opls_get_all {resemble} | R Documentation |
orthogonal scores algorithn of partial leat squares (opls_get_all)
Description
Computes orthogonal socres partial least squares (opls_get_all) regressions with the NIPALS algorithm. It retrives a comprehensive set of pls outputs (e.g. vip and sensivity radius). It allows multiple response variables. NOTE: For internal use only!
Usage
opls_get_all(X,
Y,
ncomp,
scale,
maxiter,
tol,
algorithm = "pls",
xls_min_w = 3,
xls_max_w = 15)
Arguments
X |
a matrix of predictor variables. |
Y |
a matrix of either a single or multiple response variables. |
ncomp |
the number of pls components. |
scale |
logical indicating whether |
maxiter |
maximum number of iterations. |
tol |
limit for convergence of the algorithm in the nipals algorithm. |
algorithm |
(for weights computation) a character string indicating
what method to use. Options are:
|
xls_min_w |
(for weights computation) an integer indicating the minimum window size for the "xls"
method. Only used if |
xls_max_w |
(for weights computation) an integer indicating the maximum window size for the "xls"
method. Only used if |
Value
a list containing the following elements:
ncomp
: the number of components used.coefficients
: the matrix of regression coefficients.bo
: a matrix of one row containing the intercepts for each component.scores
: the matrix of scores.X_loadings
: the matrix of X loadings.Y_loadings
: the matrix of Y loadings.vip
: the projection matrix.selectivity_ratio
: the matrix of selectivity ratio (see Rajalahti, Tarja, et al. 2009).Y
: theY
input.variance
: alist
conating two objects:x_var
andy_var
. These objects contain information on the explained variance for theX
andY
matrices respectively.transf
: alist
conating two objects:Xcenter
andXscale
.weights
: the matrix of wheights.
Author(s)
Leonardo Ramirez-Lopez