Run multiple heteroscedasticity diagnostics

Convenience wrapper that executes several heteroscedasticity tests on a fitted linear model.

Usage

runHeteroTests(
  model,
  data = NULL,
  tests = c("white", "breusch_pagan"),
  use_cache = TRUE,
  chunk_threshold_mb = 100,
  chunk_size = 10000,
  progress = interactive()
)

Details

By default runs performWhiteTest and performBPTest, but additional tests can be requested. Custom diagnostics registered via registerDiagnostic are also available.

Arguments

model

an object of class lm.

data

optional data frame used to fit model. If omitted, model.frame(model) is used.

tests

character vector of test names to run. Supported values are any names registered via registerDiagnostic.

use_cache

logical indicating whether cached results should be reused when available. Requires the digest package and defaults to TRUE.

chunk_threshold_mb

numeric threshold (in megabytes) above which streaming implementations are preferred for supported diagnostics.

chunk_size

integer number of observations processed per chunk when streaming diagnostics.

progress

logical flag controlling whether textual progress bars are displayed when running multiple diagnostics.

Value

A named list of htest objects.

References

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. Breusch, T. S., & Pagan, A. R. (1979). A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica, 47(5), 1287–1294.

Examples

data(mtcars)
m <- lm(mpg ~ wt + qsec, data = mtcars)
# Default: White and Breusch-Pagan
runHeteroTests(m, mtcars)
#> $white
#> 
#> 	White's test for heteroscedasticity
#> 
#> data:  model
#> X-squared = 11.822, df = 5, p-value = 0.0373
#> alternative hypothesis: heteroscedasticity present
#> 
#> 
#> $breusch_pagan
#> 
#> 	Breusch-Pagan test for heteroscedasticity
#> 
#> data:  mpg ~ wt + qsec
#> X-squared = 3.1348, df = 2, p-value = 0.2086
#> 
#> 
#> attr(,"class")
#> [1] "hetero_test_suite" "list"             
#> attr(,"tests")
#> [1] "white"         "breusch_pagan"
#> attr(,"model")
#> 
#> Call:
#> lm(formula = mpg ~ wt + qsec, data = mtcars)
#> 
#> Coefficients:
#> (Intercept)           wt         qsec  
#>     19.7462      -5.0480       0.9292  
#> 
#> attr(,"data_source")
#> [1] "data.frame"
# Specify additional diagnostics
runHeteroTests(m, mtcars, tests = c("white", "koenker", "ncv"))
#> [INFO] Running Koenker test
#> [INFO] Running NCV score test
#> $white
#> 
#> 	White's test for heteroscedasticity
#> 
#> data:  model
#> X-squared = 11.822, df = 5, p-value = 0.0373
#> alternative hypothesis: heteroscedasticity present
#> 
#> 
#> $koenker
#> 
#> 	Koenker studentized Breusch-Pagan test
#> 
#> data:  mpg ~ wt + qsec
#> X-squared = 3.0858, df = 2, p-value = 0.2138
#> 
#> 
#> $ncv
#> 
#> 	Cook-Weisberg score test for non-constant variance
#> 
#> data:  mpg ~ wt + qsec; variance model: fitted values
#> X-squared = 0.59099, df = 1, p-value = 0.442
#> alternative hypothesis: error variance depends on the variance model
#> 
#> 
#> attr(,"class")
#> [1] "hetero_test_suite" "list"             
#> attr(,"tests")
#> [1] "white"   "koenker" "ncv"    
#> attr(,"model")
#> 
#> Call:
#> lm(formula = mpg ~ wt + qsec, data = mtcars)
#> 
#> Coefficients:
#> (Intercept)           wt         qsec  
#>     19.7462      -5.0480       0.9292  
#> 
#> attr(,"data_source")
#> [1] "data.frame"
custom <- function(model, data) list(stat = 1)
registerDiagnostic("custom", custom)
runHeteroTests(m, mtcars, tests = c("white", "custom"))
#> $white
#> 
#> 	White's test for heteroscedasticity
#> 
#> data:  model
#> X-squared = 11.822, df = 5, p-value = 0.0373
#> alternative hypothesis: heteroscedasticity present
#> 
#> 
#> $custom
#> $custom$stat
#> [1] 1
#> 
#> 
#> attr(,"class")
#> [1] "hetero_test_suite" "list"             
#> attr(,"tests")
#> [1] "white"  "custom"
#> attr(,"model")
#> 
#> Call:
#> lm(formula = mpg ~ wt + qsec, data = mtcars)
#> 
#> Coefficients:
#> (Intercept)           wt         qsec  
#>     19.7462      -5.0480       0.9292  
#> 
#> attr(,"data_source")
#> [1] "data.frame"