cluster_im_glmRob {mmiCATs}

Cluster-Adjusted Confidence Intervals And p-Values Robust GLMs

Description

Performs cluster-adjusted inference on a robust generalized linear model object, using robust generalized linear regression within each cluster. This function is tailored for models where observations are clustered, and standard errors need adjustment for clustering. The function applies a robust generalized linear regression model to each cluster using the specified family and method, and then aggregates the results.

Usage

cluster_im_glmRob(
  robmod,
  dat,
  cluster,
  ci.level = 0.95,
  drop = TRUE,
  return.vcv = FALSE,
  engine = "robust",
  ...
)

Arguments

robmod

A robust generalized linear model object created using robust::glmRob() or robustbase::glmrob(). It must contain elements 'formula', 'family', and 'method'.

dat

A data frame containing the data used in the model.

cluster

A formula indicating the clustering variable in dat.

ci.level

Confidence level for the confidence intervals, default is 0.95.

drop

Logical; if TRUE, drops clusters where the model does not converge.

return.vcv

Logical; if TRUE, the variance-covariance matrix of the cluster-averaged coefficients will be returned.

engine

Set the engine to "robust" to use robust::glmRob() or "robustbase" to use robustbase::glmrob(). Default is "robust".

...

Additional arguments to be passed to robust::glmRob() or robustbase::glmrob().

Value

An invisible list containing the following elements:

p.values

A matrix of p-values for each independent variable.

ci

A matrix with the lower and upper bounds of the confidence intervals for each independent variable.

vcv.hat

The variance-covariance matrix of the cluster-averaged coefficients, returned if return.vcv is TRUE.

beta.bar

The cluster-averaged coefficients, returned if return.vcv is TRUE.

Examples

iris_bin <- iris
# Create a binary variable for MPG (e.g., MPG > 20)
iris_bin$high_Sepal.Length = as.factor(ifelse(iris_bin$Sepal.Length > 5.8, 1, 0))

robout <- robustbase::glmrob(formula = high_Sepal.Length ~ Petal.Length + Petal.Width,
                             family = binomial,
                             data = iris_bin)
cluster_im_glmRob(robout, dat = iris_bin, ~Species, return.vcv = TRUE,
                  engine = "robustbase")