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Create a Four-Panel Regression Assumptions Plot

Source: R/lm_plot.4way.R
lm_plot.4way.Rd

Generates a 4-panel diagnostic plot for a multiple regression model, including: 1) Fitted values vs. observed values (check for non-linearity), 2) Quantile–Quantile plot of residuals (check for non-normality), 3) Residuals vs. fitted values (check for heteroskedasticity), 4) Autocorrelation for time series otherwise influence plot (leverage also available).

Usage

lm_plot.4way(
  mdl,
  ...,
  is.ts = FALSE,
  pred.intvl = TRUE,
  pval.SW = FALSE,
  pval.BP = FALSE,
  pval.DW = FALSE,
  cook.loess = FALSE,
  rtn.all = FALSE,
  plt.nms = c("fit", "var", "qq", ifelse(is.ts, "ac", "infl")),
  parms = list()
)

Arguments

mdl

A fitted model object (typically from lm).

...

Additional arguments (not currently used).

is.ts

Logical; TRUE if data are time series, FALSE otherwise.

pred.intvl

Logical; plot prediction interval on fitted vs observed.

pval.SW

Logical; include Shapiro–Wilk p-value in qq plot.

pval.BP

Logical; include Breusch–Pagan p-value in var plot.

pval.DW

Logical; include Durbin–Watson p-value in ac plot.

cook.loess

Logical; overlay Cook's distance loess curve in infl plot.

rtn.all

Logical; return all plots and parameters (vs. 4-way plot only).

plt.nms

Character vector of which panels to plot. Defaults to fit, var, qq, and ac/infl depending on is.ts (Order: start upper left, continue clockwise)

parms

List of overrides to plot formatting parameters (see lm_plot.parms).

Value

A ggplot object representing the 4-way diagnostic panel. Optionally invisibly returns a list containing:

  • p_4way – the combined plot,

  • other elements passed through from the individual plot functions.

Details

This function is a high-level wrapper that calls internal plotting functions (lm_plot.fit, lm_plot.var, lm_plot.qq, and either lm_plot.ac or lm_plot.infl) and assembles their outputs into a combined plot_grid.

Examples


fit <- lm(mpg ~ wt + hp, data = mtcars)
lm_plot.4way(fit, is.ts = FALSE, pval.SW = TRUE)

fit <- lm(Employed ~ ., data = longley)
lm_plot.4way(fit, is.ts = TRUE, pval.DW = TRUE)