| pls {integrOmics} | R Documentation |
Functions to perform Partial Least Squares (PLS) regression.
pls(X, Y, ncomp = 3,
mode = c("regression", "canonical", "invariant", "classic"),
max.iter = 500, tol = 1e-06, scaleY=TRUE)
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 is from one to the rank of X. |
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. |
scaleY |
should the Y data be scaled ? In the case of a 'discriminant' version of the PLS
where the Y data are of discrete type, this should be set to FALSE. |
... |
not used currently. |
pls function fit PLS models with 1, ... ,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 Re-fe-rences).
The number of components to fit is specified with the argument ncomp.
It this is not supplied, the rank of X is used. The rank is compute by
using the mat.rank function.
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. |
Sébastien Déjean and Ignacio González and Kim-Anh Lê Cao.
Tenenhaus, M. (1998). La régression PLS: théorie 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.
spls, summary, mat.rank,
plotIndiv, plotVar.
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)