R/computeLinearPredictor_WCE.R

In flexrsurv: Flexible Relative Survival Analysis

.computeLinearPredictor_GA0B0ABE0<-function(allparam,
		Y, X0, X, Z, W, 
		Id, FirstId, LastId=NULL, 
		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), 
		ieta0, iWbeg, iWend, nW,
		ISpline_W =MSplineBasis(knots=NULL,  degree=3,   keep.duplicates=TRUE),
		Intercept_W=TRUE,
		bhlink=c("log", "identity"),
		debug=FALSE,  ...){
	# compute linearpredictor (log rate) if the model
	# rate = invlink(f(t)%*%gamma) exp(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 (with ncol=2 or more)
	#                the time at which the predictors are computed is Y[,1] if ncol=2, Y[,2] if ncol>2
	# 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 
	# 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
	
	
	bhlink  <- match.arg(bhlink)       # type baseline hazard
	
	if(is.null(Z)){
		nZ <- 0
	} else {
		nZ <- Z@nZ
	}
	
	# contribution of non time dependant variables
	if( nX0){
		PHterm <-X0 %*% allparam[ialpha0]
	} else {
		PHterm <- 0.0
	}
	# 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]])
			}
		}
		# eta0 = NULL because IS_W = ISpline_W * eta0
		WCEcontrib <- weighted_cummulative_exposure(Increment=W, fromT=Y[,1], toT=, Y[,2], FirstId=FirstId, LastId=LastId,
				theT=Y[,4], tId=LastId,
				eta0=NULL, iWbeg=iWbeg, iWend=iWend, ISpline_W = IS_W, Intercept_W=Intercept_W)
	}
	
	# spline bases for baseline hazard
	colEndTime <- ifelse(ncol(Y)==2, 1, 2)
	if(is.null(Spline_t0)){
		YT0Gamma0 <- 0.0
	}
	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])
	}
	
	# spline bases for each TD effect
	if(nX + nZ){
		# spline bases for each TD effect at the end of the interval
		YT <- evaluate(Spline_t, Y[,2], intercept=TRUE)
		if(nW){
			linpred <- PHterm + apply(YT * Zalphabeta, 1, sum) + apply(WCEcontrib, 1, sum)
		}
		else  {
			linpred <- PHterm + apply(YT * Zalphabeta, 1, sum)
		}
	} else {
		if(nW){
			linpred <- PHterm + apply(WCEcontrib, 1, sum)
		}
		else {
			linpred <- PHterm 
		}
	}
	
	
	if(bhlink == "log"){
		linpred <- linpred + YT0Gamma0
	} else {
		linpred <- cbind(linpred ,  YT0Gamma0)
		dimnames(linpred)[[2]] <- c("linpred", "baseline")
	}
	
	linpred
}