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<table width="100%" summary="page for simpls.fit {pls}"><tr><td>simpls.fit {pls}</td><td align="right">R Documentation</td></tr></table>
<h2>Sijmen de Jong's SIMPLS</h2>
<h3>Description</h3>
<p>
Fits a PLSR model with the SIMPLS algorithm.
</p>
<h3>Usage</h3>
<pre>simpls.fit(X, Y, ncomp, stripped = FALSE, ...)</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>X</code></td>
<td>
a matrix of observations. <code>NA</code>s and <code>Inf</code>s are not
allowed.</td></tr>
<tr valign="top"><td><code>Y</code></td>
<td>
a vector or matrix of responses. <code>NA</code>s and <code>Inf</code>s
are not allowed.</td></tr>
<tr valign="top"><td><code>ncomp</code></td>
<td>
the number of components to be used in the
modelling.</td></tr>
<tr valign="top"><td><code>stripped</code></td>
<td>
logical. If <code>TRUE</code> 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 <code>FALSE</code>.</td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
other arguments. Currently ignored.</td></tr>
</table>
<h3>Details</h3>
<p>
This function should not be called directly, but through
the generic functions <code>plsr</code> or <code>mvr</code> with the argument
<code>method="simpls"</code>. 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.
</p>
<h3>Value</h3>
<p>
A list containing the following components is returned:
</p>
<table summary="R argblock">
<tr valign="top"><td><code>coefficients</code></td>
<td>
an array of regression coefficients for 1, ...,
<code>ncomp</code> components. The dimensions of <code>coefficients</code> are
<code>c(nvar, npred, ncomp)</code> with <code>nvar</code> the number
of <code>X</code> variables and <code>npred</code> the number of variables to be
predicted in <code>Y</code>.</td></tr>
<tr valign="top"><td><code>scores</code></td>
<td>
a matrix of scores.</td></tr>
<tr valign="top"><td><code>loadings</code></td>
<td>
a matrix of loadings.</td></tr>
<tr valign="top"><td><code>Yscores</code></td>
<td>
a matrix of Y-scores.</td></tr>
<tr valign="top"><td><code>Yloadings</code></td>
<td>
a matrix of Y-loadings.</td></tr>
<tr valign="top"><td><code>projection</code></td>
<td>
the projection matrix used to convert X to scores.</td></tr>
<tr valign="top"><td><code>Xmeans</code></td>
<td>
a vector of means of the X variables.</td></tr>
<tr valign="top"><td><code>Ymeans</code></td>
<td>
a vector of means of the Y variables.</td></tr>
<tr valign="top"><td><code>fitted.values</code></td>
<td>
an array of fitted values. The dimensions of
<code>fitted.values</code> are <code>c(nobj, npred, ncomp)</code> with
<code>nobj</code> the number samples and <code>npred</code> the number of
Y variables.</td></tr>
<tr valign="top"><td><code>residuals</code></td>
<td>
an array of regression residuals. It has the same
dimensions as <code>fitted.values</code>.</td></tr>
<tr valign="top"><td><code>Xvar</code></td>
<td>
a vector with the amount of X-variance explained by each
number of components.</td></tr>
<tr valign="top"><td><code>Xtotvar</code></td>
<td>
Total variance in <code>X</code>.</td></tr>
</table>
<p>
<br>
If <code>stripped</code> is <code>TRUE</code>, only the components
<code>coefficients</code>, <code>Xmeans</code> and <code>Ymeans</code> are returned.</p>
<h3>Author(s)</h3>
<p>
Ron Wehrens and Bj鴕n-Helge Mevik
</p>
<h3>References</h3>
<p>
de Jong, S. (1993) SIMPLS: an alternative approach to partial least
squares regression. <EM>Chemometrics and Intelligent Laboratory Systems</EM>,
<B>18</B>, 251–263.
</p>
<h3>See Also</h3>
<p>
<code><a href="mvr.html">mvr</a></code>
<code><a href="mvr.html">plsr</a></code>
<code><a href="mvr.html">pcr</a></code>
<code><a href="kernelpls.fit.html">kernelpls.fit</a></code>
<code><a href="oscorespls.fit.html">oscorespls.fit</a></code>
</p>
<hr><div align="center">[Package <em>pls</em> version 1.1-0 <a href="00Index.html">Index]</a></div>
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