pphpc_ok

<p>A dataset containing simulation output data from two implementations of the
PPHPC model.</p>

datasets

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_ok function

<p>A <code>grpoutputs</code> object containing simulation output data
from 20 runs of the PPHPC model, 10 runs from each implementation. The model
has six outputs, but the object contains a seventh output corresponding to
the concatenation of the six outputs</p>

pphpc_ok: Data from two similar implementations of the PPHPC model

Description

Usage

Arguments

Format