Nothing
ll_flexrsurv_GA0B0AB_bh<-function(allparam, Y, X0, X, Z,
expected_rate,
weights=NULL,
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 log likelihood of the relative survival model
# rate = (f(t)%*%gamma )*exp( 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
# 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
# weights : vector of weights : LL = sum_i w_i ll_i
# 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 ( debug) cat(" # omputinfg the log likelihood: ll_flexrsurv_GOA0B0AB\n")
if(is.null(Z)){
nZ <- 0
} else {
nZ <- Z@nZ
}
if(is.null(Spline_t0)){
YT0Gamma0 <- 1.0
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])
}
# 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 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_gamma0_bh, intTo=Y[,1], intToStatus=Y[,2],
# step=step, Nstep=Nstep, intweightsfunc=intweightsfunc,
# gamma0=allparam[igamma0],
# Spline_t0=Spt0g, Intercept_t0=Intercept_t0)
# NPHterm <- integrate(Spline_t0, Y[,1], intercep=Intercept_t0) %*% allparam[igamma0]
if(is.null(Spline_t0)){
NPHterm <- 1.0
}
else {
NPHterm <- predict(integrate(Spt0g), Y[,1], intercep=Intercept_t0)
}
}
# spline bases for each TD effect
if(nX + nZ){
# spline bases for each TD effect
YT <- evaluate(Spline_t, Y[,1], intercept=TRUE)
eventterm <- ifelse(Y[,2] ,
log( PHterm * YT0Gamma0 * exp(apply(YT * Zalphabeta, 1, sum)) + expected_rate ),
0)
} else {
eventterm <- ifelse(Y[,2] ,
log( PHterm * YT0Gamma0 + expected_rate ),
0)
}
if (!is.null(weights)) {
if( nX0){
ret <- crossprod(eventterm - PHterm * NPHterm , weights)
} else {
ret <- crossprod(eventterm - NPHterm , weights)
}
}
else {
if( nX0){
ret <- sum( eventterm - PHterm * NPHterm )
} else {
ret <- sum( eventterm - NPHterm )
}
}
if ( debug) {
attr(ret, "eventterm") <- eventterm
attr(ret, "PHterm") <- PHterm
attr(ret, "NPHterm") <- NPHterm
}
ret
}
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