This function can estimate random effect for a given set of
model estimates and new user data. The unit may be
new to the model. The methods implements the
EB estimate (also known as BLUP)
as described in Skrondral and Rabe-Hasketh, 2009, p. 683.
This function can also provide the broken stick estimate for a given level,
the sum of the global (fixed) and individual (random) effects.
The current implementation does not provide prediction errors.

Data on multiple individuals through time are often sampled at
times that differ between persons. Irregular observation times can severely
complicate the statistical analysis of the data. The broken stick model
approximates each subject’s trajectory by one or more connected line segments.
The times at which segments connect (breakpoints) are identical for all
subjects and under control of the user. A well-fitting broken stick model
effectively transforms individual measurements made at irregular times into
regular trajectories with common observation times. Specification of the
model requires three variables: time, measurement and subject. The
model is a special case of the linear mixed model, with time as a linear
B-spline and subject as the grouping factor. The main assumptions are:
subjects are exchangeable, trajectories between consecutive breakpoints are
straight, random effects follow a multivariate normal distribution, and
unobserved data are missing at random. The package contains functions for
fitting the broken stick model to data, for predicting curves in new data
and for plotting broken stick estimates. The package supports two
optimization methods, and includes options to structure the
variance-covariance matrix of the random effects. The analyst may use the
software to smooth growth curves by a series of connected straight lines, to
align irregularly observed curves to a common time grid, to create synthetic
curves at a user-specified set of breakpoints, to estimate the time-to-time
correlation matrix and to predict future observations. See
<doi:10.18637/jss.v106.i07> for additional documentation on background,
methodology and applications.

Stef van Buuren

brokenstick

Broken Stick Model for Irregular Longitudinal Data

EB function

<dl><dt>model</dt>
<dd>An object of class <code>brokenstick</code>.</dd>
<dt>y</dt>
<dd>A vector of new measurements for unit j, scaled in the same metric as the fitted model.</dd>
<dt>X</dt>
<dd>A <code>nj * p</code> matrix with fixed effects for unit j, typically produced by <code>bs()</code>.</dd>
<dt>Z</dt>
<dd>A <code>nj * q</code> matrix with random effects for unit j. The default sets <code>Z</code> equal to <code>X</code>.</dd>
<dt>BS</dt>
<dd>A logical indicating whether broken stick estimates should be
returned (<code>BS = TRUE</code>) or the random effects (<code>BS = FALSE</code>).
The default is <code>TRUE</code>.</dd></dl>

Arguments

Author

Empirical Bayes predictor for random effects — EB

<dl>

<dt>model</dt>
<dd>An object of class <code>brokenstick</code>.</dd>


<dt>y</dt>
<dd>A vector of new measurements for unit j, scaled in the same metric as the fitted model.</dd>


<dt>X</dt>
<dd>A <code>nj * p</code> matrix with fixed effects for unit j, typically produced by <code>bs()</code>.</dd>


<dt>Z</dt>
<dd>A <code>nj * q</code> matrix with random effects for unit j. The default sets <code>Z</code> equal to <code>X</code>.</dd>


<dt>BS</dt>
<dd>A logical indicating whether broken stick estimates should be
returned (<code>BS = TRUE</code>) or the random effects (<code>BS = FALSE</code>).
The default is <code>TRUE</code>.</dd>

</dl>

EB: Empirical Bayes predictor for random effects

Description

Usage

Value

Arguments

Author

References