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AICcmodavg (version 2.3-4)

Model Selection and Multimodel Inference Based on (Q)AIC(c)

Description

Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes. The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes. The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities. Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs', 'rjags', and 'jagsUI' classes. Functions also implement model selection using BIC. Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.

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Version

Install

install.packages('AICcmodavg')

Monthly Downloads

7,288

Version

2.3-4

License

GPL (>= 2)

Maintainer

Marc J Mazerolle

Last Published

March 6th, 2025

Functions in AICcmodavg (2.3-4)

Likelihood-Ratio Test Corrected for Overdispersion

Flour Beetle Data

Identify Parameters with Large Standard Errors

Computing Confidence Set for the Kullback-Leibler Best Model

GPA Data and Standardized Test Scores

Computing AIC, AICc, QAIC, and QAICc

Compute AIC, AICc, QAIC, and QAICc from User-supplied Input

Heat Expended Following Hardening of Portland Cement

AICcmodavg-defunct

Defunct Functions in AICcmodavg Package

AICcmodavg-package

Model Selection and Multimodel Inference Based on (Q)AIC(c)

Estimate Dispersion for Poisson and Binomial GLM's and GLMM's

Check Convergence of Fitted Model

Compute Covariance Diagnostic for Lambda in N-mixture Models

Blood Calcium Concentration in Birds

Compute Summary Statistics from Detection Histories

Create Model Selection Tables from User-supplied Information Criterion

Compute Summary Statistics from Time to Detection Data

Create Model Selection Tables from Bayesian Analyses

Create Model Selection Tables based on Multiple Comparisons

Newt Capture-mark-recapture Data

Computing BIC or QBIC

Extract Distribution Family and Link Function

Fat Data and Body Measurements

Anuran Larvae Counts in Minnow Traps Across Pond Type

Compute Model-averaged Parameter Estimate (Multimodel Inference)

Habitat Preference of Lizards

Custom Computation of BIC and QBIC from User-supplied Input

Compute MacKenzie and Bailey Goodness-of-fit Test for Single Season, Dynamic, and Royle-Nichols Occupancy Models

Compute Model-averaged Effect Sizes (Multimodel Inference on Group Differences)

Create Model Selection Tables

Compute Model-averaged Parameter Estimate from User-supplied Information Criterion

Format Objects to LaTeX or HTML

Compute Condition Number

Salamander Capture-mark-recapture Data

Create Model Selection Tables from User-supplied Input Based on (Q)AIC(c)

Extract Log-Likelihood of Model

Display Model Summary Corrected for Overdispersion

Compute Importance Values of Variable

Iron Content in Food

Compute Model Selection Relative Frequencies

Gopher Tortoise Distance Sampling Data

Bullfrog Occupancy and Common Reed Invasion

Turkey Weight Gain

Compute Summary Statistics from Distance Sampling Data

Compute Summary Statistics from Count Histories

Compute Model-averaged Predictions

Compute Chi-square Goodness-of-fit Test for N-mixture Models

Compute Model-averaged Parameter Estimate with Shrinkage (Multimodel Inference)

Create Model Selection Tables Based on BIC

Create Model Selection Tables from User-supplied Input Based on (Q)BIC

Frog Dehydration Experiment on Three Substrate Types

Compute Evidence Ratio Between Two Models

Extract SE of Fixed Effects

Extract Predictors from Candidate Model List

Various Utility Functions

Compute Model-averaged Parameter Estimate from User-supplied Input Based on (Q)AIC(c)

Strength of Pine Wood Based on the Density Adjusted for Resin Content

Computing Predicted Values and Standard Errors