Description Usage Arguments Details Value Author(s) References See Also
Fits a PLSR model with the SIMPLS algorithm.
1  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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