Automated model selection and model-averaging. Provides a wrapper for glm and other functions, automatically generating all possible models (under constraints set by the user) with the specified response and explanatory variables, and finding the best models in terms of some Information Criterion (AIC, AICc or BIC). Can handle very large numbers of candidate models. Features a Genetic Algorithm to find the best models when an exhaustive screening of the candidates is not feasible.
|Author||Vincent Calcagno <email@example.com>|
|Date of publication||2013-04-12 14:36:05|
|Maintainer||Vincent Calcagno <firstname.lastname@example.org>|
|License||GPL (>= 2)|
aic: Computing an IC from a fitted model object
aicc-methods: Methods for Function aicc
aic-methods: Methods for Function aic
bic-methods: Methods for Function bic
coef.glmulti: Model averaging and multimodel inference with glmulti
consensus: Takes a consensus of several glmulti objects
consensus-methods: Consensus method for glmulti objects.
getfit: Accessing coefficients of a fitted model object
getfit-methods: Methods for Function getfit
glmulti: Automated model selection and multimodel inference with...
glmulti-class: Class "glmulti"
glmulti-methods: Methods for Function glmulti: different ways to call glmulti
qaicc-methods: Methods for Function qaicc
qaic-methods: Methods for Function qaic
summary.glmulti: Handling glmulti objects
weightable: Table of relative supports
weightable-methods: Table of relative supports
write: Writing glmulti objects
write-methods: Methods for Function write