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#' Projection to submodels
#'
#' Perform projection onto submodels of selected sizes or a specified feature
#' combination.
#'
#' @name project
#'
#' @param object Either a \code{refmodel}-type object created by
#' \link[=get_refmodel]{get_refmodel} or \link[=init_refmodel]{init_refmodel},
#' or an object which can be converted to a reference model using
#' \link[=get_refmodel]{get_refmodel}.
#' @param nterms Number of terms in the submodel (the variable combination is
#' taken from the \code{varsel} information). If a list, then the projection
#' is performed for each model size. Default is the model size suggested by
#' the variable selection (see function \code{suggest_size}). Ignored if
#' \code{solution_terms} is specified.
#' @param solution_terms Variable indices onto which the projection is done. If
#' specified, \code{nterms} is ignored.
#' @param cv_search If TRUE, then the projected coefficients after L1-selection
#' are computed without any penalization (or using only the regularization
#' determined by \code{regul}). If FALSE, then the coefficients are the
#' solution from the L1-penalized projection. This option is relevant only if
#' L1-search was used. Default is TRUE for genuine reference models and FALSE
#' if \code{object} is datafit (see \link[=init_refmodel]{init_refmodel}).
#' @param ndraws Number of posterior draws to be projected. Ignored if
#' \code{nclusters} is specified. Default is 400.
#' @param nclusters Number of clusters in the clustered projection.
#' @param intercept Whether to use intercept. Default is \code{TRUE}.
#' @param seed A seed used in the clustering (if \code{nclusters!=ndraws}). Can
#' be used to ensure same results every time. @param regul Amount of
#' regularization in the projection. Usually there is no need for
#' regularization, but sometimes for some models the projection can be
#' ill-behaved and we need to add some regularization to avoid numerical
#' problems.
#' @param regul Ridgre regularization constant to fit the projections.
#' @param ... Currently ignored.
#'
#' @return A list of submodels (or a single submodel if projection was
#' performed onto a single variable combination), each of which contains the
#' following elements:
#' \describe{
#' \item{\code{kl}}{The KL divergence from the reference model to the
#' submodel.} \item{\code{weights}}{Weights for each draw of the projected
#' model.}
#' \item{\code{dis}}{Draws from the projected dispersion parameter.}
#' \item{\code{alpha}}{Draws from the projected intercept.}
#' \item{\code{beta}}{Draws from the projected weight vector.}
#' \item{\code{solution_terms}}{The order in which the variables were added to
#' the submodel.}
#' \item{\code{intercept}}{Whether or not the model contains an
#' intercept.}
#' \item{\code{family}}{A modified \code{\link{family}}-object.}
#' }
#'
#'
#' @examples
#' \donttest{
#' if (requireNamespace("rstanarm", quietly = TRUE)) {
#' ### Usage with stanreg objects
#' n <- 30
#' d <- 5
#' x <- matrix(rnorm(n * d), nrow = n)
#' y <- x[, 1] + 0.5 * rnorm(n)
#' data <- data.frame(x, y)
#'
#' fit <- rstanarm::stan_glm(y ~ X1 + X2 + X3 + X4 + X5, gaussian(),
#' data = data, chains = 2, iter = 500)
#' vs <- varsel(fit)
#'
#' # project onto the best model with 4 variables
#' proj4 <- project(vs, nterms = 4)
#'
#' # project onto an arbitrary variable combination (variable indices 1, 3 and 5)
#' proj <- project(fit, solution_terms = c(1, 3, 5))
#' }
#' }
#'
NULL
#' @rdname project
#' @export
project <- function(object, nterms = NULL, solution_terms = NULL,
cv_search = TRUE, ndraws = 400, nclusters = NULL,
intercept = NULL, seed = NULL, regul = 1e-4, ...) {
if (!("vsel" %in% class(object)) && is.null(solution_terms)) {
stop(
"The given object is not a variable selection -object.",
"Run the variable selection first, or provide the variable ",
"indices (solution_terms)."
)
}
refmodel <- get_refmodel(object)
if (cv_search) {
## use non-cv_searched solution for datafits by default
cv_search <- !inherits(refmodel, "datafit")
}
if (inherits(refmodel, "datafit")) {
ndraws <- nclusters <- 1
}
if (!is.null(solution_terms) &&
any(object$solution_terms[1:length(solution_terms)] != solution_terms)) {
## search path not found, or the given variable combination
## not in the search path, then we need to project the
## required variables
cv_search <- TRUE
}
if (!is.null(solution_terms)) {
## if solution_terms is given, nterms is ignored
## (project only onto the given submodel)
if (!is.null(object$solution_terms)) {
vars <- object$solution_terms
} else {
## project only the given model on a fit object
vars <- setdiff(split_formula(refmodel$formula,
data = refmodel$fetch_data()
), "1")
}
if (max(solution_terms) > length(vars)) {
stop(
"solution_terms contains an index larger than the number of",
"variables in the model."
)
}
solution_terms <- c(vars[solution_terms])
nterms <- length(solution_terms)
} else {
## by default take the variable ordering from the selection
solution_terms <- object$solution_terms
if (is.null(nterms)) {
if (!is.null(object$suggested_size) && !is.na(object$suggested_size)) {
## by default, project onto the suggested model size
nterms <- min(object$suggested_size, length(solution_terms))
} else {
stop(
"No suggested model size found, please specify nterms or solution",
"terms"
)
}
} else {
if (!is.numeric(nterms) || any(nterms < 0)) {
stop("nterms must contain non-negative values.")
}
if (max(nterms) > length(solution_terms)) {
stop(paste(
"Cannot perform the projection with", max(nterms), "variables,",
"because variable selection was run only up to",
length(solution_terms),
"variables."
))
}
}
}
if (is.null(ndraws)) {
ndraws <- min(ndraws, NCOL(refmodel$mu))
} else {
if (ndraws > NCOL(refmodel$mu)) {
stop(
"Number of posterior draws exceeds the number of columns in the ",
"reference model's posterior."
)
}
if (is.null(nclusters)) {
nclusters <- ndraws
}
}
if (is.null(nclusters)) {
nclusters <- 1
} else
if (nclusters > NCOL(refmodel$mu)) {
stop(
"Number of clusters exceeds the number of columns in the reference ",
"model's posterior."
)
}
if (is.null(intercept)) {
intercept <- refmodel$intercept
}
family <- refmodel$family
## get the clustering or subsample
p_ref <- .get_refdist(refmodel,
ndraws = ndraws, nclusters = nclusters, seed = seed
)
## project onto the submodels
subm <- .get_submodels(
search_path = nlist(
solution_terms,
p_sel = object$search_path$p_sel,
sub_fits = object$search_path$sub_fits
),
nterms = nterms, family = family, p_ref = p_ref, refmodel = refmodel,
intercept = intercept, regul = regul, cv_search = cv_search
)
## add family
proj <- lapply(subm, function(model) {
model <- c(model, nlist(family))
model$p_type <- is.null(ndraws)
model$intercept <- intercept
model$extract_model_data <- refmodel$extract_model_data
model$refmodel <- refmodel
class(model) <- "projection"
return(model)
})
## If only one model size, just return the proj instead of a list of projs
.unlist_proj(proj)
}
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