When conducting a joint-significant test, different models are fitted to the data. This function tests assumptions regarding these models using the performance package.

The assumptions test are performed using check_normality, check_heteroscedasticity, and check_outliers.

Note that check_assumptions returns a mediation_model object.

check_assumptions(
  mediation_model,
  tests = c("normality", "heteroscedasticity")
)

Arguments

mediation_model

An object of class mediation_model.

tests

A character vector indicating which test to run. Supported test includes "normality", "heteroscedasticity", and "outliers"

Value

Invisibly returns an object of class mediation_model.

See also

Other assumption checks: plot_assumptions()

Examples

data(ho_et_al) ho_et_al$condition_c <- build_contrast(ho_et_al$condition, "Low discrimination", "High discrimination") my_model <- mdt_simple(data = ho_et_al, IV = condition_c, DV = hypodescent, M = linkedfate) check_assumptions(my_model)
#> X -> Y #> Warning: Non-normality of residuals detected (p < .001). #> OK: Error variance appears to be homoscedastic (p = 0.353). #> X -> M #> Warning: Non-normality of residuals detected (p < .001). #> Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.004). #> X + M -> Y #> Warning: Non-normality of residuals detected (p < .001). #> Warning: Heteroscedasticity (non-constant error variance) detected (p < .001). #>