R/ols-residual-vs-regressor-plot.R

In cmlopera/olsrr: Tools for Building OLS Regression Models

#' Residual vs regressor plot
#'
#' @description
#' Graph to determine whether we should add a new predictor to the model already
#' containing other predictors. The residuals from the model is regressed on the
#' new predictor and if the plot shows non random pattern, you should consider
#' adding the new predictor to the model.
#'
#' @param model An object of class \code{lm}.
#' @param variable New predictor to be added to the \code{model}.
#'
#' @section Deprecated Function:
#' \code{ols_rvsr_plot()} has been deprecated. Instead use \code{ols_plot_resid_regressor()}.
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt, data = mtcars)
#' ols_plot_resid_regressor(model, drat)
#'
#' @seealso [ols_plot_added_variable()], [ols_plot_comp_plus_resid()]
#'
#' @export
#'
ols_plot_resid_regressor <- function(model, variable) {

  check_model(model)

  x <- NULL
  y <- NULL

  d        <- ols_prep_rvsrplot_data(model)
  varyable <- enquo(variable)

  inter <-
    eval(model$call$data) %>%
    select(!! varyable)

  x <- pull(inter, 1)
  y <- residuals(model)
  v <- names(inter)
  k <- tibble(x = x, y = y)

  p <- ggplot(k, aes(x = x, y = y)) +
    geom_point(shape = 1, colour = "blue") +
    xlab(paste(v)) + ylab("Residual") +
    ggtitle(paste("Residual vs", v)) +
    geom_hline(yintercept = 0, colour = "red")

  print(p)

}


#' @export
#' @rdname ols_plot_resid_regressor
#' @usage NULL
#'
ols_rvsr_plot <- function(model, variable) {
  .Deprecated("ols_plot_resid_regressor()")
}