rglg

<code>rglg</code> is used to generate random numbers for a generalized log-gamma distribution.

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

rglg function

<dl><dt>n</dt>
<dd>numeric, size of the random sample.</dd>
<dt>location</dt>
<dd>numeric, represents the location parameter of a generalized log-gamma distribution. Default value is 0.</dd>
<dt>scale</dt>
<dd>numeric, represents the scale parameter of a generalized log-gamma distribution. Default value is 1.</dd>
<dt>shape</dt>
<dd>numeric, represents the shape parameter of a generalized log-gamma distribution. Default value is 1.</dd></dl>

Arguments

Carlos Alberto Cardozo Delgado &lt;cardozorpackages@gmail.com&gt;

Author

Random number generation for a generalized log-gamma distribution — rglg

<dl>

<dt>n</dt>
<dd>numeric, size of the random sample.</dd>


<dt>location</dt>
<dd>numeric, represents the location parameter of a generalized log-gamma distribution. Default value is 0.</dd>


<dt>scale</dt>
<dd>numeric, represents the scale parameter of a generalized log-gamma distribution. Default value is 1.</dd>


<dt>shape</dt>
<dd>numeric, represents the shape parameter of a generalized log-gamma distribution. Default value is 1.</dd>

</dl>

rglg: Random number generation for a generalized log-gamma distribution

Description

Usage

Value

Arguments

Author

References

Examples