get.mf

getmf

A formula of format <code>y ~ x_1 + x_2*x_3 | z_1*z_2 </code>
where <code>|</code> separates fixed (to the left of <code>|</code>) and random effects.

formula

A boolean scalar used in the case that either fixed or random effects
are entered in <code>formula</code>, but not both, which case the <code>|</code> is not entered
(e.g. <code>y ~ x_1 + x_2*x_3 </code>.  Then, if <code>random.only == TRUE</code> the variables
on the right-hand side are interpreted to be random effects; otherwise fixed
for use in <code><a rd-options="" href="/link/dpgrow?package=growcurves&version=0.2.4.1" data-mini-rdoc="growcurves::dpgrow">dpgrow</a></code> and  <code><a rd-options="" href="/link/dpgrowmm?package=growcurves&version=0.2.4.1" data-mini-rdoc="growcurves::dpgrowmm">dpgrowmm</a></code>.

random.only

Associated data.frame containing names variables in <code>formula</code>

data

Should be left blank for use in <code><a rd-options="" href="/link/dpgrow?package=growcurves&version=0.2.4.1" data-mini-rdoc="growcurves::dpgrow">dpgrow</a></code> and <code><a rd-options="" href="/link/dpgrowmm?package=growcurves&version=0.2.4.1" data-mini-rdoc="growcurves::dpgrowmm">dpgrowmm</a></code>,
where is automatically set to <code>na.fail</code>.

na.action


An internal function to <code><a rd-options="" href="/link/dpgrow?package=growcurves&version=0.2.4.1" data-mini-rdoc="growcurves::dpgrow">dpgrow</a></code> and  <code><a rd-options="" href="/link/dpgrowmm?package=growcurves&version=0.2.4.1" data-mini-rdoc="growcurves::dpgrowmm">dpgrowmm</a></code>


Employs a non-parametric formulation for by-subject random effect
parameters to borrow strength over a constrained number of repeated
measurement waves in a fashion that permits multiple effects per subject.
One class of models employs a Dirichlet process (DP) prior for the subject
random effects and includes an additional set of random effects that
utilize a different grouping factor and are mapped back to clients through
a multiple membership weight matrix; e.g. treatment(s) exposure or dosage.
A second class of models employs a dependent DP (DDP) prior for the subject
random effects that directly incorporates the multiple membership pattern.

Terrance Savitsky

growcurves

Bayesian Semi and Nonparametric Growth Curve Models that
Additionally Include Multiple Membership Random Effects

getmf function

A boolean scalar used in the case that either fixed or random effects
are entered in <code>formula</code>, but not both, which case the <code>|</code> is not entered
(e.g. <code>y ~ x_1 + x_2*x_3 </code>.  Then, if <code>random.only == TRUE</code> the variables
on the right-hand side are interpreted to be random effects; otherwise fixed
for use in <code><a rd-options='' href='dpgrow'>dpgrow</a></code> and  <code><a rd-options='' href='dpgrowmm'>dpgrowmm</a></code>.

Should be left blank for use in <code><a rd-options='' href='dpgrow'>dpgrow</a></code> and <code><a rd-options='' href='dpgrowmm'>dpgrowmm</a></code>,
where is automatically set to <code>na.fail</code>.


An internal function to <code><a rd-options='' href='dpgrow'>dpgrow</a></code> and  <code><a rd-options='' href='dpgrowmm'>dpgrowmm</a></code>


getmf: Produce fixed and random effects design matrices from single formula input

Description

Usage

Arguments

Value

See Also