Nothing
#' Fit a penalized regression model.
#'
#' This function wraps the procedure for fitting a
#' glmnet model and makes it accessible
#' to the easyml core framework.
#'
#' @param object A list of class \code{easy_glmnet}.
#' @return A list of class \code{easy_glmnet}.
#' @export
fit_model.easy_glmnet <- function(object) {
# set model arguments
model_args <- object[["model_args"]]
# process model_args
model_args[["family"]] <- object[["family"]]
if (!is.null(model_args[["standardize"]])) {
model_args[["standardize"]] <- FALSE
}
model_args[["x"]] <- as.matrix(object[["X"]])
model_args[["y"]] <- object[["y"]]
# build model_cv
model_cv <- do.call(glmnet::cv.glmnet, model_args)
object[["model_cv_args"]] <- model_args
object[["model_cv"]] <- model_cv
# build model
model_args[["nfolds"]] <- NULL
model <- do.call(glmnet::glmnet, model_args)
object[["model_args"]] <- model_args
object[["model"]] <- model
# write output
object
}
#' Predict values for a penalized regression model.
#'
#' This function wraps the procedure for predicting values from
#' a glmnet model and makes it accessible
#' to the easyml core framework.
#'
#' @param object A list of class \code{easy_glmnet}.
#' @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_glmnet <- function(object, newx = NULL) {
newx <- as.matrix(newx)
model <- object[["model"]]
model_cv <- object[["model_cv"]]
s <- model_cv$lambda.min
preds <- stats::predict(model, newx = newx, s = s, type = "response")
preds
}
#' Extract coefficients from a penalized regression model.
#'
#' This function wraps the procedure for extracting coefficients from a
#' glmnet model and makes it accessible
#' to the easyml core framework.
#'
#' @param object A list of class \code{easy_glmnet}.
#' @return A data.frame, the replicated penalized regression coefficients.
#' @export
extract_coefficients.easy_glmnet <- function(object) {
model <- object[["model"]]
model_cv <- object[["model_cv"]]
coefs <- stats::coef(model, s = model_cv$lambda.min)
coefs_df <- data.frame(t(as.matrix(as.numeric(coefs), nrow = 1)))
colnames(coefs_df) <- rownames(coefs)
coefs_df
}
#' Easily build and evaluate a penalized regression model.
#'
#' This function wraps the easyml core framework, allowing a user
#' to easily run the easyml methodology for a glmnet
#' model.
#'
#' @inheritParams easy_analysis
#' @return A list of class \code{easy_glmnet}.
#' @family recipes
#' @examples
#' \dontrun{
#' library(easyml) # https://github.com/CCS-Lab/easyml
#'
#' # Gaussian
#' data("prostate", package = "easyml")
#' results <- easy_glmnet(prostate, "lpsa",
#' n_samples = 10, n_divisions = 10,
#' n_iterations = 2, random_state = 12345,
#' n_core = 1, model_args = list(alpha = 1.0))
#'
#' # Binomial
#' data("cocaine_dependence", package = "easyml")
#' results <- easy_glmnet(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, model_args = list(alpha = 1.0))
#' }
#' @export
easy_glmnet <- 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 = TRUE, variable_importances = FALSE,
predictions = TRUE, model_performance = TRUE,
model_args = list()) {
easy_analysis(.data, dependent_variable, algorithm = "glmnet",
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)
}
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.