Test for constant variance. It assumes that the error terms are normally distributed.

ols_test_breusch_pagan(model, fitted.values = TRUE, rhs = FALSE, multiple = FALSE, p.adj = c("none", "bonferroni", "sidak", "holm"), vars = NA)

model | An object of class |
---|---|

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. |

multiple | Logical; if TRUE, specifies that multiple testing be performed. |

p.adj | Adjustment for p value, the following options are available: bonferroni, holm, sidak and none. |

vars | Variables to be used for heteroskedasticity test. |

`ols_test_breusch_pagan`

returns an object of class `"ols_test_breusch_pagan"`

.
An object of class `"ols_test_breusch_pagan"`

is a list containing the
following components:

breusch pagan statistic

p-value of `bp`

fitted values of the regression model

names of explanatory variables of fitted regression model

logical value indicating if multiple tests should be performed

adjusted p values

variables to be used for heteroskedasticity test

response variable

predictors

Breusch Pagan Test was introduced by Trevor Breusch and Adrian Pagan in 1979. It is used to test for heteroskedasticity in a linear regression model. It test whether variance of errors from a regression is dependent on the values of a independent variable.

Null Hypothesis: Equal/constant variances

Alternative Hypothesis: Unequal/non-constant variances

Computation

Fit a regression model

Regress the squared residuals from the above model on the independent variables

Compute \(nR^2\). It follows a chi square distribution with p -1 degrees of freedom, where p is the number of independent variables, n is the sample size and \(R^2\) is the coefficient of determination from the regression in step 2.

`ols_bp_test()`

has been deprecated. Instead use `ols_test_breusch_pagan()`

.

T.S. Breusch & A.R. Pagan (1979), A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica 47, 1287–1294

Cook, R. D.; Weisberg, S. (1983). "Diagnostics for Heteroskedasticity in Regression". Biometrika. 70 (1): 1–10.

Other heteroskedasticity tests: `ols_test_bartlett`

,
`ols_test_f`

, `ols_test_score`

# model model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars) # use fitted values of the model ols_test_breusch_pagan(model)#> #> Breusch Pagan Test for Heteroskedasticity #> ----------------------------------------- #> Ho: the variance is constant #> Ha: the variance is not constant #> #> Data #> ------------------------------- #> Response : mpg #> Variables: fitted values of mpg #> #> Test Summary #> --------------------------- #> DF = 1 #> Chi2 = 1.429672 #> Prob > Chi2 = 0.231818# use independent variables of the model ols_test_breusch_pagan(model, rhs = TRUE)#> Error in subtract(., 1): could not find function "subtract"# use independent variables of the model and perform multiple tests ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE)#> Error in subtract(., 1): could not find function "subtract"# bonferroni p value adjustment ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'bonferroni')#> Error in subtract(., 1): could not find function "subtract"# sidak p value adjustment ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'sidak')#> Error in subtract(., 1): could not find function "subtract"# holm's p value adjustment ols_test_breusch_pagan(model, rhs = TRUE, multiple = TRUE, p.adj = 'holm')#> Error in subtract(., 1): could not find function "subtract"