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Plot Residuals vs. Observation Order (Autocorrelation Check)

Source: R/lm_plot.ac.R
lm_plot.ac.Rd

Creates a plot of residuals against the sequence/order of observations to visually assess independence and detect autocorrelation. Outliers are labeled by default. Optionally includes p-value result from the Durbin–Watson test for autocorrelation.

Usage

lm_plot.ac(
  mdl,
  ...,
  pval.DW = FALSE,
  parms = lm_plot.parms(mdl),
  df = lm_plot.df(mdl, parms = parms)
)

Arguments

mdl

A fitted model object (typically from lm).

...

Additional arguments (not currently used).

pval.DW

(logical, default = FALSE) Option to show Durbin-Watson p-value on the plot.

parms

A list of plotting parameters, usually from lm_plot.parms().

df

Data frame with augmented model data. Defaults to lm_plot.df(mdl).

Value

A ggplot object representing the residuals vs. order plot. Included as an attribute "parm" is a list containing:

  • lim Plotted limits on x and y axes,

  • pval.DW Option to show Durbin-Watson p-value,

  • DW The htest object with Durbin-Watson test results.

Details

Points are colored and shaped according to whether they are residual outliers (as determined by Tukey's boxplot rule). The function can label points using ggrepel if parm$pts$id$outl or parm$pts$id$reg are set to TRUE.

See also

dwtest, lm_plot.df, lm_plot.parms

Examples

fit <- lm(res ~ ., data = data.frame(time = time(austres), res = austres))
lm_plot.ac(fit, pval.DW = TRUE)