pgam {pgam}R Documentation

Poisson-Gamma Additive Models

Description

Fit Poisson-Gamma Additive Models using the roughness penalty approach

Usage

pgam(formula, dataset, omega = 0.8, beta = 0.1, offset = 1, digits = getOption("digits"),
maxit = 100, eps = 1e-06, lfn.scale=1, control = list(), optim.method = "L-BFGS-B", partial.resid = "response",
smoother = "spline", bkf.eps = 0.001, bkf.maxit = 100, se.estimation = "numerical", verbose = TRUE)

Arguments

formula a model formula. See pgam.parser for details
dataset a data set in the environment search path
omega initial value for the discount factor
beta vector of initial values for covariates coefficients. If a sigle value is supplied it is replicated to fill in the whole vector
offset default is 1. Other value can be supplied here
digits number of decimal places for printing information out
maxit convergence control iterations
eps convergence control criterion
lfn.scale scales the likelihood function and is passed to control in optim. Value must be positive to ensure maximization
control convergence control of optim. See its help for details
optim.method optimization method passed to optim. Different methods can lead to different results, so the user must attempt to the trade off between speed and robustness. For example, BFGS is faster but sensitive to starting values and L-BFGS-B is more robust but slower. See its help for details.
partial.resid type of partial residual to be used if semiparametric fitting. See residuals.pgam for details
smoother smoother to be used in backfitting. See pgam.smooth for details
bkf.eps convergence control criterion for the backfitting algorithm
bkf.maxit convergence control iterations for the backfitting algorithm
se.estimation if numerical numerical standard error of parameters are returned. If analytical then analytical extraction of the standard errors is performed. By setting it to none standard error estimation is avoided
verbose if TRUE information during estimation process is printed out

Details

There are a lot of details to be written. It will be very soon.

Specific information can be obtained on functions help.

This algorithm fits fully parametric Poisson-Gamma model also.

Value

List containing an object of class pgam.

Author(s)

Washington Leite Junger
wjunger@ims.uerj.br

References

Junger, W. L. (2004) Modelo Poisson-Gama Semi-Paramétrico: Uma Abordagem de Penalização por Rugosidade. MSc Thesis. Rio de Janeiro, PUC-Rio, Departamento de Engenharia Elétrica

Harvey, A. C., Fernandes, C. (1989) Time series models for count data or qualitative observations. Journal of Business and Economic Statistics, 7(4):407–417

Campos, E. L., De Leon, A. C. M. P., Fernandes, C. A. C. (2003) Modelo Poisson-Gama para Séries Temporais de Dados de Contagem - Teoria e Aplicações. 10a ESTE - Escola de Séries Temporais e Econometria

Green, P. J., Silverman, B. W. (1994) Nonparametric Regression and Generalized Linear Models: a roughness penalty approach. Chapman and Hall, London

See Also

predict.pgam, pgam.parser, residuals.pgam, backfitting

Examples

library(pgam)
data(aihrio)
attach(aihrio)
form <- ITRESP5~f(WEEK)+HOLIDAYS+rain+PM+g(tmpmax,7)+g(wet,3)
m <- pgam(form,aihrio,omega=.8,beta=.01,maxit=1e2,eps=1e-4,optim.method="BFGS",partial.resid="response")

summary(m)


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