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#' model_mbic
#'
#' @name model_mbic
#'
#' @description Function to model mbic selection process on a given dataframe
#'
#' @param data a dataframe containing an outcome variable to be permuted (usually coming from nested bootstrap data)
#' @param outcome the outcome as a string (i.e. "y")
#' @param type model type, either "linear" or "logistic"
#' @keywords internal
#' @import bigstep
#' @import dplyr
#' @importFrom tibble rownames_to_column
#' @importFrom stats coef
#' @importFrom utils globalVariables
#' @importFrom stringr str_remove_all
#'
utils::globalVariables(c(".", "variable", "Estimate"))
model_mbic <- function(data, outcome, type) {
data <- data %>%
as.data.frame()
# TODO all_of might not work with doparralel, perhaps change to !!outcome ? see tidymodels parralel explanation.
y_temp <- data %>%
select(all_of(outcome)) %>%
as.matrix()
x_temp <- data %>%
select(-all_of(outcome))
bigstep_prepped <- bigstep::prepare_data(y_temp, x_temp, verbose = FALSE, type = type)
bigstep_prepped %>%
reduce_matrix(minpv = 0.01) %>%
fast_forward(crit = mbic) %>%
multi_backward(crit = mbic) %>%
summary() %>%
stats::coef() %>%
as.data.frame() %>%
rownames_to_column(., var = "variable") %>%
mutate(variable = str_remove_all(variable, "`")) %>%
filter(
!grepl("Xm[, -1]", variable)
) %>%
rename(estimate = Estimate) %>%
select(variable, estimate)
}
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