R/ols-dsresid-vs-pred-plot.R

In cmlopera/olsrr: Tools for Building OLS Regression Models

#' Deleted studentized residual vs fitted values plot
#'
#' @description
#' Plot for detecting violation of assumptions about residuals such as
#' non-linearity, constant variances and outliers. It can also be used to
#' examine model fit.
#'
#' @param model An object of class \code{lm}.
#'
#' @details
#' Studentized deleted residuals (or externally studentized residuals) is the
#' deleted residual divided by its estimated standard deviation. Studentized
#' residuals are going to be more effective for detecting outlying Y
#' observations than standardized residuals. If an observation has an externally
#' studentized residual that is larger than 2 (in absolute value) we can call
#' it an outlier.
#'
#' @return \code{ols_plot_resid_stud_fit} returns  a list containing the
#' following components:
#'
#' \item{outliers}{a tibble with observation number, fitted values and deleted studentized
#' residuals that exceed the \code{threshold} for classifying observations as
#' outliers/influential observations}
#' \item{threshold}{\code{threshold} for classifying an observation as an outlier/influential observation}
#'
#' @section Deprecated Function:
#' \code{ols_dsrvsp_plot()} has been deprecated. Instead use \code{ols_plot_resid_stud_fit()}.
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
#' ols_plot_resid_stud_fit(model)
#'
#' @seealso [ols_plot_resid_lev()], [ols_plot_resid_stand()],
#'   [ols_plot_resid_stud()]
#'
#' @importFrom stats fitted rstudent
#' @importFrom dplyr mutate
#'
#' @export
#'
ols_plot_resid_stud_fit <- function(model) {

  check_model(model)

  fct_color <- NULL
  color     <- NULL
  pred      <- NULL
  dsr       <- NULL
  txt       <- NULL
  obs       <- NULL
  ds        <- NULL

  k <- ols_prep_dsrvf_data(model)

  d <-
    k %>%
    use_series(ds) %>%
    mutate(
      txt = ifelse(color == "outlier", obs, NA)
    )

  f <-
    d %>%
    filter(color == "outlier") %>%
    select(obs, pred, dsr) %>%
    set_colnames(c("observation", "fitted_values", "del_stud_resid"))

  p <- ggplot(d, aes(x = pred, y = dsr, label = txt)) +
    geom_point(aes(colour = fct_color)) +
    scale_color_manual(values = c("blue", "red")) +
    ylim(k$cminx, k$cmaxx) + xlab("Predicted Value") +
    ylab("Deleted Studentized Residual") + labs(color = "Observation") +
    ggtitle("Deleted Studentized Residual vs Predicted Values") +
    geom_hline(yintercept = c(-2, 2), colour = "red") +
    geom_text(hjust = -0.2, nudge_x = 0.15, size = 3, family = "serif",
              fontface = "italic", colour = "darkred", na.rm = TRUE) +
    annotate(
      "text", x = Inf, y = Inf, hjust = 1.5, vjust = 2,
      family = "serif", fontface = "italic", colour = "darkred",
      label = paste0("Threshold: abs(", 2, ")")
    )

  suppressWarnings(print(p))
  result <- list(outliers = f, threshold = 2, plot = p)
  invisible(result)

}

#' @export
#' @rdname ols_plot_resid_stud_fit
#' @usage NULL
#'
ols_dsrvsp_plot <- function(model) {
  .Deprecated("ols_plot_resid_stud_fit()")
}