BootDistCanonicalAnalysis: Canonical Analysis based on Distances

In PERMANOVA: Multivariate Analysis of Variance Based on Distances and Permutations

Description

Canonical Analysis based on distances. Confidence Regions for the mean vectors are calculated using bootstrap.

Usage

BootDistCanonicalAnalysis(Distance, groups, dimens = NULL, nB = 100, seed = NULL,
PCoA = "Standard", ProcrustesRot = TRUE, DatosIni = TRUE, tol = 1e-04)

Arguments

Distance	A list of three elements containing the data, the distances between individuals and type of distance used.
groups	A factor containing the groups to compare.
dimens	Number of dimensions to choose . By default is 2.
nB	Number of Bootstrap samples.
seed	Seed for the generation of the random samples (Added for reproducibility)
PCoA	The type of Principal Coordinates Analysis. There are two possibilities: * Standard * Weighted By default is Standard.
ProcrustesRot	Should Procrustes rotation be applied to the configurations?
DatosIni	The input object contains the initial data?. By default is TRUE.
tol	Tolerance

Details

Calculates a Canonical Analysis based on distance matrices with confidence regions based on bootstrap resampling.

Value

D	The distance matrix used for calculations
Coefficient	Dissimilarity coefficient used to calculate the proximities
nB	Number of bootstrap samples
Groups	Factor containing the groups to compare
GroupNames	Names of the groups
Inertia	Variance accounted for ech canonical coordinate
Inertias	able with the eigenvalue, variance accounted and cumulated variance
MeanCoordinates	Coordinates of the groups in the representation
Qualities	Qualities of the representation of the group means
CumulativeQualities	Cumulative Qualities of the representation
CoordBoot	Coordinates of the bootstrap replicates
RowCoordinates	Coordinates of the individuals

Author(s)

Laura Vicente-Gonzalez, Jose Luis Vicente-Villardon

References

Gower, J. C. and Krzanowski, W. J. (1999). Analysis of distance for structured multivariate data and extensions to multivariate analysis of variance. Journal of the Royal Statistical Society: Series C (Applied Statistics), 48(4):505–519.

Examples

data(wine)
X=wine[,4:21]
Dist=DistContinuous(X)
canon=BootDistCanonicalAnalysis(Dist, groups=wine$Group, nB=10)

PERMANOVA documentation built on Sept. 6, 2021, 5:07 p.m.