kdist {ade4} | R Documentation |
An object of class kdist
is a list of distance matrices observed on the same individuals
kdist(..., epsi = 1e-07, upper = FALSE)
... |
a sequence of objects of the class |
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). |
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'
returns an object of class 'kdist' containing a list of semidefinite matrices.
Daniel Chessel
Anne B Dufour anne-beatrice.dufour@univ-lyon1.fr
Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325–338.
# 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