Nothing
############################################
## Integration Using the Trapezoidal Rule ##
############################################
integr <- function(old_coefs, added_td, coefs, desMat, predictions = list(), controls = list(), ...){
###
# x currently added base-learner
# coefs the current coefficients
# predictions list of 'offset', 'tconst', 'td'
# offset is the exp(offset)
# tconst is the exp(prediction for time-constant base-learners)
# td is the exp(prediction for time-dependent base-learners)
# controls list of 'grid' , 'trapezoid_width', 'upper', 'nu', 'which'
# grid a (time) grid
# trapezoid_width the distances in grid
# nu fraction of fit to include
# upper vector (here: of times t[i])
# which can be "NULL", "score", "fisher" returning the integral needed
# for the log likelihood ("NULL"), score vector or fisher matrix
if (length(predictions$offset) != 1) stop(sQuote("offset"), " must be a single constant")
coefs <- coefs + controls$nu * old_coefs
foo <- desMat %*% coefs
if(added_td){
foo <- matrix(foo, nrow = length(controls$grid), ncol = length(controls$grid[[1]]), byrow = TRUE)
predictions$td <- predictions$td * exp(foo)
} else {
predictions$tconst <- predictions$tconst * exp(foo)
}
###
# integrating over time for time dependent part by applying the trapezoid rule
# if controls$grid != NULL, i.e. time-dependent base-learners present
# returning exp(0) * time as integral if no time-dependent base-learners are present
if (!is.null(controls$grid)){
sub <- length(controls$grid[[1]])
predictions$td <- controls$trapezoid_width * (0.5 * (predictions$td[,1] + predictions$td[,sub]) + apply(predictions$td[,2:(sub-1)], 1, sum))
} else {
predictions$td <- controls$upper
}
###
# multiplying time dependent integral with constants
return(predictions$offset * predictions$tconst * predictions$td)
}
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