ade4 (version 1.5-2)

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)
## 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

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

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)
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")

## with NAs:
doubs$env[1,1] <- NA
nip2 <- nipals(doubs$env)
cor(nip1$li, nip2$li)
nip1$eig
nip2$eig