mixedbiastest {mixedbiastest} | R Documentation |
Bias Diagnostic for Linear Mixed Models
Description
Performs a permutation test to assess the bias of fixed effects in a linear mixed model fitted with 'lmer'. This function computes the test statistic and performs the permutation test, returning an object of class '"mixedbiastest"'.
Usage
mixedbiastest(model, n_permutations = 10000, k_list = NULL, verbose = FALSE)
Arguments
model |
An object of class 'lmerMod' fitted using 'lmer' from the 'lme4' package. |
n_permutations |
Integer. Number of permutations to perform (default is 10000). |
k_list |
Optional list of numeric vectors. Each vector specifies a linear combination of fixed effects to test. If 'NULL', each fixed effect is tested individually. |
verbose |
Logical. If 'TRUE', prints detailed messages during execution. |
Details
**Note:** This function currently supports only models with diagonal random effects covariance matrices (i.e., the G matrix is diagonal). The methodology for non-diagonal G matrices is described in Karl and Zimmerman (2021), but is not implemented in this version of the package.
See the list_fixed_effects
function if you would like to estimate the bias of a contrast of fixed effects.
Value
An object of class "mixedbiastest"
containing:
results_table
A data frame with the test results for each fixed effect or contrast, including bias estimates and p-values.
permutation_values
A list of numeric vectors containing permutation values for each fixed effect or contrast.
model
The original
lmerMod
model object provided as input.
Acknowledgments
Development of this package was assisted by GPT o1-preview, which helped in constructing the structure of much of the code and the roxygen documentation. The code is based on the R code provided by Karl and Zimmerman (2020).
References
Karl, A. T., & Zimmerman, D. L. (2021). A diagnostic for bias in linear mixed model estimators induced by dependence between the random effects and the corresponding model matrix. Journal of Statistical Planning and Inference, 212, 70–80. doi:10.1016/j.jspi.2020.06.004
Karl, A., & Zimmerman, D. (2020). Data and Code Supplement for 'A Diagnostic for Bias in Linear Mixed Model Estimators Induced by Dependence Between the Random Effects and the Corresponding Model Matrix'. Mendeley Data, V1. doi:10.17632/tmynggddfm.1
Examples
if (requireNamespace("plm", quietly = TRUE) && requireNamespace("lme4", quietly = TRUE)) {
library(lme4)
data("Gasoline", package = "plm")
# Fit a random effects model using lme4
mixed_model <- lmer(lgaspcar ~ lincomep + lrpmg + lcarpcap + (1 | country),
data = Gasoline)
result <- mixedbiastest(mixed_model)
print(result)
plot(result)
}
if (requireNamespace("lme4", quietly = TRUE)) {
library(lme4)
example_model <- lmer(y ~ x + (1| group), data = example_data)
result2 <- mixedbiastest(example_model)
print(result2)
plot(result2)
#Simulate data
set.seed(123)
n_groups <- 30
n_obs_per_group <- 10
group <- rep(1:n_groups, each = n_obs_per_group)
x <- runif(n_groups * n_obs_per_group)
beta0 <- 2
beta1 <- 5
sigma_u <- 1
sigma_e <- 0.5
u <- rnorm(n_groups, 0, sigma_u)
e <- rnorm(n_groups * n_obs_per_group, 0, sigma_e)
y <- beta0 + beta1 * x + u[group] + e
data_sim <- data.frame(y = y, x = x, group = factor(group))
model3 <- lmer(y ~ x + (1 | group), data = data_sim)
result3 <- mixedbiastest(model3, verbose = TRUE)
plot(result3)
}