supdist {ade4} | R Documentation

## 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. https://doi.org/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
    if(!adegraphicsLoaded()) {
     par(mfrow = c(1, 2))
     s.label(pco1$li)
     s.label(rbind.data.frame(pco2$li, cSup2$coordSup[, 1:2]))
    } else {
     gl1 <- s.label(pco1$li, plabels.optim = TRUE)
     gl2 <- s.label(rbind.data.frame(pco2$li, cSup2$coordSup[, 1:2]),
      plabels.optim = TRUE)
     ADEgS(list(gl1, gl2))
    }

[Package ade4 version 1.7-13 Index]