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

In ade4: Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

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)
## S3 method for class 'nipals'
scatter(x, xax = 1, yax = 2, clab.row = 0.75, clab.col
= 1, posieig = "top", sub = NULL, ...)
## S3 method for class 'nipals'
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

Details

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

Value

Returns a list of classes nipals:

tab	the scaled data frame
eig	the pseudoeigenvalues
rank	the rank of the analyzed matrice
nf	the number of factors
c1	the column normed scores
co	the column coordinates
li	the row coordinates
call	the call function
nb	the number of iterations for each axis
rec	a data frame obtained by the reconstitution of the scaled data using the nf axes

Author(s)

Stéphane Dray [e-mail]

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", quietly = TRUE)) {
    g1 <- s1d.barchart(acp1$eig, psub.text = "dudi.pca", p1d.horizontal = FALSE, plot = FALSE)
    g2 <- s1d.barchart(nip1$eig, psub.text = "nipals", p1d.horizontal = FALSE, plot = FALSE)
    g3 <- lattice::xyplot(nip1$c1[, 1] ~ acp1$c1[, 1], main = "col scores", xlab = "dudi.pca", 
      ylab = "nipals")
    g4 <- lattice::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)

ade4 documentation built on Feb. 16, 2023, 7:58 p.m.