simpls.fit  R Documentation 
Fits a PLSR model with the SIMPLS algorithm.
simpls.fit(X, Y, ncomp, center = TRUE, stripped = FALSE, ...)
X 
a matrix of observations. 
Y 
a vector or matrix of responses. 
ncomp 
the number of components to be used in the modelling. 
center 
logical, determines if the X and Y matrices are mean centered or not. Default is to perform mean centering. 
stripped 
logical. If 
... 
other arguments. Currently ignored. 
This function should not be called directly, but through the generic
functions plsr
or mvr
with the argument
method="simpls"
. SIMPLS is much faster than the NIPALS algorithm,
especially when the number of X variables increases, but gives slightly
different results in the case of multivariate Y. SIMPLS truly maximises the
covariance criterion. According to de Jong, the standard PLS2 algorithms
lie closer to ordinary leastsquares regression where a precise fit is
sought; SIMPLS lies closer to PCR with stable predictions.
A list containing the following components is returned:
coefficients 
an array of regression coefficients for 1, ...,

scores 
a matrix of scores. 
loadings 
a matrix of loadings. 
Yscores 
a matrix of Yscores. 
Yloadings 
a matrix of Yloadings. 
projection 
the projection matrix used to convert X to scores. 
Xmeans 
a vector of means of the X variables. 
Ymeans 
a vector of means of the Y variables. 
fitted.values 
an
array of fitted values. The dimensions of 
residuals 
an array of
regression residuals. It has the same dimensions as 
Xvar 
a vector with the amount of Xvariance explained by each component. 
Xtotvar 
Total variance in 
If stripped
is TRUE
, only the components coefficients
,
Xmeans
and Ymeans
are returned.
Ron Wehrens and BjørnHelge Mevik
de Jong, S. (1993) SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18, 251–263.
mvr
plsr
pcr
kernelpls.fit
widekernelpls.fit
oscorespls.fit
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