check_heterogeneity_bias: Check model predictor for heterogeneity bias

In performance: Assessment of Regression Models Performance

Check model predictor for heterogeneity bias

Description

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,
  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.
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.
group	Deprecated. Use by instead.

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"), by = "ID")

performance documentation built on Oct. 19, 2024, 1:07 a.m.