dudi.pca: Principal Component Analysis In ade4: Analysis of Ecological Data : Exploratory and Euclidean Methods in Environmental Sciences

Description

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

Usage

 ```1 2 3``` ```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 B Dufour [email protected]

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

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32``` ```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)) } ```

Example output

```
```

ade4 documentation built on May 31, 2017, 4:06 a.m.