mcd_estimation

an integer vector of numbers of measurements for subject.

a vector of responses for all subjects.

model matrix for the mean structure model.

model matrix for the diagonal matrix.

model matrix for the lower triangular matrix.

starting values for the parameters in the model.

start

when covonly is true, it is used as the given mean.

mean

the values of the objective function and the parameters are
printed for all the trace'th iterations.

trace

whether parameters should be estimated sequentially using the
idea of profile likelihood or not.

profile

whether or not the error message should be print.

errormsg

estimate the covariance structure only, and use given mean.

covonly

optimization method, choose "default" or "BFGS"(vmmin in
R).

optim_method

Fit joint mean-covariance models based on MCD.

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

mcd_estimation function

<dl><dt>m</dt>
<dd>an integer vector of numbers of measurements for subject.</dd>
<dt>Y</dt>
<dd>a vector of responses for all subjects.</dd>
<dt>X</dt>
<dd>model matrix for the mean structure model.</dd>
<dt>Z</dt>
<dd>model matrix for the diagonal matrix.</dd>
<dt>W</dt>
<dd>model matrix for the lower triangular matrix.</dd>
<dt>start</dt>
<dd>starting values for the parameters in the model.</dd>
<dt>mean</dt>
<dd>when covonly is true, it is used as the given mean.</dd>
<dt>trace</dt>
<dd>the values of the objective function and the parameters are
printed for all the trace'th iterations.</dd>
<dt>profile</dt>
<dd>whether parameters should be estimated sequentially using the
idea of profile likelihood or not.</dd>
<dt>errormsg</dt>
<dd>whether or not the error message should be print.</dd>
<dt>covonly</dt>
<dd>estimate the covariance structure only, and use given mean.</dd>
<dt>optim_method</dt>
<dd>optimization method, choose "default" or "BFGS"(vmmin in
R).</dd></dl>

Arguments

Fit Joint Mean-Covariance Models based on MCD — mcd_estimation

<dl>

<dt>m</dt>
<dd>an integer vector of numbers of measurements for subject.</dd>


<dt>Y</dt>
<dd>a vector of responses for all subjects.</dd>


<dt>X</dt>
<dd>model matrix for the mean structure model.</dd>


<dt>Z</dt>
<dd>model matrix for the diagonal matrix.</dd>


<dt>W</dt>
<dd>model matrix for the lower triangular matrix.</dd>


<dt>start</dt>
<dd>starting values for the parameters in the model.</dd>


<dt>mean</dt>
<dd>when covonly is true, it is used as the given mean.</dd>


<dt>trace</dt>
<dd>the values of the objective function and the parameters are
printed for all the trace'th iterations.</dd>


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


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


<dt>covonly</dt>
<dd>estimate the covariance structure only, and use given mean.</dd>


<dt>optim_method</dt>
<dd>optimization method, choose "default" or "BFGS"(vmmin in
R).</dd>

</dl>

mcd_estimation: Fit Joint Mean-Covariance Models based on MCD

Description

Usage

Arguments

See Also