ade4: dudi.pca – R documentation

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dudi.pca

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

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))
}

ade4

Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

v1.7-16

GPL (>= 2)

Authors

Stéphane Dray <stephane.dray@univ-lyon1.fr>, Anne-Béatrice Dufour <anne-beatrice.dufour@univ-lyon1.fr>, and Jean Thioulouse <jean.thioulouse@univ-lyon1.fr>, with contributions from Thibaut Jombart, Sandrine Pavoine, Jean R. Lobry, Sébastien Ollier, Daniel Borcard, Pierre Legendre, Stéphanie Bougeard and Aurélie Siberchicot. Based on earlier work by Daniel Chessel.

Initial release