dudi.pca {ade4} | R Documentation

## Principal Component Analysis

### Description

`dudi.pca` performs a principal component analysis of a data frame and returns the results as objects of class `pca` and `dudi`.

### Usage

    dudi.pca(df, row.w = rep(1, nrow(df))/nrow(df),
        col.w = rep(1, ncol(df)), center = TRUE, scale = TRUE,
        scannf = TRUE, nf = 2)

### Arguments

`df` | a data frame with n rows (individuals) and p columns (numeric variables)
`row.w` | an optional row weights (by default, uniform row weights)
`col.w` | an optional column weights (by default, unit column weights)
`center` | a logical or numeric value, centring option if TRUE, centring by the mean if FALSE no centring if a numeric vector, its length must be equal to the number of columns of the data frame df and gives the decentring
`scale` | a logical value indicating whether the column vectors should be normed for the row.w weighting
`scannf` | a logical value indicating whether the screeplot should be displayed
`nf` | if scannf FALSE, an integer indicating the number of kept axes

### Value

Returns a list of classes `pca` and `dudi` (see dudi) containing the used information for computing the principal component analysis :

`tab` | the data frame to be analyzed depending of the transformation arguments (center and scale)
`cw` | the column weights
`lw` | the row weights
`eig` | the eigenvalues
`rank` | the rank of the analyzed matrice
`nf` | the number of kept factors
`c1` | the column normed scores i.e. the principal axes
`l1` | the row normed scores
`co` | the column coordinates
`li` | the row coordinates i.e. the principal components
`call` | the call function
`cent` | the p vector containing the means for variables (Note that if `center = F`, the vector contains p 0)
`norm` | the p vector containing the standard deviations for variables i.e. the root of the sum of squares deviations of the values from their means divided by n (Note that if `norm = F`, the vector contains p 1)

### Author(s)

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr

### See Also

`prcomp`, `princomp` in the `mva` library

### Examples

    data(deug)
    deug.dudi <- dudi.pca(deug$tab, center = deug$cent, scale = FALSE, scan = FALSE)
    deug.dudi1 <- dudi.pca(deug$tab, center = TRUE, scale = TRUE, scan = FALSE)

    if(adegraphicsLoaded()) {
      g1 <- s.class(deug.dudi$li, deug$result, plot = FALSE)
      g2 <- s.arrow(deug.dudi$c1, lab = names(deug$tab), plot = FALSE)
      g3 <- s.class(deug.dudi1$li, deug$result, plot = FALSE)
      g4 <- s.corcircle(deug.dudi1$co, lab = names(deug$tab), full = FALSE, plot = FALSE)
      G1 <- rbindADEg(cbindADEg(g1, g2, plot = FALSE), cbindADEg(g3, g4, plot = FALSE), plot = TRUE)

      G2 <- s1d.hist(deug.dudi$tab, breaks = seq(-45, 35, by = 5), type = "density", xlim = c(-40, 40),
        right = FALSE, ylim = c(0, 0.1), porigin.lwd = 2)

    } else {
      par(mfrow = c(2, 2))
      s.class(deug.dudi$li, deug$result, cpoint = 1)
      s.arrow(deug.dudi$c1, lab = names(deug$tab))
      s.class(deug.dudi1$li, deug$result, cpoint = 1)
      s.corcircle(deug.dudi1$co, lab = names(deug$tab), full = FALSE, box = TRUE)
      par(mfrow = c(1, 1))

      # for interpretations
      par(mfrow = c(3, 3))
      par(mar = c(2.1, 2.1, 2.1, 1.1))
      for(i in 1:9) {
        hist(deug.dudi$tab[,i], xlim = c(-40, 40), breaks = seq(-45, 35, by = 5),
          prob = TRUE, right = FALSE, main = names(deug$tab)[i], xlab = "", ylim = c(0, 0.10))
      abline(v = 0, lwd = 3)
      }
      par(mfrow = c(1, 1))
    }

[Package ade4 version 1.7-13 Index]