print.pgam

regression

smooth

This work is an extension of the state space model for Poisson count data, Poisson-Gamma model, towards a semiparametric specification. Just like the generalized additive models (GAM), cubic splines are used for covariate smoothing. The semiparametric models are fitted by an iterative process that combines maximization of likelihood and backfitting algorithm.

Washington Junger

pgam

Poisson-Gamma Additive Models

print.pgam function

<dl> <dt>x</dt>
<dd>object of class <code>summary.pgam</code> holding the fitted model information</dd> <dt>digits</dt>
<dd>number of decimal places for output</dd> <dt>...</dt>
<dd>further arguments passed to method</dd></dl>

Arguments

Washington Leite Junger <a href="/link/wjunger%40ims.uerj.br?package=pgam&version=0.4.17" data-mini-rdoc="pgam::wjunger@ims.uerj.br">wjunger@ims.uerj.br</a> and Antonio Ponce de Leon <a href="/link/ponce%40ims.uerj.br?package=pgam&version=0.4.17" data-mini-rdoc="pgam::ponce@ims.uerj.br">ponce@ims.uerj.br</a>

Author

Model output — print.pgam

<dl>

 <dt>x</dt>
<dd>object of class <code>summary.pgam</code> holding the fitted model information</dd>

 <dt>digits</dt>
<dd>number of decimal places for output</dd>

 <dt>...</dt>
<dd>further arguments passed to method</dd>

</dl>

Washington Leite Junger <a href='mailto:wjunger@ims.uerj.br'>wjunger@ims.uerj.br</a> and Antonio Ponce de Leon <a href='mailto:ponce@ims.uerj.br'>ponce@ims.uerj.br</a>

print.pgam: Model output

Description

Usage

Value

Arguments

Author

Details

See Also