Variance-covariance structure where the residuals are independent but may have different variance.
Can be stratified on a categorical variable.

multivariate

Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers"
taught by the section of Biostatistics of the University of Copenhagen.
It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...).
Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom.
Normalized residuals are provided to assess model misspecification.
Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients.
Predictions can be computed conditional to covariates only or also to outcome values.

Brice Ozenne

LMMstar

Repeated Measurement Models for Discrete Times

Julie Forman

IND function

<dl><dt>formula</dt>
<dd>formula indicating variables influencing the residual variance,
using either as a multiplicative factor (right hand side) or stratification (left hand side) to model their effect.</dd>
<dt>var.cluster</dt>
<dd>[character] cluster variable.</dd>
<dt>var.time</dt>
<dd>[character] time variable.</dd>
<dt>add.time</dt>
<dd>Should the default formula (i.e. when <code>NULL</code>) contain a time effect.</dd></dl>

Arguments

Independence Structure — IND

<dl>

<dt>formula</dt>
<dd>formula indicating variables influencing the residual variance,
using either as a multiplicative factor (right hand side) or stratification (left hand side) to model their effect.</dd>


<dt>var.cluster</dt>
<dd>[character] cluster variable.</dd>


<dt>var.time</dt>
<dd>[character] time variable.</dd>


<dt>add.time</dt>
<dd>Should the default formula (i.e. when <code>NULL</code>) contain a time effect.</dd>

</dl>

IND: Independence Structure

Description

Usage

Value

Arguments

Details

Examples