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#' Linear Models
#'
#' Fits linear models.
#'
#' @details
#' \describe{
#' \item{Response types:}{\code{factor}, \code{matrix}, \code{numeric}}
#' }
#'
#' Further model details can be found in the source link below.
#'
#' In calls to \code{\link{varimp}} for \code{LModel}, numeric argument
#' \code{base} may be specified for the (negative) logarithmic transformation of
#' p-values [defaul: \code{exp(1)}]. Transformed p-values are automatically
#' scaled in the calculation of variable importance to range from 0 to 100. To
#' obtain unscaled importance values, set \code{scale = FALSE}.
#'
#' @return \code{MLModel} class object.
#'
#' @seealso \code{\link[stats]{lm}}, \code{\link{fit}}, \code{\link{resample}}
#'
#' @examples
#' fit(sale_amount ~ ., data = ICHomes, model = LMModel)
#'
LMModel <- function() {
MLModel(
name = "LMModel",
label = "Linear Model",
packages = "stats",
response_types = c("factor", "matrix", "numeric"),
weights = TRUE,
predictor_encoding = "model.matrix",
na.rm = TRUE,
params = new_params(environment()),
fit = function(formula, data, weights, ...) {
y <- response(data)
data <- as.data.frame(formula, data = data)
if (is.factor(y)) {
data[[response(formula)]] <- if (nlevels(y) == 2) {
as.numeric(y) - 1
} else {
mat <- model.matrix(~ y - 1)
colnames(mat) <- levels(y)
mat
}
}
stats::lm(
formula, data = data, weights = weights, na.action = na.pass, ...
)
},
predict = function(object, newdata, ...) {
newdata <- as.data.frame(newdata)
predict(object, newdata = newdata)
},
varimp = function(object, base = exp(1), ...) {
varimp_pval(object, test = "F", base = base)
}
)
}
MLModelFunction(LMModel) <- NULL
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