Function reference • actuaRE

All functions

actuaRE-package actuaRE: Handling Hierarchically Structured Risk Factors using Random Effects Models

adjustIntercept(): Adjust the intercept to regain the balance property

BalanceProperty(): Balance property

dataCar: data Car

.addREs(): Add random effects to the data frame

findbars(): Determine random-effects expressions from a formula

fixef(<hierCredGLM>) fixef(<hierCredTweedie>): Extract the fixed-effects estimates from a fitted random effects model

fixed.effects fixef: Extract fixed-effects estimates

hachemeisterLong: Hachemeister Data Set

print(<hierCredGLM>) summary(<hierCredGLM>) fitted(<hierCredGLM>): Class "hierCredGLM" of fitted random effects models estimated with Ohlsson's GLMC algorithm

hierCredGLM(): Combining the hierarchical credibility model with a GLM (Ohlsson, 2008)

print(<hierCredibility>) summary(<hierCredibility>) fitted(<hierCredibility>): Class "hierCredibility" of fitted hierarchical credibility models

hierCredibility(): Hierarchical credibility model of Jewell

print(<hierCredTweedie>) summary(<hierCredTweedie>) fitted(<hierCredTweedie>): Class "hierCredTweedie" of fitted random effects models estimated with Ohlsson's GLMC algorithm

hierCredTweedie(): Combining the hierarchical credibility model with a GLM (Ohlsson, 2008)

is.formula(): Formula

isNested(): Is f1 nested within f2?

glFormula(): Modular Functions for Mixed Model Fits

nobars(): Omit terms separated by vertical bars in a formula

NrUnique(): Number of unique elements in a vector

plotRE(): Visualizing the random effect estimates using ggplot2

predict(<hierCredGLM>): Model predictions

predict(<hierCredibility>): Model predictions

predict(<hierCredTweedie>): Model predictions

print(<BalanceProperty>): Print method for an object of class BalanceProperty

ranef(<hierCredibility>) ranef(<hierCredGLM>) ranef(<hierCredTweedie>): Extract the random effect estimates from a fitted random effects model

ranef: Extract the modes of the random effects

tweedieGLMM(): Fitting a Tweedie GLMM, using the initial estimates of hierCredTweedie

weights(<cpglm>) weights(<speedglm>) weights(<hierCredGLM>) weights(<hierCredTweedie>): Extract the model weights