Residual Diagnostics Functions

A set of functions to calculate residual diagnostics on models, including constructors, a generic function, a test of whether an object is of the residualDiagnostics class, and methods.

Usage

residualDiagnostics(object, ...)

as.residualDiagnostics(x)

is.residualDiagnostics(x)

# S3 method for class 'lm'
residualDiagnostics(
  object,
  ev.perc = 0.001,
  robust = FALSE,
  distr = "normal",
  standardized = TRUE,
  cut = 8L,
  quantiles = TRUE,
  ...
)

Arguments

object

A fitted model object, with methods for model.frame, resid and fitted.

...

Additional arguments passed to methods.

x

A object (e.g., list or a modelDiagnostics object) to test or attempt coercing to a residualDiagnostics object.

ev.perc

A real number between 0 and 1 indicating the proportion of the theoretical distribution beyond which values are considered extreme values (possible outliers). Defaults to .001.

robust

Whether to use robust mean and standard deviation estimates for normal distribution

distr

A character string given the assumed distribution. Passed on to testDistribution. Defaults to “normal”.

standardized

A logical whether to use standardized residuals. Defaults to TRUE generally where possible but may depend on method.

cut

An integer, how many unique predicted values there have to be at least for predicted values to be treated continuously, otherwise they are treated as discrete values. Defaults to 8.

quantiles

A logical whether to calculate quantiles for the residuals. Defaults to TRUE. If FALSE, then do not calculate them. These are based on simple quantiles for each predicted value if the predicted values are few enough to be treated discretely. See cut argument. Otherwise they are based on quantile regression. First trying smoothing splines, and falling back to linear quantil regression if the splines fail. You may also want to turn these off if they are not working well, or are not of value in your diagnostics.

Value

A logical (is.residualDiagnostics) or a residualDiagnostics object (list) for as.residualDiagnostics and residualDiagnostics.

Examples

testm <- stats::lm(mpg ~ hp * factor(cyl), data = mtcars)

resm <- residualDiagnostics(testm)
plot(resm$testDistribution)


resm <- residualDiagnostics(testm, standardized = FALSE)
plot(resm$testDistribution)


## clean up
rm(testm, resm)
if (FALSE) { # \dontrun{

testdat <- data.frame(
  y = c(1, 2, 2, 3, 3, NA, 9000000, 2, 2, 1),
  x = c(1, 2, 3, 4, 5, 6, 5, 4, 3, 2))

residualDiagnostics(
  lm(y ~ x, data = testdat, na.action = "na.omit"),
  ev.perc = .1)$Residuals

residualDiagnostics(
  lm(y ~ x, data = testdat, na.action = "na.exclude"),
  ev.perc = .1)$Residuals

residualDiagnostics(
  lm(sqrt(mpg) ~ hp, data = mtcars, na.action = "na.omit"),
  ev.perc = .1)$Residuals
} # }