logLik.sglg

logLik.sglg extracts log-likehood from a model from an object of class 'sglg'.

Set of tools to fit a linear multiple or semi-parametric regression
models with the possibility of non-informative random right-censoring.
Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression
or semi-parametric functions, whose non-parametric components may be approximated
by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes
the generalized extreme value and standard normal distributions as important special cases. Inference is based on penalized likelihood and bootstrap methods.
Also, some numerical and graphical devices for diagnostic of the fitted models are offered.

Carlos Cardozo Delgado

sglg

Fitting Semi-Parametric Generalized log-Gamma Regression Models

logLik.sglg function

<dl><dt>object</dt>
<dd>an object of the class sglg. This object is returned from the call to glg(), sglg(), survglg() or ssurvglg() function.</dd>
<dt>...</dt>
<dd>other arguments.</dd></dl>

Arguments

Extract Log-Likehood — logLik.sglg

<dl>

<dt>object</dt>
<dd>an object of the class sglg. This object is returned from the call to glg(), sglg(), survglg() or ssurvglg() function.</dd>


<dt>...</dt>
<dd>other arguments.</dd>

</dl>

logLik.sglg: Extract Log-Likehood

Description

Usage

Arguments