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#' model_mcp
#'
#' @name model_mcp
#'
#' @description Function to model mcp 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 dplyr
#' @import ncvreg
#' @import broom
#' @importFrom tibble rownames_to_column
#' @importFrom stats coef
#' @importFrom utils globalVariables
#' @importFrom stringr str_remove_all
#'
#'
utils::globalVariables(c(".", "variable", "estimate", "x"))
model_mcp <- function(data, outcome, type) {
type <- case_when(
type == "logistic" ~ "binomial",
type == "linear" ~ "gaussian"
)
data <- data %>%
as.data.frame()
y_temp <- data %>%
select(all_of(outcome)) %>%
as.matrix()
x_temp <- data %>%
select(-all_of(outcome))
fit_mcp <- cv.ncvreg(X = x_temp, y = y_temp, family = type)
fit_mcp %>%
coef() %>%
as_tibble(rownames = "variable") %>%
rename(
estimate = value
) %>%
filter(
estimate != 0,
!grepl("Xm[, -1]", variable)
) %>%
mutate(variable = str_remove_all(variable, "`"))
}
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