Plot of residuals versus linear predictor values

Usage

residuals_linpred_plot(
  model,
  type = c("deviance", "pearson", "response", "pit", "quantile"),
  ylab = NULL,
  xlab = NULL,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  point_col = "black",
  point_alpha = 1,
  line_col = "red",
  seed = NULL
)

Arguments

model: a fitted model. Currently only class "gam".
type: character; type of residuals to use. One of "deviance", "response", "pearson", "pit", and "quantile" residuals are allowed. "pit" uses probability integral transform (PIT) residuals, which, if the model is correct should be approximately uniformly distributed, while "quantile" transforms the PIT residuals through application of the inverse CDF of the standard normal, and therefore the quantile residuals should be approximately normally distributed (mean = 0, sd = 1) if the model is correct. PIT and quantile residuals are not yet available for most families that can be handled by gam(), but most standard families are supported, e.g. those used by glm().
ylab: character or expression; the label for the y axis. If not supplied, a suitable label will be generated.
xlab: character or expression; the label for the y axis. If not supplied, a suitable label will be generated.
title: character or expression; the title for the plot. See ggplot2::labs().
subtitle: character or expression; the subtitle for the plot. See ggplot2::labs().
caption: character or expression; the plot caption. See ggplot2::labs().
point_col: colour used to draw points in the plots. See graphics::par() section Color Specification. This is passed to the individual plotting functions, and therefore affects the points of all plots.
point_alpha: numeric; alpha transparency for points in plots.
line_col: colour specification for 1:1 line.
seed: integer; random seed to use for PIT or quantile residuals.