jmcmControl

Construct control structures for joint mean covariance model
fitting

Fit joint mean-covariance models for longitudinal data. The models
and their components are represented using S4 classes and methods. The core
computational algorithms are implemented using the 'Armadillo' C++ library
for numerical linear algebra and 'RcppArmadillo' glue.

Jianxin Pan

jmcm

Joint Mean-Covariance Models using 'Armadillo' and S4

Yi Pan

jmcmControl function

<dl><dt>trace</dt>
<dd>whether or not the value of the objective function and the
parameters should be print on every trace'th iteration.</dd>
<dt>profile</dt>
<dd>whether or not parameters should be estimated sequentially
using the idea of profile likelihood.</dd>
<dt>ignore.const.term</dt>
<dd>whether or not the constant term should be considered 
when calculating log-likelihood and BIC.
function</dd>
<dt>original.poly.order</dt>
<dd>whether or not the original poly order p q d should 
be used</dd>
<dt>errormsg</dt>
<dd>whether or not the error message should be print</dd></dl>

Arguments

Control of Joint Mean Covariance Model Fitting — jmcmControl

<dl>

<dt>trace</dt>
<dd>whether or not the value of the objective function and the
parameters should be print on every trace'th iteration.</dd>


<dt>profile</dt>
<dd>whether or not parameters should be estimated sequentially
using the idea of profile likelihood.</dd>


<dt>ignore.const.term</dt>
<dd>whether or not the constant term should be considered 
when calculating log-likelihood and BIC.
function</dd>


<dt>original.poly.order</dt>
<dd>whether or not the original poly order p q d should 
be used</dd>


<dt>errormsg</dt>
<dd>whether or not the error message should be print</dd>

</dl>

jmcmControl: Control of Joint Mean Covariance Model Fitting

Description

Usage

Arguments