## 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 B 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