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#' Binomial logistic regression multivariable models: \code{finalfit} model
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#' wrapper
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#'
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#' Using \code{finalfit} conventions, produces a multivariable binomial
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#' logistic regression model for a set of explanatory variables against a
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#' binary dependent.
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#'
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#' Uses \code{\link[stats]{glm}} with \code{finalfit} modelling conventions.
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#' Output can be passed to \code{\link{fit2df}}.
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#'
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#' @param .data Data frame.
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#' @param dependent Character vector of length 1: name of depdendent variable
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#'   (must have 2 levels).
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#' @param explanatory Character vector of any length: name(s) of explanatory
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#'   variables.
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#' @param family Character vector quoted or unquoted of the error distribution
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#'   and link function to be used in the model, see \code{\link[stats]{glm}}.
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#' @param ... Other arguments to pass to \code{\link[stats]{glm}}.
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#' @return A multivariable \code{\link[stats]{glm}} fitted model.
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#'
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#' @seealso \code{\link{fit2df}, \link{finalfit_merge}}
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#' @family finalfit model wrappers
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#' @export
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#'
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#' @examples
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#' library(finalfit)
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#' library(dplyr)
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#' explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
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#' dependent = "mort_5yr"
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#'
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#' colon_s %>%
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#' 	glmmulti(dependent, explanatory) %>%
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#' 	fit2df(estimate_suffix=" (univariable)")
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#' 
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glmmulti <- function(.data, dependent, explanatory, family = "binomial", ...){
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  ff_eval(
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    glm(ff_formula(dependent, explanatory),
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        data = .data, family = family, ...)
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  )
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}

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