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add conditional density function #15

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Feb 9, 2024
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4 changes: 2 additions & 2 deletions DESCRIPTION
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
Package: vinereg
Type: Package
Title: D-Vine Quantile Regression
Version: 0.9.2
Version: 0.10.0
Authors@R: c(
person("Thomas", "Nagler",, "mail@tnagler.com", role = c("aut", "cre")),
person("Dani", "Kraus",,, role = c("ctb"))
Expand All @@ -26,7 +26,7 @@ LinkingTo:
wdm,
RcppThread,
kde1d
RoxygenNote: 7.1.2
RoxygenNote: 7.2.3
Roxygen: list(markdown = TRUE)
Suggests:
knitr,
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1 change: 1 addition & 0 deletions NAMESPACE
Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@ S3method(predict,vinereg)
S3method(print,vinereg)
S3method(summary,vinereg)
export(cll)
export(cpdf)
export(cpit)
export(plot_effects)
export(vinereg)
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7 changes: 7 additions & 0 deletions NEWS.md
Original file line number Diff line number Diff line change
@@ -1,3 +1,10 @@
# vinereg 0.10.0

NEW FEATURE

* New function `pdf()` to compute the conditional log-likelihood.


# vinereg 0.9.2

BUG FIX
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4 changes: 4 additions & 0 deletions R/RcppExports.R
Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,10 @@ cond_dist_cpp <- function(u, vinecop_r, num_threads) {
.Call(`_vinereg_cond_dist_cpp`, u, vinecop_r, num_threads)
}

cond_dens_cpp <- function(u, vinecop_r, num_threads) {
.Call(`_vinereg_cond_dens_cpp`, u, vinecop_r, num_threads)
}

cond_loglik_cpp <- function(u, vinecop_r, num_threads) {
.Call(`_vinereg_cond_loglik_cpp`, u, vinecop_r, num_threads)
}
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35 changes: 35 additions & 0 deletions R/cpit.R
Original file line number Diff line number Diff line change
Expand Up @@ -64,3 +64,38 @@ cll <- function(object, newdata, cores = 1) {
ll_cop <- cond_loglik_cpp(newdata, object$vine, cores)
ll_cop + ll_marg
}

#' Conditional PDF
#'
#' Calculates the conditional density of the response given the covariates.
#'
#' @param object an object of class \code{vinereg}.
#' @param newdata matrix of response and covariate values for which to compute
#' the conditional density
#' @param cores integer; the number of cores to use for computations.
#'
#' @export
#'
#' @examples
#' \dontshow{
#' set.seed(1)
#' }
#' # simulate data
#' x <- matrix(rnorm(500), 250, 2)
#' y <- x %*% c(1, -2)
#' dat <- data.frame(y = y, x = x, z = as.factor(rbinom(250, 2, 0.5)))
#'
#' # fit vine regression model
#' fit <- vinereg(y ~ ., dat)
#'
#' cpdf(fit, dat)
cpdf <- function(object, newdata, cores = 1) {
newdata <- prepare_newdata(newdata, object, use_response = TRUE)
dens_marg <- if (inherits(object$margins[[1]], "kde1d")) {
kde1d::dkde1d(newdata[, 1], object$margins[[1]])
} else {
1
}
newdata <- to_uscale(newdata, object$margins)
cond_dens_cpp(newdata, object$vine, cores) * dens_marg
}
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