View source: R/diblasi_bowman.R
diblasi_bowman | R Documentation |
This function implements the nonparametric test of \insertCiteDiblasi97;textualskedastic for testing for heteroskedasticity in a linear regression model.
diblasi_bowman( mainlm, distmethod = c("moment.match", "bootstrap"), H = 0.08, ignorecov = TRUE, B = 500L, seed = 1234, statonly = FALSE )
mainlm |
Either an object of |
distmethod |
A character specifying the method by which to estimate
the p-values, either |
H |
A hyperparameter denoting the bandwidth matrix in the kernel
function used for weights in nonparametric smoothing. If a double of
length 1 (the default), |
ignorecov |
A logical. If |
B |
An integer specifying the number of nonparametric bootstrap
replications to be used, if |
seed |
An integer specifying a seed to pass to
|
statonly |
A logical. If |
The test entails undertaking a transformation of the OLS residuals s_i=√{|e_i|}-E_0(√{|e_i|}), where E_0 denotes expectation under the null hypothesis of homoskedasticity. The kernel method of nonparametric regression is used to fit the relationship between these s_i and the explanatory variables. This leads to a test statistic T that is a ratio of quadratic forms involving the vector of s_i and the matrix of normal kernel weights. Although nonparametric in its method of fitting the possible heteroskedastic relationship, the distributional approximation used to compute p-values assumes normality of the errors.
An object of class
"htest"
. If object is
not assigned, its attributes are displayed in the console as a
tibble
using tidy
.
mtcars_lm <- lm(mpg ~ wt + qsec + am, data = mtcars) diblasi_bowman(mtcars_lm) diblasi_bowman(mtcars_lm, ignorecov = FALSE) diblasi_bowman(mtcars_lm, distmethod = "bootstrap")
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