ade4 (version 1.7-5)

nipals: Non-linear Iterative Partial Least Squares (NIPALS) algorithm

Description

This function performs NIPALS algorithm, i.e. a principal component analysis of a data table that can contain missing values.

Usage

nipals(df, nf = 2, rec = FALSE, niter = 100, tol = 1e-09)
"scatter"(x, xax = 1, yax = 2, clab.row = 0.75, clab.col
= 1, posieig = "top", sub = NULL, ...)
"print"(x, ...)

Arguments

df

a data frame that can contain missing values

nf

an integer, the number of axes to keep

rec

a logical that specify if the functions must perform the reconstitution of the data using the nf axes

niter

an integer, the maximum number of iterations

tol

a real, the tolerance used in the iterative algorithm

x

an object of class nipals

xax

the column number for the x-axis

yax

the column number for the y-axis

clab.row

a character size for the rows

clab.col

a character size for the columns

posieig

if "top" the eigenvalues bar plot is upside, if "bottom" it is downside, if "none" no plot

sub

a string of characters to be inserted as legend

...

further arguments passed to or from other methods

Value

Returns a list of classes nipals:

Details

Data are scaled (mean 0 and variance 1) prior to the analysis.

References

Wold, H. (1966) Estimation of principal components and related models by iterative least squares. In P. Krishnaiah, editors.Multivariate Analysis, Academic Press, 391--420.

Wold, S., Esbensen, K. and Geladi, P. (1987) Principal component analysis Chemometrics and Intelligent Laboratory Systems, 2, 37--52.

See Also

dudi.pca

Examples

data(doubs)
## nipals is equivalent to dudi.pca when there are no NA
acp1 <- dudi.pca(doubs$env, scannf = FALSE, nf = 2)
nip1 <- nipals(doubs$env)


if(adegraphicsLoaded()) {
  if(requireNamespace("lattice", quiet = TRUE)) {
    g1 <- s1d.barchart(acp1$eig, psub.text = "dudi.pca", p1d.hori = F, plot = F)
    g2 <- s1d.barchart(nip1$eig, psub.text = "nipals", p1d.hori = F, plot = F)
    g3 <- xyplot(nip1$c1[, 1] ~ acp1$c1[, 1], main = "col scores", xlab = "dudi.pca", 
      ylab = "nipals")
    g4 <- xyplot(nip1$li[, 1] ~ acp1$li[, 1], main = "row scores", xlab = "dudi.pca", 
      ylab = "nipals")
    G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2))
  }
  
} else {
  par(mfrow = c(2, 2))
  barplot(acp1$eig, main = "dudi.pca")
  barplot(nip1$eig, main = "nipals")
  plot(acp1$c1[, 1], nip1$c1[, 1], main = "col scores", xlab = "dudi.pca", ylab = "nipals")
  plot(acp1$li[, 1], nip1$li[, 1], main = "row scores", xlab = "dudi.pca", ylab = "nipals")
}

## Not run: 
# ## with NAs:
# doubs$env[1, 1] <- NA
# nip2 <- nipals(doubs$env)
# cor(nip1$li, nip2$li)
# nip1$eig
# nip2$eig
# ## End(Not run)