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.
|Author||Nuno Fachada [aut, cre]|
|Date of publication||2016-08-04 21:56:52|
|Maintainer||Nuno Fachada <email@example.com>|
|License||MIT + file LICENSE|
assumptions: Parametric tests assumptions
assumptions.cmpoutput: Get assumptions for parametric tests performed on output...
assumptions_manova: Determine the assumptions for the MANOVA test
assumptions.micomp: Get assumptions for parametric tests performed on each...
assumptions_paruv: Determine the assumptions for the parametric comparison test
centerscale: Center and scale vector
cmpoutput: Compares output observations from two or more groups
concat_outputs: Concatenate multiple outputs with multiple observations
grpoutputs: Load and group outputs from files
micomp: Multiple independent comparisons of observations
micompr: micompr: multivariate independent comparison of observations
plot.assumptions_cmpoutput: Plot _p_-values for testing the assumptions of the parametric...
plot.assumptions_manova: Plot _p_-values for testing the multivariate normality...
plot.assumptions_micomp: Plot _p_-values for testing the assumptions of the parametric...
plot.assumptions_paruv: Plot _p_-values for testing the assumptions of the parametric...
plot.cmpoutput: Plot comparison of an output
plotcols: Default colors for plots in 'micomp' package
plot.grpoutputs: Plot grouped outputs
plot.micomp: Plot projection of output observations on the first two...
pphpc_diff: Data from two implementations of the PPHPC model, one of...
pphpc_noshuff: Data from two implementations of the PPHPC model, one of...
pphpc_ok: Data from two similar implementations of the PPHPC model
pphpc_testvlo: Data for testing variable length outputs
print.assumptions_cmpoutput: Print method for the assumptions of parametric tests used in...
print.assumptions_manova: Print information about the assumptions of the MANOVA test
print.assumptions_micomp: Print information about the assumptions concerning the...
print.assumptions_paruv: Print information about the assumptions of the parametric...
print.cmpoutput: Print information about comparison of an output
print.grpoutputs: Print information about grouped outputs
print.micomp: Print information about multiple comparisons of outputs
pst: Concatenate strings without any separator characters
pvalcol: Associate colors to _p_-values
pvalf: Format p-values
pvalf.default: Default p-value formatting method
summary.assumptions_cmpoutput: Summary method for the assumptions of parametric tests used...
summary.assumptions_micomp: Summary method for the assumptions of parametric tests used...
summary.cmpoutput: Summary method for comparison of an output
summary.grpoutputs: Summary method for grouped outputs
summary.micomp: Summary method for multiple comparisons of outputs
tikzscat: Simple 'TikZ' scatter plot
toLatex.cmpoutput: Convert 'cmpoutput' object to 'LaTeX' table
toLatex.micomp: Convert 'micomp' object to 'LaTeX' table
tscat_apply: Multiple 'TikZ' 2D scatter plots for a list of output...