ade4 (version 1.2-2)

dist.quant: Computation of Distance Matrices on Quantitative Variables

Description

computes on quantitative variables, some distance matrices as canonical, Joreskog and Mahalanobis.

Usage

dist.quant(df, method = NULL, diag = FALSE, upper = FALSE, 
    tol = 1e-07)

Arguments

df

a data frame containing only quantitative variables

method

an integer between 1 and 3. If NULL the choice is made with a console message. See details

diag

a logical value indicating whether the diagonal of the distance matrix should be printed by `print.dist'

upper

a logical value indicating whether the upper triangle of the distance matrix should be printed by `print.dist'

tol

used in case 3 of method as a tolerance threshold for null eigenvalues

Value

• an object of class dist

Details

All the distances are of type $d=\|x-y\|_A = \sqrt{(x-y)^{t}A(x-y)}$ 1 = Canonical{A = Identity} 2 = Joreskog{$A=\frac{1}{diag(cov)}$} 3 = Mahalanobis{A = inv(cov)}

Examples

library(mva)
data(ecomor)
par(mfrow = c(2,2))
scatter(dudi.pco(dist.quant(ecomor$morpho,3), scan = FALSE))
scatter(dudi.pco(dist.quant(ecomor$morpho,2), scan = FALSE))
scatter(dudi.pco(dist(scalewt(ecomor$morpho)), scan = FALSE))
scatter(dudi.pco(dist.quant(ecomor$morpho,1), scan = FALSE))
par(mfrow = c(1,1))