Test for heteroskedasticity under the assumption that the errors are independent and identically distributed (i.i.d.).

ols_test_score(model, fitted_values = TRUE, rhs = FALSE, vars = NULL)

Arguments

model

An object of class lm.

fitted_values

Logical; if TRUE, use fitted values of regression model.

rhs

Logical; if TRUE, specifies that tests for heteroskedasticity be performed for the right-hand-side (explanatory) variables of the fitted regression model.

vars

Variables to be used for for heteroskedasticity test.

Value

ols_test_score returns an object of class "ols_test_score". An object of class "ols_test_score" is a list containing the following components:

score

f statistic

p

p value of score

df

degrees of freedom

fv

fitted values of the regression model

rhs

names of explanatory variables of fitted regression model

resp

response variable

preds

predictors

Deprecated Function

ols_score_test() has been deprecated. Instead use ols_test_score().

References

Breusch, T. S. and Pagan, A. R. (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47, 1287–1294.

Cook, R. D. and Weisberg, S. (1983) Diagnostics for heteroscedasticity in regression. Biometrika 70, 1–10.

Koenker, R. 1981. A note on studentizing a test for heteroskedasticity. Journal of Econometrics 17: 107–112.

See also

Other heteroskedasticity tests: ols_test_bartlett, ols_test_breusch_pagan, ols_test_f

Examples

# model model <- lm(mpg ~ disp + hp + wt, data = mtcars) # using fitted values of the model ols_test_score(model)
#> #> Score Test for Heteroskedasticity #> --------------------------------- #> Ho: Variance is homogenous #> Ha: Variance is not homogenous #> #> Variables: fitted values of mpg #> #> Test Summary #> ---------------------------- #> DF = 1 #> Chi2 = 0.6452907 #> Prob > Chi2 = 0.4218014
# using predictors from the model ols_test_score(model, rhs = TRUE)
#> #> Score Test for Heteroskedasticity #> --------------------------------- #> Ho: Variance is homogenous #> Ha: Variance is not homogenous #> #> Variables: disp hp wt #> #> Test Summary #> ---------------------------- #> DF = 3 #> Chi2 = 0.945898 #> Prob > Chi2 = 0.8143398
# specify predictors from the model ols_test_score(model, vars = c('disp', 'wt'))
#> #> Score Test for Heteroskedasticity #> --------------------------------- #> Ho: Variance is homogenous #> Ha: Variance is not homogenous #> #> Variables: disp wt #> #> Test Summary #> ---------------------------- #> DF = 2 #> Chi2 = 0.5349726 #> Prob > Chi2 = 0.7653008