Performs a MANOVA and a Canonical Analysis based on of Distance Matrices (usally for continuous data)
CanonicalDistanceAnalysis(Prox, group, dimens = 2, Nsamples = 1000,
PCoA = "Standard", ProjectInd = TRUE)An object of class "CanonicalDistanceAnalysis" with:
The Matrix of Distances from which the Analysis has been made
A factor containing the group struture of the individuals
Total sum of squares
Between groups sum of squares
Within groups sum of squares
Experimental pseudo F-value
p value based on the permutation test
p value based on the permutation test
Variances explained by the PCoA
Coordinates of the groups for the graphical representation
Qualities of the representation of the groups
Cummulative qualities of the representation of the groups
Coordinates of the individuals for the graphical representation
A object containing proximities
A factor with the group structure of the rows
The dimension of the solution
Number of samples for the permutation test. Number of permutations.
Type of Principal Coordinates for the Canonical Analysis calculated from the Principal coordinates of the Mean Matrix. "Standard" : Standard Principal Coordinates Analysis, "Weighted": Weighted Principal Coordinates Analysis, "WPCA")
Should the individual points be Projected onto the representation For the moment only available for Continuous Data.
Jose Luis Vicente Villardon
Performs a MANOVA and a Canonical Analysis based on of Distance Matrices (usally for continuous data). The MANOVA statistics is calculated from a decomposition of the distance matrix based on a factor of a external classification. The significance of the test is calculated using a premutation test. The approach depens only on the distances and then is useful with any kind of data.
The Canonical Representation is calculated from a Principal Coordinates Analysis od the distance matrix among the means. Thus, it is only possible for continuous data. The PCoA representation can be "Standard" using the means directly, "Weighted" weighting each group with its sample size or using weighted Princiopal Components Analysis of the matrix of means.
A measure of the quality of representation of the groups is provided. When possible, the measure is also provided for the individual points.
Soon, a biplot representation will also be developed.
Gower, J. C., & 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.
Krzanowski, W. J. (2004). Biplots for multifactorial analysis of distance. Biometrics, 60(2), 517-524.
data(iris)
group=iris[,5]
X=as.matrix(iris[1:4])
D=ContinuousProximities(X, coef = 1)
CDA=CanonicalDistanceAnalysis(D, group, dimens=2)
summary(CDA)
plot(CDA)
Run the code above in your browser using DataLab