Fits a PLSR model with the orthogonal scores algorithm (aka the NIPALS algorithm).
oscorespls.fit(X, Y, ncomp, center = TRUE, stripped = FALSE,
tol = .Machine$double.eps^0.5, maxit = 100, …)
a matrix of observations. NA
s and Inf
s are not
allowed.
a vector or matrix of responses. NA
s and Inf
s
are not allowed.
the number of components to be used in the modelling.
logical, determines if the \(X\) and \(Y\) matrices are mean centered or not. Default is to perform mean centering.
logical. If TRUE
the calculations are stripped
as much as possible for speed; this is meant for use with
cross-validation or simulations when only the coefficients are
needed. Defaults to FALSE
.
numeric. The tolerance used for determining convergence in multi-response models.
positive integer. The maximal number of iterations used in the internal Eigenvector calculation.
other arguments. Currently ignored.
A list containing the following components is returned:
an array of regression coefficients for 1, …,
ncomp
components. The dimensions of coefficients
are
c(nvar, npred, ncomp)
with nvar
the number
of X
variables and npred
the number of variables to be
predicted in Y
.
a matrix of scores.
a matrix of loadings.
a matrix of loading weights.
a matrix of Y-scores.
a matrix of Y-loadings.
the projection matrix used to convert X to scores.
a vector of means of the X variables.
a vector of means of the Y variables.
an array of fitted values. The dimensions of
fitted.values
are c(nobj, npred, ncomp)
with
nobj
the number samples and npred
the number of
Y variables.
an array of regression residuals. It has the same
dimensions as fitted.values
.
a vector with the amount of X-variance explained by each component.
Total variance in X
.
If stripped is TRUE, only the components coefficients, Xmeans and Ymeans are returned.
This function should not be called directly, but through
the generic functions plsr
or mvr
with the argument
method="oscorespls"
. It implements the orthogonal scores
algorithm, as described in Martens and N<U+00E6>s (1989). This is one
of the two “classical”
PLSR algorithms, the other being the orthogonal loadings algorithm.
Martens, H., N<U+00E6>s, T. (1989) Multivariate calibration. Chichester: Wiley.