kdist {ade4} | R Documentation

## the class of objects 'kdist' (K distance matrices)

### Description

An object of class `kdist` is a list of distance matrices observed on the same individuals

### Usage

    kdist(..., epsi = 1e-07, upper = FALSE)

### Arguments

`...` | a sequence of objects of the class `kdist`.
`epsi` | a tolerance threshold to test if distances are Euclidean (Gower's theorem) using \frac{λ_n}{λ_1} is larger than -epsi.
`upper` | a logical value indicating whether the upper of a distance matrix is used (TRUE) or not (FALSE).

### Details

The attributs of a 'kdist' object are:
`names`: the names of the distances
`size`: the number of points between distances are known
`labels`: the labels of points
`euclid`: a logical vector indicating whether each distance of the list is Euclidean or not.
`call`: a call order
`class`: object 'kdist'

### Value

returns an object of class 'kdist' containing a list of semidefinite matrices.

### Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

### References

Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325–338.

### Examples

    # starting from a list of matrices
    data(yanomama)
    lapply(yanomama,class)
    kd1 = kdist(yanomama)
    print(kd1)

    # giving the correlations of Mantel's test
    cor(as.data.frame(kd1))
    pairs(as.data.frame(kd1))

    # starting from a list of objects 'dist'
    data(friday87)
    fri.w <- ktab.data.frame(friday87$fau, friday87$fau.blo,
        tabnames = friday87$tab.names)
    fri.kd = lapply(1:10, function(x) dist.binary(fri.w[[x]],2))
    names(fri.kd) = friday87$tab.names
    unlist(lapply(fri.kd,class)) # a list of distances
    fri.kd = kdist(fri.kd)
    fri.kd
    s.corcircle(dudi.pca(as.data.frame(fri.kd), scan = FALSE)$co)

    # starting from several distances
    data(ecomor)
    d1 <- dist.binary(ecomor$habitat, 1)
    d2 <- dist.prop(ecomor$forsub, 5)
    d3 <- dist.prop(ecomor$diet, 5)
    d4 <- dist.quant(ecomor$morpho, 3)
    d5 <- dist.taxo(ecomor$taxo)
    ecomor.kd <- kdist(d1, d2, d3, d4, d5)
    names(ecomor.kd) = c("habitat", "forsub", "diet", "morpho", "taxo")
    class(ecomor.kd)
    s.corcircle(dudi.pca(as.data.frame(ecomor.kd), scan = FALSE)$co)

    data(bsetal97)
    X <- prep.fuzzy.var(bsetal97$biol, bsetal97$biol.blo)
    w1 <- attr(X, "col.num")
    w2 <- levels(w1)
    w3 <- lapply(w2, function(x) dist.quant(X[,w1==x], method = 1))
    names(w3) <- names(attr(X, "col.blocks"))
    w3 <- kdist(list = w3)
    s.corcircle(dudi.pca(as.data.frame(w3), scan = FALSE)$co)

    data(rpjdl)
    w1 = lapply(1:10, function(x) dist.binary(rpjdl$fau, method = x))
    w2 = c("JACCARD", "SOCKAL_MICHENER", "SOCKAL_SNEATH_S4", "ROGERS_TANIMOTO")
    w2 = c(w2, "CZEKANOWSKI", "S9_GOWER_LEGENDRE", "OCHIAI", "SOKAL_SNEATH_S13")
    w2 <- c(w2, "Phi_PEARSON", "S2_GOWER_LEGENDRE")
    names(w1) <- w2
    w3 = kdist(list = w1)
    w4 <- dudi.pca(as.data.frame(w3), scan = FALSE)$co
    w4

[Package ade4 version 1.7-13 Index]