aod1.3.3 package

Analysis of Overdispersed Data

aic-class

Representation of Objects of Formal Class "aic"

AIC

Akaike Information Criteria

anova.glimML

Likelihood-Ratio Tests for Nested ML Models

aod-pkg

Analysis of Overdispersed Data

betabin

Beta-Binomial Model for Proportions

coef

Methods for Function "coef" in Package "aod"

deviance

Methods for Function "deviance" in Package "aod"

df.residual

Methods for Function "df.residual" in Package "aod"

donner

Test of Proportion Homogeneity using Donner's Adjustment

drs-class

Representation of Objects of Formal Class "drs"

fitted

Methods for Function "fitted" in Package "aod"

glimML-class

Representation of Models of Formal Class "glimML"

glimQL-class

Representation of Models of Formal Class "glimQL"

iccbin-class

Representation of Objects of Formal Class "iccbin"

iccbin

Intra-Cluster Correlation for Binomial Data

invlink

Transformation from the Link Scale to the Observation Scale

link

Transformation from the Observation Scale to the Link Scale

logLik

Methods for Functions "logLik" in Package "aod"

negbin

Negative-Binomial Model for Counts

predict

Methods for Function "predict" in Package "aod"

quasibin

Quasi-Likelihood Model for Proportions

quasipois

Quasi-Likelihood Model for Counts

raoscott

Test of Proportion Homogeneity using Rao and Scott's Adjustment

residuals

Residuals for Maximum-Likelihood and Quasi-Likelihood Models

splitbin

Split Grouped Data Into Individual Data

summary.aic

Akaike Information Statistics

summary.glimML-class

Summary of Objects of Class "summary.glimML"

varbin-class

Representation of Objects of Formal Class "varbin"

varbin

Mean, Variance and Confidence Interval of a Proportion

vcov

Methods for Function "vcov" in Package "aod"

wald.test

Wald Test for Model Coefficients

Provides a set of functions to analyse overdispersed counts or proportions. Most of the methods are already available elsewhere but are scattered in different packages. The proposed functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM).