assumptions_manova

Determine two assumptions for the MANOVA test: a) multivariate normality
of each group; b) homogeneity of covariance matrices.

A procedure for comparing multivariate samples associated with
different groups. It uses principal component analysis to convert
multivariate observations into a set of linearly uncorrelated statistical
measures, which are then compared using a number of statistical methods. The
procedure is independent of the distributional properties of samples and
automatically selects features that best explain their differences, avoiding
manual selection of specific points or summary statistics. It is appropriate
for comparing samples of time series, images, spectrometric measures or
similar multivariate observations. This package is described in Fachada et
al. (2016) <doi:10.32614/RJ-2016-055>.

Nuno Fachada

micompr

Multivariate Independent Comparison of Observations

assumptions_manova function

<dl><dt>data</dt>
<dd>Data used for the MANOVA test (rows correspond to observations,
columns to dependent variables).</dd>
<dt>factors</dt>
<dd>Groups to which rows of <code>data</code> belong to (independent
variables).</dd></dl>

Arguments

Determine the assumptions for the MANOVA test — assumptions_manova

<dl>

<dt>data</dt>
<dd>Data used for the MANOVA test (rows correspond to observations,
columns to dependent variables).</dd>


<dt>factors</dt>
<dd>Groups to which rows of <code>data</code> belong to (independent
variables).</dd>

</dl>

assumptions_manova: Determine the assumptions for the MANOVA test

Description

Usage

Value

Arguments

Examples