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Check model predictor for heterogeneity bias (Deprecated)

Source: R/zzz-deprecated-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 any within-group variance (Bell and Jones, 2015).

We recommend using check_group_variation() instead, for a more detailed and flexible examination of group-wise variability.

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 in datawizard::demean().

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.

See also

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

For a more detailed and flexible examination of group-wise variability, see check_group_variation().

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")
#> `check_heterogeneity_bias()` is deprecated. Please use
#>   `check_group_variation()` instead.
#> Possible heterogeneity bias due to following predictors: Sepal.Length, Petal.Length