JWileymisc (version 1.4.1)

residualDiagnostics: Residual Diagnostics Functions

Description

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 lm
residualDiagnostics(
  object,
  ev.perc = 0.001,
  robust = FALSE,
  distr = "normal",
  standardized = TRUE,
  cut = 4L,
  ...
)

Value

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

as.residualDiagnostics and residualDiagnostics.

Arguments

object

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

...

Additional arguments, not currently used.

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 4.

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) {

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
}