Nothing
ll_flexrsurv_fromto_alpha0alpha_bh<-function(alpha0alpha, beta0, beta, gamma0,
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,
nX,
ialpha,
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 = exp( f(t)%*%gamma + X0%*%alpha0 + X%*%beta0(t) + sum( alphai(zi)betai(t) ))
# case where nX0 > 0 ie, there are PH effects
# gamma : vector of coef for baseline hazard
# alpha0alpa ; vector of all coefs for non time dependant variables (may contain non-loglinear terms
# such as spline) AND alpah(Z) for NLG and NPH
# alpha0= alpha0alpha[ialpha0]
# alpha= alpha0alpah[ialpha]
# beta0 ; matrix of all coefs for log-linear but time dependant variables X%*%beta0(t)
# beta : matrix of coefs for beta(t) nTbasis * nTDvars for NLG and NPH
# 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 : 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 : 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
# 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
# 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
# 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(is.null(Z)){
nZ <- 0
} else
{
nZ <- Z@nZ
}
if(Intercept_t0){
tmpgamma0 <- gamma0
}
else {
tmpgamma0 <- c(0, gamma0)
}
# baseline hazard at the end of the interval
YT0Gamma0 <- predictSpline(Spline_t0*tmpgamma0, Y[,1], intercept=Intercept_t0)
# contribution of non time dependant variables
if( nX0){
PHterm <-exp(X0 %*% alpha0alpha[ialpha0])
}
else PHterm <- 1
# contribution of time d?pendant effect
# parenthesis are important for efficiency
if(nZ) {
Zalphabeta <- Z@DM %*%( diag(alpha0alpha[ialpha]) %*% Z@signature %*% t(ExpandAllCoefBasis(beta, ncol=nZ, value=1)) )
if(nX) {
Zalphabeta <- Zalphabeta + X %*% t(ExpandCoefBasis(beta0,
ncol=nX,
splinebasis=Spline_t,
expand=!Intercept_t_NPH,
value=0))
}
}
else {
if(nX) {
Zalphabeta <- X %*% t(ExpandCoefBasis(beta0,
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, Nstep,
intweightsfunc=intweightsfunc,
fromT=Y[,1], toT=Y[,2],
gamma0=gamma0, Zalphabeta=Zalphabeta,
Spline_t0=Spline_t0*tmpgamma0, Intercept_t0=Intercept_t0,
Spline_t = Spline_t, Intercept_t=TRUE)
}
else {
NPHterm <- intTD(rateTD_gamma0, intFrom=Y[,1], intTo=Y[,2], intToStatus=Y[,3],
step=step, Nstep=Nstep,
intweightsfunc=intweightsfunc,
fromT=Y[,1], toT=Y[,2],
gamma0=gamma0,
Spline_t0=Spline_t0*tmpgamma0, Intercept_t0=Intercept_t0)
}
# spline bases for baseline hazard at the end of intervals
YT0 <- evaluate(Spline_t0, Y[,2], intercept=Intercept_t0)
# spline bases for each TD effect
if(nX + nZ){
# spline bases for each TD effect at the end of intervals
YT <- evaluate(Spline_t, Y[,2], intercept=TRUE)
eventterm <- ifelse(Y[,3] ,
log( PHterm * (YT0Gamma0) * exp(apply(YT * Zalphabeta, 1, sum)) + expected_rate ),
0)
}
else {
eventterm <- ifelse(Y[,3] ,
log( PHterm * (YT0Gamma0) + expected_rate ),
0)
}
if (!is.null(weights)) {
ret <- crossprod(eventterm - PHterm * NPHterm , weights)
}
else {
ret <- sum( eventterm - PHterm * NPHterm )
}
ret
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.