
# first part is similar to gr_
#
#
#################################################################################################################
#################################################################################################################
# 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)
# expected_rate : expected rate at event time T
# weights : vector of weights : LL = sum_i w_i ll_i
# 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 mahaboubi model
if(is.null(Z)){
nZ <- 0
} else {
nZ <- Z@nZ
}
if(is.null(Spline_t0)){
YT0 <- NULL
YT0Gamma0 <- 0.0
Spt0g <- NULL
igamma0 <- 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) {
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=allparam[igamma0], 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=allparam[igamma0], Zalphabeta=Zalphabeta,
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
Spline_t = Spline_t, Intercept_t=TRUE,
debug=debug.gr)
}
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=allparam[igamma0], 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)
YT <- fevaluate(Spline_t, Y[,2], intercept=TRUE)
RatePred <- ifelse(Y[,3] ,
PHterm * exp(YT0Gamma0 + apply(YT * Zalphabeta, 1, sum)),
0)
}
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)
if(is.null(Spline_t0)){
Intb0 <- rep(0.0, dim(Y)[1])
} 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=allparam[igamma0],
Spline_t0=Spt0g, Intercept_t0=Intercept_t0,
debug=debug.gr)
if(!Intercept_t0 & !is.null(Spline_t0)){
Intb0<- Intb0[,-1]
}
}
Intb <- NULL
YT <- NULL
RatePred <- ifelse(Y[,3] ,
PHterm * exp(YT0Gamma0) ,
0)
}
F <- ifelse(Y[,3] ,
RatePred/(RatePred + expected_rate ),
0)
if(nX + nZ) {
if(nX0>0) {
Intb <- Intb * c(PHterm)
}
IntbF <- YT*F - Intb
}
else {
IntbF <- NULL
}
Intb0 <- Intb0 * c(PHterm)
#####################################################################"
# now computes the gradients
# d<ldgamma0
if(is.null(Spline_t0)){
dLdgamma0 <- NULL
}
else {
dLdgamma0 <- YT0 * F - Intb0
}
# dalpha0
if (nX0) {
dLdalpha0 <- ( F - c(PHterm)* NPHterm ) * X0
}
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] * IntbF)
}
else {
dLdbeta0 <- cbind(dLdbeta0, X[,i] * IntbF[,indx_without_intercept])
}
}
}
else {
dLdbeta0 <- NULL
}
if (nZ) {
baseIntbF <- IntbF %*% t(tBeta)
dLdalpha <- NULL
dLdbeta <- NULL
indZ <- getIndex(Z)
for(iZ in 1:nZ){
dLdalpha<- cbind( dLdalpha , Z@DM[,indZ[iZ,1]:indZ[iZ,2]]* baseIntbF[,iZ] )
dLdbeta <- cbind(dLdbeta, IntbF[,-1, drop=FALSE] * Zalpha[, iZ , drop=TRUE])
}
} else {
dLdalpha <- NULL
dLdbeta <- NULL
}
grad <- cbind(dLdgamma0,
dLdalpha0,
dLdbeta0,
dLdalpha,
dLdbeta )
if (!is.null(weights)) {
Fisher <- crossprod(weights*grad , grad)
}
else {
Fisher <- crossprod(grad)
}
if(debug.gr){
attr(Fisher, "intb0") <- Intb0
attr(Fisher, "intb") <- Intb
attr(Fisher, "intbF") <- IntbF
attr(Fisher, "F") <- F
attr(Fisher, "YT0") <- YT0
attr(Fisher, "YT") <- YT
attr(Fisher, "RatePred") <- RatePred
if(debug.gr >1000){
cat("FisherTypeE_fromto_G0A0B0AB: Fischer Infoirmation matrix computed\n")
}
}
Fisher
}
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