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pgam (version 0.3.2)

Poisson-Gamma Additive Models.

Description

This work is aimed at extending the Poisson-Gamma models towards a more general specification, where the linear predictor of covariates is replaced by an additive predictor of generic functions of these covariates. Just like the generalized additive models (GAM), the linear functions of covariates are a particular case of additive models and the natural cubic splines are used as smoothing functions. The semiparametric specification allows to enlarge the possibilities of application of these models. The semiparametric models are fitted by an iterative process that combines maximization of likelihood and backfitting algorithm.

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Version

Install

install.packages('pgam')

Monthly Downloads

238

Version

0.3.2

License

GPL version 2 or later

Maintainer

Washington Junger

Last Published

August 19th, 2022

Functions in pgam (0.3.2)

logLik.pgam

Loglik extraction
framebuilder

Utility function
AIC.pgam

AIC extraction
deviance.pgam

Deviance extraction
periodogram

Periodogram
backfitting

Backfitting algorithm
envelope

Normal plot with simulated envelope of the residuals.
pgam.parser

Read the model formula and split it into the parametric and nonparametric partitions
pgam.hes2se

Utility function
coef.pgam

Coefficients extraction
pgam.fit

One-step ahead prediction and variance
g

Utility function
predict.pgam

Prediction
pgam.psi2par

Utility function
pgam

Poisson-Gamma Additive Models
residuals.pgam

Residuals extraction
summary.pgam

Summary output
pgam.filter

Estimation of the conditional distributions parameters of the level
elapsedtime

Utility function
link

Utility function
fnz

Utility function
plot.pgam

Plot of estimated curves
pgam.likelihood

Likelihood function to be maximized
aihrio

Sample dataset
lpnorm

Utility function
f

Utility function
intensity

Utility function
pgam.smooth

Smoothing of nonparametric terms
pgam.par2psi

Utility function
print.summary.pgam

Summary output
fitted.pgam

Fitted values extraction