cda.calc {MorphoTools2} | R Documentation |
Canonical Discriminant Analysis
Description
This function performs canonical discriminant analysis.
Usage
cda.calc(object, passiveSamples = NULL)
Arguments
object |
an object of class |
passiveSamples |
taxa or populations, which will be only predicted, see Details. |
Details
The cda.calc
function performs canonical discriminant analysis using the candisc
method from the candisc
package. Canonical discriminant analysis finds linear combination of the quantitative variables that maximize the difference in the mean discriminant score between groups. This function allows exclude subset of samples (passiveSamples
) from computing the discriminant function, and only passively predict them in multidimensional space. This approach is advantageous for testing the positions of “atypical” populations (e.g., putative hybrids) or for assessing positions of selected individuals (e.g., type herbarium specimens).
Value
an object of class cdadata
with the following elements:
objects |
ID | IDs of each row of scores object. |
|
Population | population membership of each row of scores object. |
|
Taxon | taxon membership of each row of scores object. |
|
scores | ordination scores of cases (objects, OTUs). | |
eigenValues |
eigenvalues, i.e., proportion of variation of the original dataset expressed by individual axes. |
eigenvaluesAsPercent |
eigenvalues as percent, percentage of their total sum. |
cumulativePercentageOfEigenvalues |
cumulative percentage of eigenvalues. |
groupMeans |
|
rank |
number of non-zero eigenvalues. |
coeffs.raw |
matrix containing the raw canonical coefficients. |
coeffs.std |
matrix containing the standardized canonical coefficients. |
totalCanonicalStructure |
matrix containing the total canonical structure coefficients, i.e., total-sample correlations between the original variables and the canonical variables. |
canrsq |
squared canonical correlations. |
Examples
data(centaurea)
centaurea = naMeanSubst(centaurea)
centaurea = removePopulation(centaurea, populationName = c("LIP", "PREL"))
cdaRes = cda.calc(centaurea)
summary(cdaRes)
plotPoints(cdaRes, col = c("red", "green", "blue", "red"),
pch = c(20, 17, 8, 21), pt.bg = "orange", legend = TRUE)