F test

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

Usage

ols_test_f(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.

...

Other arguments.

Value

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

f

f statistic

p

p-value of f

fv

fitted values of the regression model

rhs

names of explanatory variables of fitted regression model

numdf

numerator degrees of freedom

dendf

denominator degrees of freedom

vars

variables to be used for heteroskedasticity test

resp

response variable

preds

predictors

References

Wooldridge, J. M. 2013. Introductory Econometrics: A Modern Approach. 5th ed. Mason, OH: South-Western.

See also

Other heteroskedasticity tests: ols_test_bartlett(), ols_test_breusch_pagan(), ols_test_score()

Examples

# model
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)

# using fitted values
ols_test_f(model)
#> 
#>  F Test for Heteroskedasticity
#>  -----------------------------
#>  Ho: Variance is homogenous
#>  Ha: Variance is not homogenous
#> 
#>  Variables: fitted values of mpg 
#> 
#>       Test Summary        
#>  -------------------------
#>  Num DF     =    1 
#>  Den DF     =    30 
#>  F          =    0.4920617 
#>  Prob > F   =    0.4884154 

# using all predictors of the model
ols_test_f(model, rhs = TRUE)
#> 
#>  F Test for Heteroskedasticity
#>  -----------------------------
#>  Ho: Variance is homogenous
#>  Ha: Variance is not homogenous
#> 
#>  Variables: disp hp wt qsec 
#> 
#>       Test Summary        
#>  -------------------------
#>  Num DF     =    4 
#>  Den DF     =    27 
#>  F          =    0.4594694 
#>  Prob > F   =    0.7647271 

# using fitted values
ols_test_f(model, vars = c('disp', 'hp'))
#> 
#>  F Test for Heteroskedasticity
#>  -----------------------------
#>  Ho: Variance is homogenous
#>  Ha: Variance is not homogenous
#> 
#>  Variables: disp hp 
#> 
#>       Test Summary        
#>  -------------------------
#>  Num DF     =    2 
#>  Den DF     =    29 
#>  F          =    0.4669306 
#>  Prob > F   =    0.631555