nipals {plspm}R Documentation

NIPALS: Non-linear Iterative Partial Least Squares

Description

Principal Component Analysis with NIPALS algorithm

Usage

  nipals(x, nc = 2, scaled = TRUE)

Arguments

x A numeric matrix or data frame.
nc Number of components kept in the results (by default 2)
scaled A logical value indicating whether scaling data is performed (TRUE by default).

Details

The function nipals performs Principal Component Analysis of a data matrix that may contain missing data.

Value

An object of class "nipals", basically a list with the following elements:

values The pseudo eigenvalues.
scores The extracted scores.
loadings The loadings.
cor.sco Correlations between the variables and the scores.
disto Squared distance of the observations to the origin.
contrib Contributions of the observations (rows).
cos Squared cosinus.
dmod Distance to the Model.

When the analyzed data contain missing values, the help interpretation tools (e.g. cor.sco, disto, contrib, cos, dmod) may not be meaningful, that is to say, some of the results may not be coherent.

Author(s)

Gaston Sanchez

References

Tenenhaus, M. (1998) La Regression PLS. Theorie et Pratique. Editions TECHNIP, Paris.

Tenenhaus, M. (2007) Statistique. Methodes pour decrire, expliquer et prevoir. Dunod, Paris.

See Also

plot.nipals

Examples

  ## Not run: 
  ## example of NIPALS algorithm
  data(wines)
  nip1 <- nipals(wines[,-1], nc=5)
  plot(nip1)

  ## USArrests data vary 
  nip2 <- nipals(USArrests)
  plot(nip2)
  ## End(Not run)

[Package plspm version 0.1-4 Index]