simpls.fit(X, Y, ncomp, stripped = FALSE, ...)
NA
s and Inf
s are not
allowed.NA
s and Inf
s
are not allowed.TRUE
the calculations are stripped
as much as possible for speed; this is meant for use with
cross-validation or simulations when only the coefficients are
needed. Defaults to FALSE
.ncomp
components. The dimensions of coefficients
are
c(nvar, npred, ncomp)
with nvar
the number
of X
variables and npred
the number of variables to be
predicted in Y
.fitted.values
are c(nobj, npred, ncomp)
with
nobj
the number samples and npred
the number of
Y variables.fitted.values
.X
.stripped
is TRUE
, only the components
coefficients
, Xmeans
and Ymeans
are returned.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 least-squares regression where
a precise fit is sought; SIMPLS lies closer to PCR with stable
predictions.mvr
plsr
pcr
kernelpls.fit
oscorespls.fit