R/gr_linkrate_flexrsurv_fromto_GA0B0AB.R

In flexrsurv: Flexible Relative Survival Analysis

gr_link_flexrsurv_fromto_GA0B0AB<-function(allparam, Y, X0, X, Z, 
		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 linear parts of the rate (on the link scale) of the relatice survival model
	# rate = invlink(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=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)
	# 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: gr_linkrate_flexrsurv_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 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 )
	}  
	YT <- fevaluate(Spline_t, Y[,2], intercept=TRUE)
	indx_without_intercept <- 2:getNBases(Spline_t)
	
	#####################################################################"
# now computes the mean score
	
	
# d<dgamma0
	if(is.null(Spline_t0)){
		dLdgamma0 <- NULL
	}
	else {
		dLdgamma0 <- YT0 
	}
	if (nX0) {
		dLdalpha0 <- 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] *  YT)
			}
			else {
				dLdbeta0 <- cbind(dLdbeta0, X[,i]  * YT[,indx_without_intercept])
			}
		}
	}
	else {
		dLdbeta0 <- NULL
	}
	
	if (nZ) { 
		YTbeta <- YT %*% 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]] * YTbeta[,iZ] )
			dLdbeta <- cbind(dLdbeta, YT[,-1, drop=FALSE] * Zalpha[,iZ])
		}
	}
	else {
		dLdalpha <- NULL
		dLdbeta <- NULL
	}
	rep <- cbind(dLdgamma0,          
			dLdalpha0,          
			dLdbeta0,          
			dLdalpha,          
			dLdbeta )
	
	rep
	
}