Check model predictor for heterogeneity bias — check_heterogeneity

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, by = NULL, nested = FALSE)

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.

by

Character vector (or formula) with the name of the variable that indicates the group- or cluster-ID. For cross-classified or nested designs, by can also identify two or more variables as group- or cluster-IDs. If the data is nested and should be treated as such, set nested = TRUE. Else, if by defines two or more variables and nested = FALSE, a cross-classified design is assumed. If x is a model object, this argument will be ignored.

For nested designs, by can be:

a character vector with the name of the variable that indicates the levels, ordered from highest level to lowest (e.g. by = c("L4", "L3", "L2").
a character vector with variable names in the format by = "L4/L3/L2", where the levels are separated by /.

See also section De-meaning for cross-classified designs and De-meaning for nested designs below.

nested

Logical, if TRUE, the data is treated as nested. If FALSE, the data is treated as cross-classified. Only applies if by contains more than one variable.

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.

Examples

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