get.target.logdensity.rho

get.target.density.rho 
Return the log-density of the full conditional distribution for the parameter rho

These functions use smoothing-splines for the assessment of single-member ensembles of climate projections.
- Cheng, C.-I. and P. L. Speckman (2012) <doi:10.1016/j.csda.2012.05.020>.

Guillaume Evin

qualypsoss

Uncertainties of Climate Projections using Smoothing Splines

get.target.logdensity.rho function

<dl><dt>nFull</dt>
<dd>nP x nMO</dd>
<dt>deltaRV</dt>
<dd>variance of the residual terms for the max value of the continuous predictor</dd>
<dt>distSS</dt>
<dd>sum of square distances between the climate change responses and the ANOVA model</dd>
<dt>weight.hetero</dt>
<dd>output of <code>get.vec.weight.hetero</code></dd>
<dt>nMO</dt>
<dd>number of possible simulation chains (missing and non-missing)</dd>
<dt>nP</dt>
<dd>number of continuous predictors (e.g. future times)</dd>
<dt>rho</dt>
<dd>AR(1) correlation parameter in (-1,1)</dd></dl>

Arguments

Author

get.target.density.rho 
Return the log-density of the full conditional distribution for the parameter rho — get.target.logdensity.rho

<dl>

<dt>nFull</dt>
<dd>nP x nMO</dd>


<dt>deltaRV</dt>
<dd>variance of the residual terms for the max value of the continuous predictor</dd>


<dt>distSS</dt>
<dd>sum of square distances between the climate change responses and the ANOVA model</dd>


<dt>weight.hetero</dt>
<dd>output of <code>get.vec.weight.hetero</code></dd>


<dt>nMO</dt>
<dd>number of possible simulation chains (missing and non-missing)</dd>


<dt>nP</dt>
<dd>number of continuous predictors (e.g. future times)</dd>


<dt>rho</dt>
<dd>AR(1) correlation parameter in (-1,1)</dd>

</dl>

get.target.logdensity.rho: get.target.density.rho Return the log-density of the full conditional distribution for the parameter rho

Description

Usage

Value

Arguments

Author