rtgamma

Truncated Gamma draws are needed for the standard
 deviation of the random effects Gibbs conditional.

Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.

Rodney Sparapani

BART

Bayesian Additive Regression Trees

Robert McCulloch

Robert Gramacy

Matthew Pratola

Charles Spanbauer

Martyn Plummer

Nicky Best

Kate Cowles

Karen Vines

rtgamma function

<dl> <dt>n</dt>
<dd>Number of samples.</dd> <dt>shape</dt>
<dd>Sampling from a truncated Gamma where
 \(E[x]=shape/rate\).</dd> <dt>rate</dt>
<dd>This parameter is the inverse of the scale
 which is an alternative representation for the Gamma distribution.</dd> <dt>a</dt>
<dd>The truncation point, i.e., \(a&lt;x\).</dd> </dl>

Arguments

Testing truncated Gamma sampling — rtgamma

<dl>

 <dt>n</dt>
<dd>Number of samples.</dd>

 <dt>shape</dt>
<dd>Sampling from a truncated Gamma where
 \(E[x]=shape/rate\).</dd>

 <dt>rate</dt>
<dd>This parameter is the inverse of the scale
 which is an alternative representation for the Gamma distribution.</dd>

 <dt>a</dt>
<dd>The truncation point, i.e., \(a&lt;x\).</dd>

 </dl>

Testing truncated Gamma sampling

rtgamma: Testing truncated Gamma sampling

Description

Usage

Value

Arguments

References

Examples