# dist.quant: Computation of Distance Matrices on Quantitative Variables

Description Usage Arguments Details Value Author(s) Examples

### Description

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

### Usage

 ```1 2``` ```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

### 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 = 1 / diag(cov)

3 = Mahalanobis

A = inv(cov)

### Value

an object of class `dist`

### Author(s)

Daniel Chessel
Stéphane Dray stephane.dray@univ-lyon1.fr

### Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```data(ecomor) if(adegraphicsLoaded()) { g1 <- scatter(dudi.pco(dist.quant(ecomor\$morpho, 3), scan = FALSE), plot = FALSE) g2 <- scatter(dudi.pco(dist.quant(ecomor\$morpho, 2), scan = FALSE), plot = FALSE) g3 <- scatter(dudi.pco(dist(scalewt(ecomor\$morpho)), scan = FALSE), plot = FALSE) g4 <- scatter(dudi.pco(dist.quant(ecomor\$morpho, 1), scan = FALSE), plot = FALSE) G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2)) } else { 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)) } ```