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#' Fit a penalized regression model with interactions.
#'
#' This function wraps the procedure for fitting a
#' glinternet model and makes it accessible
#' to the easyml core framework.
#'
#' @param object A list of class \code{easy_glinternet}.
#' @return A list of class \code{easy_glinternet}.
#' @export
fit_model.easy_glinternet <- function(object) {
# set model arguments
model_args <- object[["model_args"]]
# process model_args
X <- as.matrix(object[["X"]])
model_args[["X"]] <- X
model_args[["Y"]] <- object[["y"]]
model_args[["family"]] <- object[["family"]]
cat_vars <- object[["categorical_variables"]]
if (is.null(cat_vars)) {
numLevels <- rep(1, ncol(X))
} else {
numLevels <- cat_vars + 1
}
model_args[["numLevels"]] <- numLevels
# build model
done <- FALSE
while (!done) {
model <- try(do.call(glinternet::glinternet.cv, model_args), silent = TRUE)
done <- class(model) != "try-error"
}
object[["model_args"]] <- model_args
object[["model"]] <- model
# write output
object
}
#' Predict values for a penalized regression model with interactions.
#'
#' This function wraps the procedure for predicting values from
#' a glinternet model and makes it accessible
#' to the easyml core framework.
#'
#' @param object A list of class \code{easy_glinternet}.
#' @param newx A data.frame, the new data to use for predictions.
#' @return A vector, the predicted values using the new data.
#' @export
predict_model.easy_glinternet <- function(object, newx = NULL) {
newx <- as.matrix(newx)
model <- object[["model"]]
stats::predict(model, X = newx)
}
#' Easily build and evaluate a penalized regression model with interactions.
#'
#' This function wraps the easyml core framework, allowing a user
#' to easily run the easyml methodology for a glinternet
#' model.
#'
#' @inheritParams easy_analysis
#' @return A list of class \code{easy_glinternet}.
#' @family recipes
#' @examples
#' \dontrun{
#' library(easyml) # https://github.com/CCS-Lab/easyml
#'
#' # Gaussian
#' data("prostate", package = "easyml")
#' results <- easy_glinternet(prostate, "lpsa",
#' n_samples = 10, n_divisions = 10,
#' n_iterations = 2, random_state = 12345,
#' n_core = 1)
#'
#' # Binomial
#' data("cocaine_dependence", package = "easyml")
#' results <- easy_glinternet(cocaine_dependence, "diagnosis",
#' family = "binomial",
#' exclude_variables = c("subject"),
#' categorical_variables = c("male"),
#' preprocess = preprocess_scale,
#' n_samples = 10, n_divisions = 10,
#' n_iterations = 2, random_state = 12345,
#' n_core = 1)
#' }
#' @export
easy_glinternet <- function(.data, dependent_variable, family = "gaussian",
resample = NULL, preprocess = preprocess_scale,
measure = NULL, exclude_variables = NULL,
categorical_variables = NULL,
train_size = 0.667, foldid = NULL,
survival_rate_cutoff = 0.05,
n_samples = 1000, n_divisions = 1000,
n_iterations = 10, random_state = NULL,
progress_bar = TRUE, n_core = 1,
coefficients = FALSE,
variable_importances = FALSE,
predictions = TRUE, model_performance = TRUE,
model_args = list()) {
n_core <- 1 # TODO need to explore why glinternet pauses on parallelization
easy_analysis(.data, dependent_variable, algorithm = "glinternet",
family = family, resample = resample,
preprocess = preprocess, measure = measure,
exclude_variables = exclude_variables,
categorical_variables = categorical_variables,
train_size = train_size, foldid = foldid,
survival_rate_cutoff = survival_rate_cutoff,
n_samples = n_samples, n_divisions = n_divisions,
n_iterations = n_iterations, random_state = random_state,
progress_bar = progress_bar, n_core = n_core,
coefficients = coefficients,
variable_importances = variable_importances,
predictions = predictions, model_performance = model_performance,
model_args = model_args)
}
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