View source: R/PLS_jack_svds_HA.R
PLS4jack | R Documentation |
nfactor
latent variables.in PLS regression (PLSR),PLS4jack
: computes a
supplementary projection for a jackknifed estimation of
one supplementary element.
The prediction is computed
for 1 to nfactor
latent variables.
PLS4jack
is mainly used by
PLSR_SVD
for computing the random effect prediction of jackknifed
observations in PLSR, but it can also be used to
project supplementary observation in PLSR.
PLS4jack(X, Y, xsup, nfactor)
X |
the X matrix of predictors in the PLSR model. |
Y |
the Y matrix to be predicted by tge PLSR model. |
xsup |
the supplementary elements whose Y values are to be predicted. |
nfactor |
number of factors of the model. |
see Abdi (2010) for details and examples.
Yhatsup
the matrix of the predicted values.
Hervé Abdi, Lei Xuan
#' @references
(see also https://personal.utdallas.edu/~herve/
)
Abdi, H. (2010). Partial least square regression, projection on latent structure regression, PLS-Regression. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 97-106.
Abdi, H. (2007). Partial least square regression (PLS regression). In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 740-744.
Abdi. H. (2003). Partial least squares regression (PLS-regression). In M. Lewis-Beck, A. Bryman, T. Futing (Eds): Encyclopedia for Research Methods for the Social Sciences. Thousand Oaks (CA): Sage. pp. 792-795.
PLSR_SVD
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