#' Binomial logistic regression multivariable models: \code{finalfit} model

#' wrapper

#'

#' Using \code{finalfit} conventions, produces a multivariable binomial

#' logistic regression model for a set of explanatory variables against a

#' binary dependent.

#'

#' Uses \code{\link[stats]{glm}} with \code{finalfit} modelling conventions.

#' Output can be passed to \code{\link{fit2df}}.

#'

#' @param .data Data frame.

#' @param dependent Character vector of length 1: name of depdendent variable

#'   (must have 2 levels).

#' @param explanatory Character vector of any length: name(s) of explanatory

#'   variables.

#' @param family Character vector quoted or unquoted of the error distribution

#'   and link function to be used in the model, see \code{\link[stats]{glm}}.

#' @param ... Other arguments to pass to \code{\link[stats]{glm}}.

#' @return A multivariable \code{\link[stats]{glm}} fitted model.

#'

#' @seealso \code{\link{fit2df}, \link{finalfit_merge}}

#' @family finalfit model wrappers

#' @export

#'

#' @examples

#' library(finalfit)

#' library(dplyr)

#' explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")

#' dependent = "mort_5yr"

#'

#' colon_s %>%

#' 	glmmulti(dependent, explanatory) %>%

#' 	fit2df(estimate_suffix=" (univariable)")

#' 

glmmulti <- function(.data, dependent, explanatory, family = "binomial", ...){

  )

}