Canonical Analysis based on distances. Confidence Regions for the mean vectors are calculated using bootstrap.
BootDistCanonicalAnalysis(Distance, groups, dimens = NULL, nB = 100, seed = NULL,
PCoA = "Standard", ProcrustesRot = TRUE, DatosIni = TRUE, tol = 1e-04)A list of three elements containing the data, the distances between individuals and type of distance used.
A factor containing the groups to compare.
Number of dimensions to choose . By default is 2.
Number of Bootstrap samples.
Seed for the generation of the random samples (Added for reproducibility)
The type of Principal Coordinates Analysis.
There are two possibilities:
* Standard
* Weighted
By default is Standard.
Should Procrustes rotation be applied to the configurations?
The input object contains the initial data?. By default is TRUE.
Tolerance
The distance matrix used for calculations
Dissimilarity coefficient used to calculate the proximities
Number of bootstrap samples
Factor containing the groups to compare
Names of the groups
Variance accounted for ech canonical coordinate
able with the eigenvalue, variance accounted and cumulated variance
Coordinates of the groups in the representation
Qualities of the representation of the group means
Cumulative Qualities of the representation
Coordinates of the bootstrap replicates
Coordinates of the individuals
Calculates a Canonical Analysis based on distance matrices with confidence regions based on bootstrap resampling.
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<U+2013>519.
# NOT RUN {
data(wine)
X=wine[,4:21]
Dist=DistContinuous(X)
canon=BootDistCanonicalAnalysis(Dist, groups=wine$Group, nB=10)
# }
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