residuals.brokenstick

Extract residuals from brokenstick model

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

residuals.brokenstick function

<dl><dt>object</dt>
<dd>A <code>brokenstick</code> object.</dd>
<dt>newdata</dt>
<dd>Optional. A data frame in which to look for variables with
which to predict. The training data are used if omitted and
if <code>object$light</code> is <code>FALSE</code>.</dd>
<dt>...</dt>
<dd>Additional arguments. Ignored.</dd></dl>

Arguments

Extract residuals from brokenstick model — residuals.brokenstick

<dl>

<dt>object</dt>
<dd>A <code>brokenstick</code> object.</dd>


<dt>newdata</dt>
<dd>Optional. A data frame in which to look for variables with
which to predict. The training data are used if omitted and
if <code>object$light</code> is <code>FALSE</code>.</dd>


<dt>...</dt>
<dd>Additional arguments. Ignored.</dd>

</dl>

residuals.brokenstick: Extract residuals from brokenstick model

Description

Usage

Value

Arguments

See Also