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>

random.only

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

data

Should be left blank for use in
  <code><a href="/link/dpgrow?package=growcurves&version=0.2.3.8" rd-options="" data-mini-rdoc="growcurves::dpgrow">dpgrow</a></code> and <code><a href="/link/dpgrowmm?package=growcurves&version=0.2.3.8" rd-options="" 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 href="/link/dpgrow?package=growcurves&version=0.2.3.8" rd-options="" data-mini-rdoc="growcurves::dpgrow">dpgrow</a></code> and
<code><a href="/link/dpgrowmm?package=growcurves&version=0.2.3.8" rd-options="" 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.

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

Description

Usage

Arguments

Value

See Also