Model Diagnostics Functions

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

Usage

modelDiagnostics(object, ...)

as.modelDiagnostics(x)

is.modelDiagnostics(x)

# S3 method for lm
modelDiagnostics(
  object,
  ev.perc = 0.001,
  robust = FALSE,
  distr = "normal",
  standardized = TRUE,
  ...
)

Arguments

object

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

...

Additional arguments, passed to residualDiagnostics.

x

An object to test or a list to coerce to a modelDiagnostics 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.

Value

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

as.modelDiagnostics and modelDiagnostics.

Examples

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

md <- modelDiagnostics(testm)
#> Warning: singularity problem
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: singularity problem
#> Warning: tiny diagonals replaced with Inf when calling blkfct
plot(md$residualDiagnostics$testDistribution)

md$extremeValues
#> Empty data.table (0 rows and 3 cols): mpg,Index,EffectType

plot(md)



md <- modelDiagnostics(testm, ev.perc = .1)
#> Warning: singularity problem
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: singularity problem
#> Warning: tiny diagonals replaced with Inf when calling blkfct
md$extremeValues
#>      mpg Index EffectType
#>    <num> <int>     <char>
#> 1:  24.4     8  Residuals
#> 2:  10.4    15  Residuals
#> 3:  10.4    16  Residuals
#> 4:  32.4    18  Residuals
#> 5:  33.9    20  Residuals
#> 6:  21.5    21  Residuals
#> 7:  30.4    28  Residuals
plot(md, ncol = 2)


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))

modelDiagnostics(
  lm(y ~ x, data = testdat, na.action = "na.omit"),
  ev.perc = .1)$extremeValues
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#>        y Index EffectType
#>    <num> <int>     <char>
#> 1: 9e+06     7  Residuals

modelDiagnostics(
  lm(y ~ x, data = testdat, na.action = "na.exclude"),
  ev.perc = .1)$extremeValues
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#> Warning: tiny diagonals replaced with Inf when calling blkfct
#>        y Index EffectType
#>    <num> <int>     <char>
#> 1: 9e+06     7  Residuals

## clean up
rm(testm, md, testdat)