Nothing
#' Fit an average neural network model.
#'
#' This function wraps the procedure for fitting an
#' average neural network model and makes it accessible
#' to the easyml core framework.
#'
#' @param object A list of class \code{easy_avNNet}.
#' @return A list of class \code{easy_avNNet}.
#' @export
fit_model.easy_avNNet <- function(object) {
# set model arguments
model_args <- object[["model_args"]]
# process model_args
model_args[["x"]] <- as.matrix(object[["X"]])
model_args[["y"]] <- object[["y"]]
# build model
model <- do.call(caret::avNNet, model_args)
object[["model_args"]] <- model_args
object[["model"]] <- model
# write output
object
}
#' Predict values for an average neural network model.
#'
#' This function wraps the procedure for predicting values from
#' a average neural network model and makes it accessible
#' to the easyml core framework.
#'
#' @param object A list of class \code{easy_avNNet}.
#' @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_avNNet <- function(object, newx = NULL) {
newx <- as.matrix(newx)
model <- object[["model"]]
family <- object[["family"]]
if (family == "gaussian") {
type <- "raw"
} else if (family == "binomial") {
type <- "class"
}
preds <- stats::predict(model, newdata = newx, type = type)
preds
}
#' Easily build and evaluate an average neural network model.
#'
#' This function wraps the easyml core framework, allowing a user
#' to easily run the easyml methodology for an average neural network
#' model.
#'
#' @inheritParams easy_analysis
#' @return A list of class \code{easy_avNNet}.
#' @family recipes
#' @examples
#' \dontrun{
#' library(easyml) # https://github.com/CCS-Lab/easyml
#'
#' # Gaussian
#' data("prostate", package = "easyml")
#' results <- easy_avNNet(prostate, "lpsa",
#' n_samples = 10, n_divisions = 10,
#' n_iterations = 2, random_state = 12345,
#' n_core = 1)
#'
#' # Binomial
#' data("cocaine_dependence", package = "easyml")
#' model_args <- list(size = 5, linout = TRUE, trace = FALSE)
#' results <- easy_avNNet(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 = model_args)
#' }
#' @export
easy_avNNet <- 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()) {
easy_analysis(.data, dependent_variable,
algorithm = "avNNet",
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.