summary.pgam {pgam} | R Documentation |
Output of model information
## S3 method for class 'pgam': summary(object, smo.test = FALSE, ...)
object |
object of class pgam holding the fitted model |
smo.test |
Approximate significance test of smoothing terms. It can take long, so default is FALSE |
... |
further arguments passed to method |
Hypothesis tests of coefficients are based o t distribution. Significance tests of smooth terms are approximate for model selection purpose only. Be very careful about the later.
List containing all the information about the model fitted.
Washington Leite Junger wjunger@ims.uerj.br and Antonio Ponce de Leon ponce@ims.uerj.br
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
Junger, W. L. (2004) Modelo Poisson-Gama Semi-Paramérico: Uma Abordagem de Penalização por Rugosidade. MSc Thesis. Rio de Janeiro, PUC-Rio, Departamento de Engenharia Elétrica
Green, P. J., Silverman, B. W. (1994) Nonparametric Regression and Generalized Linear Models: a roughness penalty approach. Chapman and Hall, London
Hastie, T. J., Tibshirani, R. J.(1990) Generalized Additive Models. Chapman and Hall, London
McCullagh, P., Nelder, J. A. (1989). Generalized Linear Models. Chapman and Hall, 2nd edition, London
Pierce, D. A., Schafer, D. W. (1986) Residuals in generalized linear models. Journal of the American Statistical Association, 81(396),977-986
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") summary(m)