supdist {ade4}

Projection of additional items in a PCO analysis

Description

This function takes the grand distance matrix between all items (Active + Supplementary). It computes the PCO of the distance matrix between Active items, and projects the distance matrix of Supplementary items in this PCO.

Usage

supdist(d, fsup, tol = 1e-07)

Arguments

d

Grand distance matrix between all (Active + Supplementary) items

fsup

A factor with two levels giving the Active (level ‘A’) or Supplementary (level ‘S’) status for each item in the distance matrix.

tol

Numeric tolerance used to evaluate zero eigenvalues

Value

coordSup

Coordinates of Supplementary items projected in the PCO of Active items

coordAct

Coordinates of Active item

coordTot

Coordinates of Active plus Supplementary items

Author(s)

Jean Thioulouse

References

Computations based on the Methods section of the following paper: Pele J, Abdi H, Moreau M, Thybert D, Chabbert M (2011) Multidimensional Scaling Reveals the Main Evolutionary Pathways of Class A G-Protein-Coupled Receptors. PLoS ONE 6(4): e19094. doi:10.1371/journal.pone.0019094

See Also

dudi.pco, suprow

Examples

data(meau)
## Case 1: Supplementary items = subset of Active items
## Supplementary coordinates should be equal to Active coordinates
## PCO of active items (meau dataset has 6 sites and 10 variables)
envpca1 <- dudi.pca(meau$env, scannf = FALSE)
dAct <- dist(envpca1$tab)
pco1 <- dudi.pco(dAct, scannf = FALSE)
## Projection of rows 19:24 (winter season for the 6 sites)
## Supplementary items must be normalized
f1 <- function(w) (w - envpca1$cent) / envpca1$norm
envSup <- t(apply(meau$env[19:24, ], 1, f1))
envTot <- rbind.data.frame(envpca1$tab, envSup)
dTot <- dist(envTot)
fSA1 <- as.factor(rep(c("A", "S"), c(24, 6)))
cSup1 <- supdist(dTot, fSA1)
## Comparison (coordinates should be equal)
cSup1$coordSup[, 1:2]
pco1$li[19:24, ]

data(meaudret)
## Case 2: Supplementary items = new items
## PCO of active items (meaudret dataset has only 5 sites and 9 variables)
envpca2 <- dudi.pca(meaudret$env, scannf = FALSE)
dAct <- dist(envpca2$tab)
pco2 <- dudi.pco(dAct, scannf = FALSE)
## Projection of site 6 (four seasons, without Oxyg variable)
## Supplementary items must be normalized
f1 <- function(w) (w - envpca2$cent) / envpca2$norm
envSup <- t(apply(meau$env[seq(6, 24, 6), -5], 1, f1))
envTot <- rbind.data.frame(envpca2$tab, envSup)
dTot <- dist(envTot)
fSA2 <- as.factor(rep(c("A", "S"), c(20, 4)))
cSup2 <- supdist(dTot, fSA2)
## Supplementary items vs. real items (both in red)
if(!adegraphicsLoaded()) {
 par(mfrow = c(2, 2))
 s.label(pco1$li, boxes = FALSE)
 s.label(rbind.data.frame(pco2$li, cSup2$coordSup[, 1:2]), boxes = FALSE)
} else {
 gl1 <- s.label(pco1$li, plabels.optim = TRUE, plabels.col=rep(c(rep("black", 5),"red"), 4))
 gl2 <- s.label(rbind.data.frame(pco2$li, cSup2$coordSup[, 1:2]),
  plabels.optim = TRUE, plabels.col=rep(c("black","red"),c(20, 4)))
 ADEgS(list(gl1, gl2))
}