Bayes.fit-class


S4 class for the Bayesian estimation results


Inference on stochastic differential models Ornstein-Uhlenbeck or
Cox-Ingersoll-Ross, with one or two random effects in the drift function.

Charlotte Dion

mixedsde

Estimation Methods for Stochastic Differential Mixed Effects
Models

Bayes.fit-class function

 <dl>
<dt><code>sigma2</code></dt><dd>vector of posterior samples for $\sigma^2$</dd> <dt><code>mu</code></dt><dd>matrix of posterior samples for $\mu$</dd> <dt><code>omega</code></dt><dd>matrix of posterior samples for $\omega$</dd> <dt><code>alpha</code></dt><dd>matrix of posterior samples for $\alpha$</dd> <dt><code>beta</code></dt><dd>matrix of posterior samples for $\beta$</dd> <dt><code>random</code></dt><dd>1, 2 or c(1,2)</dd> <dt><code>burnIn</code></dt><dd>proposal for the burn-in phase</dd> <dt><code>thinning</code></dt><dd>proposal for the thinning rate</dd> <dt><code>model</code></dt><dd>'OU' or 'CIR'</dd> <dt><code>prior</code></dt><dd>list of prior values, input variable or calculated by the first 10% of series</dd> <dt><code>times</code></dt><dd>vector of observation times, storage of input variable</dd> <dt><code>X</code></dt><dd>matrix of observations, storage of input variable</dd> <dt><code>ind.4.prior</code></dt><dd>indices of series used for the prior parameter calculation, if prior knowledge is availabe it is set to M+1</dd> </dl>

Bayes.fit-class: S4 class for the Bayesian estimation results

Description

Arguments

Slots