Robust Generalized Linear Models (GLM) using Mixtures
AIC for robmixglm object
BIC for robmixglm object
Coefficients for a robmixglm object
Extract AIC from a Fitted Model
Fitted values.
log Likelikelihood for robmixglm object
Calculate outlier probabilities for each observation.
Test for the presence of outliers.
Plot outlier probabilities.
Predict Method for robmixglm
Print an outlierTest object
Extract Model Residuals
Fits random effects meta-analysis models including robust models
Fits a Robust Generalized Linear Model and Variants
summaryficients for robmixglm object
Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) <doi:10.1080/02664763.2017.1414164>. This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model.