pphpc_testvlo

<p>A dataset with six outputs of different lengths for testing purposes only.</p>

internal

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

pphpc_testvlo function

<p>A <code>grpoutputs</code> object containing simulation output data
from 6 runs of the PPHPC model, 3 runs from different implementations. The
model has six outputs, but the object contains a seventh output corresponding
to the concatenation of the six outputs</p>

pphpc_testvlo: Data for testing variable length outputs

Description

Usage

Arguments

Format