Skip to contents

Check model predictor for heterogeneity bias

Source: R/check_heterogeneity_bias.R
check_heterogeneity_bias.Rd

check_heterogeneity_bias() checks if model predictors or variables may cause a heterogeneity bias, i.e. if variables have a within- and/or between-effect (Bell and Jones, 2015).

Usage

check_heterogeneity_bias(x, select = NULL, group = NULL)

Arguments

x

A data frame or a mixed model object.

select

Character vector (or formula) with names of variables to select that should be checked. If x is a mixed model object, this argument will be ignored.

group

Character vector (or formula) with the name of the variable that indicates the group- or cluster-ID. If x is a model object, this argument will be ignored.

References

  • Bell A, Jones K. 2015. Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data. Political Science Research and Methods, 3(1), 133–153.

See also

For further details, read the vignette https://easystats.github.io/parameters/articles/demean.html and also see documentation for datawizard::demean().

Examples

data(iris)
iris$ID <- sample(1:4, nrow(iris), replace = TRUE) # fake-ID
check_heterogeneity_bias(iris, select = c("Sepal.Length", "Petal.Length"), group = "ID")
#> Possible heterogeneity bias due to following predictors: Sepal.Length, Petal.Length