svdpc.fit  R Documentation 
Fits a PCR model using the singular value decomposition.
svdpc.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 pcr
or mvr
with the argument method="svdpc"
.
The singular value decomposition is used to calculate the principal
components.
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. 
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
Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.
mvr
plsr
pcr
cppls
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