1 ```#' Linear regression multivariable models: \code{finalfit} model wrapper ``` 2 ```#' ``` 3 ```#' Using \code{finalfit} conventions, produces a multivariable linear regression ``` 4 ```#' model for a set of explanatory variables against a continuous dependent. ``` 5 ```#' ``` 6 ```#' Uses \code{\link[stats]{lm}} with \code{finalfit} modelling conventions. ``` 7 ```#' Output can be passed to \code{\link{fit2df}}. ``` 8 ```#' ``` 9 ```#' @param .data Dataframe. ``` 10 ```#' @param dependent Character vector of length 1: name of depdendent variable ``` 11 ```#' (must a continuous vector). ``` 12 ```#' @param explanatory Character vector of any length: name(s) of explanatory ``` 13 ```#' variables. ``` 14 ```#' @param ... Other arguments to pass to \code{\link[stats]{lm}}. ``` 15 ```#' @return A multivariable \code{\link[stats]{lm}} fitted model. ``` 16 ```#' ``` 17 ```#' @seealso \code{\link{fit2df}} ``` 18 ```#' @family finalfit model wrappers ``` 19 ```#' @export ``` 20 ```#' ``` 21 ```#' @examples ``` 22 ```#' library(finalfit) ``` 23 ```#' library(dplyr) ``` 24 ```#' ``` 25 ```#' explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") ``` 26 ```#' dependent = "nodes" ``` 27 ```#' ``` 28 ```#' colon_s %>% ``` 29 ```#' lmmulti(dependent, explanatory) %>% ``` 30 ```#' fit2df() ``` 31 ```#' ``` 32 ```lmmulti <- function(.data, dependent, explanatory, ...){ ``` 33 1 ``` ff_eval( ``` 34 1 ``` lm(ff_formula(dependent, explanatory), data = .data, ...) ``` 35 ``` ) ``` 36 ```} ```

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