Freq.fit-class


S4 class for the frequentist 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

Freq.fit-class function

 <dl>
<dt><code>model</code></dt><dd>character 'OU' or 'CIR'</dd> <dt><code>random</code></dt><dd>numeric 1, 2, or c(1,2)</dd> <dt><code>fixed</code></dt><dd>numeric value of the fixed effect if there is one</dd> <dt><code>gridf</code></dt><dd>matrix of values on which the estimated is done</dd> <dt><code>mu</code></dt><dd>numeric MLE estimator for parametric approach</dd> <dt><code>omega</code></dt><dd>numeric  MLE estimator for parametric approach</dd> <dt><code>cutoff</code></dt><dd>value of the cutoff if there is one</dd> <dt><code>sigma2</code></dt><dd>numeric estimated value of $\sigma^2$</dd> <dt><code>estimf.trunc</code></dt><dd>matrix estimator of the density of $\phi$ for the truncated estimateur of the random effects</dd> <dt><code>estimphi.trunc</code></dt><dd>matrix truncated estimator of the random effects</dd> <dt><code>index</code></dt><dd>index of the used trajectories</dd> <dt><code>estimphi</code></dt><dd>matrix of the estimator of the random effects</dd> <dt><code>estimf</code></dt><dd>estimator of the density of $\phi$</dd> <dt><code>estim.fixed</code></dt><dd>estimator of the fixed parameter if option estim.fix = 1</dd> <dt><code>estim.fix</code></dt><dd>1 if the user asked for the estimation of fixed parameter</dd> <dt><code>bic</code></dt><dd>numeric bic</dd> <dt><code>aic</code></dt><dd>numeric aic</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> </dl>

Freq.fit-class: S4 class for the frequentist estimation results

Description

Arguments

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