dist.quant: Computation of Distance Matrices on Quantitative Variables In ade4: Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

 dist.quant R Documentation

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

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

```data(ecomor)

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))
}```

ade4 documentation built on Feb. 16, 2023, 7:58 p.m.