mixOmics (version 4.1-4)

pls: Partial Least Squares (PLS) Regression

Description

Function to perform Partial Least Squares (PLS) regression.

Usage

pls(X, Y, ncomp = 2, 
    mode = c("regression", "canonical", "invariant", "classic"), 
    max.iter = 500, tol = 1e-06, ...)

Arguments

X

numeric matrix of predictors. NAs are allowed.

Y

numeric vector or matrix of responses (for multi-response models). NAs are allowed.

ncomp

the number of components to include in the model. Default to 2.

mode

character string. What type of algorithm to use, (partially) matching one of "regression", "canonical", "invariant" or "classic". See Details.

max.iter

integer, the maximum number of iterations.

tol

a not negative real, the tolerance used in the iterative algorithm.

...

arguments to pass to nearZeroVar.

Value

• pls returns an object of class "pls", a list that contains the following components:
• X
  the centered and standardized original predictor matrix.
• Y
  the centered and standardized original response vector or matrix.
• ncomp
  the number of components included in the model.
• mode
  the algoritthm used to fit the model.
• mat.c
  matrix of coefficients to be used internally by predict.
• variates
  list containing the $X$ and $Y$ variates.
• loadings
  list containing the estimated loadings for the variates.
• names
  list containing the names to be used for individuals and variables.
• nzv
  list containing the zero- or near-zero predictors information.

encoding

latin1

Details

pls function fit PLS models with $1, \ldots ,$ncomp components. Multi-response models are fully supported. The X and Y datasets can contain missing values. The type of algorithm to use is specified with the mode argument. Four PLS algorithms are available: PLS regression ("regression"), PLS canonical analysis ("canonical"), redundancy analysis ("invariant") and the classical PLS algorithm ("classic") (see References). The estimation of the missing values can be performed by the reconstitution of the data matrix using the nipals function. Otherwise, missing values are handled by casewise deletion in the pls function without having to delete the rows with missing data.

References

Tenenhaus, M. (1998). La r�gression PLS: th�orie et pratique. Paris: Editions Technic. Wold H. (1966). Estimation of principal components and related models by iterative least squares. In: Krishnaiah, P. R. (editors), Multivariate Analysis. Academic Press, N.Y., 391-420.

See Also

spls, summary, plotIndiv, plotVar, predict, valid.

Examples

data(linnerud)
X <- linnerud$exercise
Y <- linnerud$physiological
linn.pls <- pls(X, Y, mode = "classic")

data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic

toxicity.pls <- pls(X, Y, ncomp = 3)