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%%%%%%%%%%%%%%%%
\begin{document}
%%%%%%%%%%%%%%%%
\title{Introduction to the \pkg{pls} Package}
\author{
Bjørn-Helge Mevik\\ %\thanks for footnotes
University Center for Information Technology, University of Oslo\\
Norway\\
\and
Ron Wehrens\\
Biometris, Wageningen University \& Research\\
The Netherlands\\
}
\maketitle
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@
\begin{abstract}
The \pkg{pls} package implements Principal Component Regression (PCR) and
Partial Least Squares Regression (PLSR) in
\proglang{R}, and is freely available from the
CRAN website, licensed under the Gnu General Public License (GPL).
The user interface is modelled after the traditional formula
interface, as exemplified by \code{lm}. This was done so that people
used to \proglang{R} would not have to learn yet another interface, and also
because we believe the formula interface is a good way of working
interactively with models. It thus has methods for generic functions like
\code{predict}, \code{update} and \code{coef}. It also has more specialised
functions like \code{scores}, \code{loadings} and \code{RMSEP}, and a
flexible cross-validation system. Visual inspection and assessment is
important in chemometrics, and the \pkg{pls} package has a number of plot
functions for plotting scores, loadings, predictions, coefficients and RMSEP
estimates.
The package implements PCR and several algorithms for PLSR. The design is
modular, so that it should be easy to use the underlying algorithms in other
functions. It is our hope that the package will serve well both for
interactive data analysis and as a building block for other functions or
packages using PLSR or PCR.
We will here describe the package and how it is used for data analysis, as
well as how it can be used as a part of other packages. Also included is a
section about formulas and data frames, for people not used to the
\proglang{R} modelling idioms.
\end{abstract}
\section{Introduction}\label{sec:introduction}
%%%%%%%%%%%%%%%%
This vignette is meant as an introduction to the \pkg{pls} package. It is
based on the paper `The pls Package: Principal Component and Partial Least
Squares Regression in R', published in
\emph{Journal of Statistical Software}~\cite{MevWeh:plsJSS}.
The PLSR methodology is shortly described in Section~\ref{sec:theory}.
Section~\ref{sec:example-session} presents an example session, to get an
overview of the package. In Section~\ref{sec:formula-data-frame} we
describe formulas and data frames (as they are used in \pkg{pls}). Users
familiar with formulas and data frames in \proglang{R} can skip this section
on first reading. Fitting of models is described in
Section~\ref{sec:fitting}, and cross-validatory choice of components is
discussed in Section~\ref{sec:cross-validation}. Next, inspecting and
plotting models is described (Section~\ref{sec:inspecting}), followed by a
section on predicting future observations (Section~\ref{sec:predicting}).
Finally, Section~\ref{sec:advanced} covers more advanced topics such as
parallel computing, setting options, using the underlying functions directly,
and implementation details.
\section{Theory}\label{sec:theory}
%%%%%%%%%%%%%%%%
Multivariate regression methods like Principal Component Regression
(PCR) and Partial Least Squares Regression (PLSR) enjoy large
popularity in a wide range of fields, including the natural
sciences. The main reason is that they have been designed to confront
the situation that there are many, possibly correlated, predictor
variables, and relatively few samples---a situation that is common,
especially in chemistry where developments in spectroscopy since the
seventies have revolutionised chemical analysis. In fact, the origin
of PLSR lies in chemistry (see, e.g.,~\cite{Wold2001,Martens2001}). The field
of \emph{near-infrared} (NIR) spectroscopy,
with its highly overlapping lines and difficult to interpret
overtones, would not have existed but for a method to obtain
quantitative information from the spectra.
Also other fields have benefited greatly from multivariate regression
methods like PLSR and PCR. In medicinal chemistry, for example, one
likes to derive molecular properties from the molecular
structure. Most of these Quantitative Structure-Activity Relations
(QSAR, and also Quantitative Structure-Property Relations, QSPR), and
in particular, Comparative Molecular
Field Analysis (ComFA)~\cite{Cramer1988}, use PLSR. Other applications
range from statistical process control~\cite{Kresta1991} to tumour
classification~\cite{Nguyen2002} to spatial analysis in brain
images~\cite{McIntosh1996} to marketing~\cite{Fornell1982}.
In the usual multiple linear regression (MLR) context, the
least-squares solution for
\begin{equation}
\bY = \bX\bB + \mathcal{E}
\end{equation}
is given by
\begin{equation}
\bB = (\bX^T \bX)^{-1} \bX^T \bY
\label{eq:lsq}
\end{equation}
The problem often is that $\bX^T \bX$ is singular, either
because the number of variables (columns) in $\bX$ exceeds the number
of objects (rows), or because of collinearities. Both PCR and
PLSR circumvent this by decomposing $\bX$ into orthogonal scores $\bT$ and
loadings $\bP$
\begin{equation}
\bX = \bT \bP
\end{equation}
and regressing $\bY$ not on $\bX$ itself but on the first $a$ columns
of the scores $\bT$. In PCR, the scores are given by the
left singular vectors of $\bX$, multiplied with the corresponding
singular values, and the loadings are the right singular vectors of
$\bX$. This, however, only takes into account
information about $\bX$, and therefore may be suboptimal for
prediction purposes. PLSR aims to incorporate information on both $\bX$
and $\bY$ in the definition of the scores and loadings. In fact, for
one specific version of PLSR, called SIMPLS~\cite{Jong1993a}, it can be
shown that the scores and loadings are chosen in such a way to
describe as much as possible of the {\em covariance} between $\bX$ and
$\bY$, where PCR concentrates on the {\em variance} of $\bX$. Other
PLSR algorithms give identical results to SIMPLS in the case of one
$\bY$ variable, but deviate slightly for the multivariate $\bY$ case;
the differences are not likely to be important in practice.
\subsection{Algorithms}
In PCR, we approximate the $\bX$ matrix by the first $a$ Principal
Components (PCs), usually obtained from the singular value
decomposition (SVD):
\[
\bX = \tilde{\bX}_{(a)} + \mathcal{E}_X
= (\bU_{(a)} \bD_{(a)} ) \bV^T_{(a)} + \mathcal{E}_X
= \bT_{(a)} \bP_{(a)}^T + \mathcal{E}_X
\]
Next, we regress $\bY$ on the scores, which leads to regression
coefficients
\[
\bB = \bP (\bT^T \bT)^{-1} \bT^T \bY
= \bV \bD^{-1} \bU^T \bY
\]
where the subscripts $a$ have been dropped for clarity.
For PLSR, the components, called Latent Variables (LVs) in this
context, are obtained iteratively. One starts with the
SVD of the crossproduct matrix $\bS = \bX^T \bY$, thereby including
information on variation in both $\bX$ and $\bY$, and on the
correlation between them. The first left and right singular vectors,
$w$ and $q$, are used as weight vectors for $\bX$ and $\bY$,
respectively, to obtain scores $t$ and $u$:
\begin{equation}
t = \bX w = \bE w
\end{equation}
\begin{equation}
u = \bY q = \bF q
\end{equation}
where $\bE$ and $\bF$ are initialised as $\bX$ and $\bY$,
respectively. The X scores $t$ are often normalised:
\begin{equation}
t = t / \sqrt{t^Tt}
\end{equation}
The Y scores $u$ are not actually necessary in the regression but are often
saved for interpretation purposes. Next, X and Y loadings are
obtained by regressing against the {\em same} vector $t$:
\begin{equation}
\label{eq:plspt}
p = \bE^T t
\end{equation}
\begin{equation}
\label{eq:plsqt}
q = \bF^T t
\end{equation}
Finally, the data matrices are `deflated': the information related
to this latent variable, in the form of the outer products $t p^T$ and
$t q^T$, is subtracted from the (current) data matrices $\bE$ and $\bF$.
\begin{equation}
\bE_{n+1} = \bE_n - t p^T
\end{equation}
\begin{equation}
\bF_{n+1} = \bF_n - t q^T
\end{equation}
The estimation of the next component then can start from the SVD of
the crossproduct matrix $\bE_{n+1}^T\bF_{n+1}$. After every iteration,
vectors $w$, $t$, $p$ and $q$ are saved as columns in matrices $\bW$,
$\bT$, $\bP$ and $\bQ$, respectively. One complication is that columns
of matrix $\bW$ can not be compared directly: they are derived from
successively deflated matrices $\bE$ and $\bF$. It has been shown that
an alternative way to represent the weights, in such a way that all
columns relate to the original $\bX$ matrix, is given by
\begin{equation}
\bR = \bW (\bP^T \bW)^{-1}
\end{equation}
Now, we are in the same position as in the PCR case: instead of
regressing $\bY$ on $\bX$, we use scores $\bT$ to calculate the
regression coefficients, and later convert these back to the
realm of the original variables by pre-multiplying with matrix $\bR$
(since $\bT = \bX \bR$):
\[
\bB = \bR (\bT^T \bT)^{-1} \bT^T \bY
= \bR \bT^T \bY
= \bR \bQ^T
\]
Again, here, only the first $a$ components are used. How many
components are optimal has to be determined, usually by
cross-validation.
Many alternative formulations can be found in literature. It has been
shown, for instance, that only one of $\bX$ and $\bY$ needs to be
deflated; alternatively, one can directly deflate the crossproduct
matrix $\bS$ (as is done in SIMPLS, for example). Moreover, there are
many equivalent ways of scaling. In the example above, the scores $t$
have been normalised, but one can also choose to introduce
normalisation at another point in the algorithm. Unfortunately, this
can make it difficult to directly compare the scores and loadings of
different PLSR implementations.
\subsection{On the use of PLSR and PCR}
In theory, PLSR should have an advantage over PCR. One could imagine a
situation where a minor component in $\bX$ is highly correlated with
$\bY$; not selecting enough components would then lead to very bad
predictions. In PLSR, such a component would be automatically present
in the first LV. In practice, however, there is hardly any difference
between the use of PLSR and PCR; in most situations, the methods
achieve similar prediction accuracies, although PLSR usually needs
fewer latent variables than PCR. Put the other way around: with the
same number of latent variables, PLSR will cover more of the variation
in $\bY$ and PCR will cover more of $\bX$. In turn, both behave very
similar to ridge regression~\cite{Frank1993}.
It can also be shown that both PCR and PLSR behave
as shrinkage methods~\cite{TESL}, although in some cases PLSR seems to
increase the variance of individual regression coefficients, one possible
explanation of why PLSR is not always better than PCR.
\section{Example session}\label{sec:example-session}
%%%%%%%%%%%%%%%%
In this section we will walk through an example session, to get an overview
of the package.
To be able to use the package, one first has to load it:
<<>>=
library(pls)
@
This prints a message telling that the package has been attached, and that
the package implements a function \code{loadings} that masks a function of
the same name in package \pkg{stats}. (The output of the commands have in
some cases been suppressed to save space.)
Three example data sets are included in \pkg{pls}:
\begin{description}
\item[\code{yarn}] A data set with 28 near-infrared spectra (\code{NIR}) of PET yarns,
measured at 268 wavelengths, as predictors, and density as response
(\code{density})~\cite{SwiWeiWijBuy_StrConRobMulCalMod}. The data set also
includes a logical variable \code{train} which can be used to split the
data into a training data set of size 21 and test data set of size 7. See
\code{?yarn} for details.
\item[\code{oliveoil}] A data set with 5 quality measurements
(\code{chemical}) and 6 panel sensory panel variables (\code{sensory}) made
on 16 olive oil samples~\cite{Mas_etal_HandChemQualB}. See
\code{?oliveoil} for details.
\item[\code{gasoline}] A data set consisting of octane number
(\code{octane}) and NIR spectra (\code{NIR}) of 60 gasoline
samples~\cite{Kal:2DatNIR}. Each NIR spectrum consists of 401 diffuse
reflectance measurements from 900 to 1700 nm. See \code{?gasoline} for
details.
\end{description}
These will be used in the examples that follow. To use the data sets, they
must first be loaded:
<<>>=
data(yarn)
data(oliveoil)
data(gasoline)
@
For the rest of the paper, it will be assumed that the package and the data
sets have been loaded as above. Also, all examples are run with
\code{options(digits = 4)}.
\begin{figure}
\begin{center}
<>=
par(mar = c(2, 4, 0, 1) + 0.1)
matplot(t(gasoline$NIR), type = "l", lty = 1, ylab = "log(1/R)", xaxt = "n")
ind <- pretty(seq(from = 900, to = 1700, by = 2))
ind <- ind[ind >= 900 & ind <= 1700]
ind <- (ind - 898) / 2
axis(1, ind, colnames(gasoline$NIR)[ind])
@
\caption{Gasoline NIR spectra}\label{fig:NIR}
\end{center}
\end{figure}
In this section, we will do a PLSR on the \code{gasoline} data to illustrate
the use of \pkg{pls}. The spectra are shown in Figure~\ref{fig:NIR}. We
first divide the data set into train and test data sets:
<<>>=
gasTrain <- gasoline[1:50,]
gasTest <- gasoline[51:60,]
@
A typical way of fitting a PLSR model is
<<>>=
gas1 <- plsr(octane ~ NIR, ncomp = 10, data = gasTrain, validation = "LOO")
@
This fits a model with 10 components, and includes \emph{leave-one-out} (LOO)
cross-validated predictions~\cite{LacMic:EstERRDA}. We can get an overview
of the fit and validation results with the \code{summary} method:
<<>>=
summary(gas1)
@
The validation results here are \emph{Root Mean Squared Error of Prediction}
(RMSEP). There are two cross-validation estimates: \code{CV} is the
ordinary CV estimate, and \code{adjCV} is a bias-corrected CV
estimate~\cite{MevCed:MSEPest}. (For a LOO CV, there is virtually no
difference).
It is often simpler to judge the RMSEPs by plotting them:
<>=
plot(RMSEP(gas1), legendpos = "topright")
@
%
\begin{figure}
\begin{center}
<>=
par(mar = c(4, 4, 2.5, 1) + 0.1)
plot(RMSEP(gas1), legendpos = "topright")
@
\caption{Cross-validated RMSEP curves for the \code{gasoline} data}\label{fig:RMSEPplsr}
\end{center}
\end{figure}
This plots the estimated RMSEPs as functions of the number of components
(Figure~\ref{fig:RMSEPplsr}). The \code{legendpos} argument adds a legend at
the indicated position. Two components seem to be enough. This gives an
RMSEP of
\Sexpr{format(drop(RMSEP(gas1, "CV", ncomp = 2, intercept = FALSE)$val), digits = 3)}. %$
As mentioned in the introduction, the main practical difference between PCR
and PLSR is that PCR often needs more components than PLSR to achieve the
same prediction error. On this data set, PCR would need three components to
achieve the same RMSEP.
Once the number of components has been chosen, one can inspect different
aspects of the fit by plotting predictions, scores, loadings, etc.
The default plot is a prediction plot:
<>=
plot(gas1, ncomp = 2, asp = 1, line = TRUE)
@
%
\begin{figure}
\begin{center}
\setkeys{Gin}{width=3.5in}
<>=
par(mar = c(4, 4, 2.5, 1) + 0.1)
plot(gas1, ncomp = 2, asp = 1, line = TRUE)
@
\setkeys{Gin}{width=5in}
\caption{Cross-validated predictions for the \code{gasoline}
data}\label{fig:cvpreds}
\end{center}
\end{figure}
This shows the cross-validated predictions with two components versus
measured values (Figure~\ref{fig:cvpreds}). We have chosen an aspect ratio
of 1, and to draw a target line. The points follow the target line quite
nicely, and there is no indication of a curvature or other anomalies.
Other plots can be selected with the argument \code{plottype}:
<>=
plot(gas1, plottype = "scores", comps = 1:3)
@
%
\begin{figure}
\begin{center}
<>=
plot(gas1, plottype = "scores", comps = 1:3)
@
\caption{Score plot for the \code{gasoline} data}\label{fig:scores}
\end{center}
\end{figure}
\begin{figure}
\begin{center}
<>=
par(mar = c(4, 4, 0.3, 1) + 0.1)
plot(gas1, "loadings", comps = 1:2, legendpos = "topleft",
labels = "numbers", xlab = "nm")
abline(h = 0)
@
\caption{Loading plot for the \code{gasoline} data}\label{fig:loadings}
\end{center}
\end{figure}
This gives a pairwise plot of the score values for the three first
components (Figure~\ref{fig:scores}). Score plots are often used to look for
patterns, groups or outliers in the data. (For instance, plotting the two
first components for a model built on the \code{yarn} dataset clearly
indicates the experimental design of that data.) In this example, there is
no clear indication of grouping or outliers. The numbers in parentheses
after the component labels are the relative amount of X variance
explained by each component. The explained variances can be extracted
explicitly with
<<>>=
explvar(gas1)
@
The loading plot (Figure~\ref{fig:loadings}) is much
used for interpretation purposes, for instance to look for known spectral
peaks or profiles:
<>=
plot(gas1, "loadings", comps = 1:2, legendpos = "topleft",
labels = "numbers", xlab = "nm")
abline(h = 0)
@
%
The \code{labels = "numbers"} argument makes the plot function try to
interpret the variable names as numbers, and use them as $x$ axis labels.
A fitted model is often used to predict the response values of new
observations. The following predicts the responses for the ten observations
in \code{gasTest}, using two components:
<<>>=
predict(gas1, ncomp = 2, newdata = gasTest)
@
Because we know the true response values for these samples, we can calculate
the test set RMSEP:
<<>>=
RMSEP(gas1, newdata = gasTest)
@
For two components, we get
\Sexpr{format(drop(RMSEP(gas1, ncomp = 2, intercept = FALSE, newdata = gasTest)$val), digits = 3)}, %$
which is quite close to the cross-validated estimate above
(\Sexpr{format(drop(RMSEP(gas1, "CV", ncomp = 2, intercept = FALSE)$val), digits = 3)}). %$
\goodbreak
\section{Formulas and data frames}\label{sec:formula-data-frame}
%%%%%%%%%%%%%%%%
The \pkg{pls} package has a formula interface that works like the formula
interface in \proglang{R}'s standard \code{lm} functions, in most ways.
This section gives a short description of formulas and data frames as they
apply to \pkg{pls}. More information on formulas can be found in the
\code{lm} help file, in Chapter~11 of `An Introduction to R', and in
Chapter~2 of `The White Book'~\cite{R:Chambers+Hastie:1992}. These are
good reads for anyone wanting to understand how \proglang{R} works with formulas, and
the user is strongly advised to read them.
\subsection{Formulas}
%%%%%%%%%%%%%%%%
A \emph{formula} consists of a \emph{left hand side} (lhs), a tilde
(\code{\textasciitilde}), and a \emph{right hand side} (rhs). The lhs
consists of a single term, representing the response(s). The rhs consists of
one or more terms separated by \code{+}, representing the regressor(s). For
instance, in the formula \code{a \textasciitilde\ b + c + d}, \code{a} is the
response, and \code{b}, \code{c}, and \code{d} are the regressors. The
intercept is handled automatically, and need not be specified in the formula.
Each term represents a matrix, a numeric vector or a factor (a
factor should not be used as the response). If the response term is a
matrix, a multi-response model is fit.
In \pkg{pls}, the right hand side quite often consists of a
single term, representing a matrix regressor: \code{y \textasciitilde\ X}.
It is also possible to specify transformations of the variables. For
instance, \code{log(y) \textasciitilde\ msc(Z)} specifies a regression of the
logarithm of \code{y} onto \code{Z} after \code{Z} has been transformed by
\emph{Multiplicative Scatter (or Signal) Correction} (MSC)~\cite{Geladi1985},
a pre-treatment that is very common in infrared spectroscopy. If the
transformations contain symbols that are interpreted in the formula handling,
e.g., \code{+}, \code{*} or \verb|^|, the terms should be protected with the
\code{I()} function, like this: \code{y \textasciitilde\ x1 + I(x2 + x3)}. This specifies
\emph{two} regressors: \code{x1}, and the sum of \code{x2} and \code{x3}.
\subsection{Data frames}
%%%%%%%%%%%%%%%%
The fit functions first look for the specified variables in a supplied
data frame, and it is advisable to collect all variables there. This
makes it easier to know what data has been used for fitting, to keep
different variants of the data around, and to predict new data.
To create a data frame, one can use the \code{data.frame} function: if
\code{v1}, \code{v2} and \code{v3} are factors or numeric vectors,
\code{mydata <- data.frame(y = v1, a = v2, b = v3)} will result in a data
frame with variables named \code{y}, \code{a} and \code{b}.
PLSR and PCR are often used with a matrix as the single predictor term
(especially when one is working with spectroscopic data). Also,
multi-response models require a matrix as the response term. If \code{Z} is
a matrix, it has to be protected by the `protect function' \code{I()} in
calls to \code{data.frame}: \code{mydata <- data.frame(..., Z = I(Z))}.
Otherwise, it will be split into separate variables for each column, and
there will be no variable called \code{Z} in the data frame, so we cannot
use \code{Z} in the formula.
One can also add the matrix to an existing data frame:
\begin{verbatim}
> mydata <- data.frame(...)
> mydata$Z <- Z
\end{verbatim}
%$
This will also prevent \code{Z} from being split into separate variables.
Finally, one can use \code{cbind} to combine vectors and matrices into
matrices on the fly in the formula. This is most useful for the response, e.g.,
\code{cbind(y1, y2) \textasciitilde\ X}.
Variables in a data frame can be accessed with the \code{\$} operator, e.g.,
\code{mydata\$y}. However, the \pkg{pls} functions access the variables
automatically, so the user should never use \code{\$} in formulas.
\section{Fitting models}\label{sec:fitting}
%%%%%%%%%%%%%%%%
The main functions for fitting models are \code{pcr} and \code{plsr}. (They
are simply wrappers for the function \code{mvr}, selecting the appropriate
fit algorithm). We will use \code{plsr} in the examples in this section,
but everything could have been done with \code{pcr} (or \code{mvr}).
In its simplest form, the function call for fitting models is
\code{plsr(formula, ncomp, data)} (where \code{plsr} can be substituted with
\code{pcr} or \code{mvr}). The argument \code{formula} is a formula as
described above, \code{ncomp} is the number of components one wishes to fit,
and \code{data} is the data frame containing the variables to use in the
model. The function returns a fitted model (an object of class
\code{"mvr"}) which can be inspected (Section~\ref{sec:inspecting}) or used
for predicting new observations (Section~\ref{sec:predicting}). For
instance:
<<>>=
dens1 <- plsr(density ~ NIR, ncomp = 5, data = yarn)
@
If the response term of the formula is a matrix, a multi-response model is
fit, e.g.,
<<>>=
dim(oliveoil$sensory)
plsr(sensory ~ chemical, data = oliveoil)
@
(As we see, the \code{print} method simply tells us what type of model this
is, and how the fit function was called.)
The argument \code{ncomp} is optional. If it is missing, the maximal
possible number of components are used. Also \code{data} is optional, and
if it is missing, the variables specified in the formula is searched for in
the global environment (the user's workspace). Usually, it is preferable to
keep the variables in data frames, but it can sometimes be convenient to
have them in the global environment. If the variables reside in a data
frame, e.g.\ \code{yarn}, \emph{do not} be tempted to use formulas like
\code{yarn\$density \textasciitilde\ yarn\$NIR}! Use \code{density
\textasciitilde\ NIR} and specify the
data frame with \code{data = yarn} as above.
There are facilities for working interactively with models. To use only
part of the samples in a data set, for instance the first 20, one can use
arguments \code{subset = 1:20} or \code{data = yarn[1:20,]}.
Also, if one wants to try different alternatives of the model, one can use
the function \code{update}. For instance
<<>>=
trainind <- which(yarn$train == TRUE)
dens2 <- update(dens1, subset = trainind)
@
will refit the model \code{dens1} using only the observations which are
marked as \code{TRUE} in \code{yarn\$train}, and
<<>>=
dens3 <- update(dens1, ncomp = 10)
@
will change the number of components to 10. Other arguments, such as
\code{formula}, can also be changed with \code{update}. This can save a bit
of typing when working interactively with models (but it doesn't save
computing time; the model is refitted each time).
In general, the reader is referred to `The White
Book'~\cite{R:Chambers+Hastie:1992} or `An Introduction to R' for more
information about fitting and working with models in \proglang{R}.
Missing data can sometimes be a problem. The PLSR and PCR algorithms
currently implemented in \pkg{pls} do not handle missing values
intrinsically, so observations with missing values must be removed. This
can be done with the \code{na.action} argument. With \code{na.action =
na.omit} (the default), any observation with missing values will be removed
from the model completely. With \code{na.action = na.exclude}, they will be
removed from the fitting process, but included as \code{NA}s in the
residuals and fitted values. If you want an explicit error when there are
missing values in the data, use \code{na.action = na.fail}. The default
\code{na.action} can be set with \code{options()}, e.g.,
\code{options(na.action = quote(na.fail))}.
Standardisation and other pre-treatments of predictor variables are often
called for. In \code{pls}, the predictor variables are always centered, as a
part of the fit algorithm. Scaling can be requested with the \code{scale}
argument. If \code{scale} is \code{TRUE}, each variable is standardised by
dividing it by its standard deviation, and if \code{scale} is a numeric
vector, each variable is divided by the corresponding number.
For instance, this will fit a model with standardised chemical
measurements:
<<>>=
olive1 <- plsr(sensory ~ chemical, scale = TRUE, data = oliveoil)
@
As mentioned earlier, MSC~\cite{Geladi1985} is implemented in \pkg{pls} as a
function \code{msc} that can be used in formulas:
<<>>=
gas2 <- plsr(octane ~ msc(NIR), ncomp = 10, data = gasTrain)
@
This scatter corrects \code{NIR} prior to the fitting, and arranges for new
spectra to be automatically scatter corrected (using the same reference
spectrum as when fitting) in \code{predict}:
<>=
predict(gas2, ncomp = 3, newdata = gasTest)
@
There are other arguments that can be given in the fit call:
\code{validation} is for selecting validation, and \code{...} is for sending
arguments to the underlying functions, notably the cross-validation function
\code{mvrCv}. For the other arguments, see \code{?mvr}.
\section{Choosing the number of components with cross-validation}\label{sec:cross-validation}
%%%%%%%%%%%%%%%%
Cross-validation, commonly used to determine the optimal number of
components to take into account, is controlled by the \code{validation}
argument in the modelling functions (\code{mvr}, \code{plsr} and
\code{pcr}). The default value is \code{"none"}. Supplying a value of
\code{"CV"} or \code{"LOO"} will cause the modelling procedure to call
\code{mvrCv} to perform cross-validation; \code{"LOO"} provides
leave-one-out cross-validation, whereas \code{"CV"} divides the data into
segments. Default is to use ten segments, randomly selected, but also
segments of consecutive objects or interleaved segments (sometimes also
referred to as `Venetian blinds') are possible through the use of the
argument \code{segment.type}. One can also specify the segments explicitly
with the argument \code{segments}; see \code{?mvrCv} for details.
When validation is performed in this way, the
model will contain an element comprising information on the out-of-bag
predictions (in the form of predicted values, as well as MSEP and R2
values). As a reference, the MSEP error using no components at all is
calculated as well. The validation results can be visualised using the
\code{plottype = "validation"} argument of the standard plotting
function. An example is shown in Figure~\ref{fig:RMSEPplsr} for the
gasoline data; typically, one would select a number of components
after which the cross-validation error does not show a significant
decrease.
The decision on how many components to retain will to some extent
always be subjective. However, especially when building large numbers
of models (e.g., in simulation studies), it can be crucial to have a
consistent strategy on how to choose the ``optimal'' number of
components. Two such strategies have been implemented in function
\code{selectNcomp}. The first is based on the so-called one-sigma
heuristic~\cite{TESL2013} and consists of choosing the model with fewest
components that is still less than one standard error away from the
overall best model. The second strategy employs a permutation
approach, and basically tests whether adding a new component is
beneficial at all~\cite{Voet1994}. It is implemented backwards, again
taking the global minimum in the crossvalidation curve as a starting
point, and assessing models with fewer and fewer components: as long
as no significant deterioration in performance is found (by default on
the $\alpha = 0.01$ level), the algorithm
continues to remove components. Applying the function is quite
straightforward:
<>=
ncomp.onesigma <- selectNcomp(gas2, method = "onesigma", plot = TRUE,
ylim = c(.18, .6))
ncomp.permut <- selectNcomp(gas2, method = "randomization", plot = TRUE,
ylim = c(.18, .6))
@
This leads to the plots in Figure~\ref{fig:NComp} -- note that
graphical arguments can be supplied to customize the plots. In both cases,
the global minimum of the crossvalidation curve is indicated with gray
dotted lines, and the suggestion for the optimal number of components
with a vertical blue dashed line. The left plot shows the width of the
one-sigma intervals on which the suggestion is based; the right plot
indicates which models have been assessed by the permutation approach
through the large blue circles. The two criteria do not always agree
(as in this case) but usually are quite close.
\begin{figure}[tb]
\centering
\setkeys{Gin}{width=\textwidth}
<>=
par(mfrow = c(1,2))
ncomp.onesigma <- selectNcomp(gas1, "onesigma", plot = TRUE,
ylim = c(.18, .6))
ncomp.permut <- selectNcomp(gas1, "randomization", plot = TRUE,
ylim = c(.18, .6))
@
\caption{The two strategies for suggesting optimal model dimensions:
the left plot shows the one-sigma strategy, the right plot the
permutation strategy.}
\label{fig:NComp}
\end{figure}
When a pre-treatment that depends on the composition of the training set is
applied, the cross-validation procedure as described above is not optimal,
in the sense that the cross-validation errors are biased downward. As long
as the only purpose is to select the optimal number of components, this bias
may not be very important, but it is not too difficult to avoid it. The
modelling functions have an argument \code{scale} that can be used for
auto-scaling per segment. However, more elaborate methods such as MSC need
explicit handling per segment. For this, the function \code{crossval} is
available. It takes an \code{mvr} object and performs the cross-validation
as it should be done: applying the pre-treatment for each segment. The
results can be shown in a plot (which looks very similar to
Figure~\ref{fig:RMSEPplsr}) or summarised in numbers.
<<>>=
gas2.cv <- crossval(gas2, segments = 10)
plot(MSEP(gas2.cv), legendpos="topright")
summary(gas2.cv, what = "validation")
@
Applying MSC in this case leads to nearly identical cross-validation
estimates of prediction error.
When the scaling does not depend on the division of the data into
segments (e.g., log-scaling), functions \code{crossval} and
\code{mvrCv} give the same results; however, \code{crossval} is much
slower.
Cross-validation can be computationally demanding (especially when using the
function \code{crossval}). Therefore, both \code{mvrCv} and \code{crossval}
can perform the calculations in parallel on a multi-core machine or on
several machines. How to do this is described in
Section~\ref{sec:parallel-cv}.
\section{Inspecting fitted models}\label{sec:inspecting}
%%%%%%%%%%%%%%%%
A closer look at the fitted model may reveal interesting agreements or
disagreements with what is known about the relations between X and
Y. Several functions are implemented in \pkg{pls} for plotting,
extracting and summarising model components.
\subsection{Plotting}
%%%%%%%%%%%%%%%%
One can access all plotting functions through the \code{"plottype"}
argument of the \code{plot} method for \code{mvr} objects. This is simply a wrapper
function calling the actual plot functions; the latter are available
to the user as well.
The default plot is a prediction plot (\code{predplot}), showing predicted
versus measured values. Test set predictions are used if a test set is
supplied with the \code{newdata} argument. Otherwise, if the model was
built using cross-validation, the cross-validated predictions are used,
otherwise the predictions for the training set. This can be overridden with
the \code{which} argument. An example of this type of plot can be seen in
Figure~\ref{fig:cvpreds}. An optional argument can be used to indicate how
many components should be included in the prediction.
To assess how many components are optimal, a validation plot
(\code{validationplot}) can be
used such as the one shown in Figure~\ref{fig:RMSEPplsr}; this shows a
measure of prediction performance (either RMSEP,
MSEP, or $R^2$) against the number of components. Usually, one takes the
first local minimum rather than the absolute minimum in the curve, to
avoid over-fitting.
The regression coefficients can be visualised using
\code{plottype = "coef"} in the \code{plot} method, or directly through
function \code{coefplot}. This allows simultaneous plotting of the
regression vectors for several different numbers of components at
once. The regression vectors for the \code{gasoline} data set using
MSC are shown in Figure~\ref{fig:gascoefs} using the command
<>=
plot(gas1, plottype = "coef", ncomp=1:3, legendpos = "bottomleft",
labels = "numbers", xlab = "nm")
@
\begin{figure}
\begin{center}
<>=
par(mar = c(4, 4, 2.5, 1) + 0.1)
plot(gas1, plottype = "coef", ncomp=1:3, legendpos = "bottomleft",
labels = "numbers", xlab = "nm")
@
\caption{Regression coefficients for the \code{gasoline} data}\label{fig:gascoefs}
\end{center}
\end{figure}
Note that the coefficients for two components and three components are
similar. This is because the third component contributes little to the
predictions. The RMSEPs (see Figure~\ref{fig:RMSEPplsr}) and predictions
(see Section~\ref{sec:predicting}) for two and three components are quite
similar.
Scores and loadings can be plotted using functions \code{scoreplot}
(an example is shown in Figure~\ref{fig:scores})
and \code{loadingplot} (in Figure~\ref{fig:loadings}),
respectively. One can indicate the number of
components with the \code{comps} argument; if more than two components
are given, plotting the scores will give a pairs plot, otherwise a
scatter plot. For \code{loadingplot}, the default is to use line plots.
Finally, a `correlation loadings' plot (function \code{corrplot}, or
\code{plottype = "correlation"} in \code{plot}) shows the
correlations between each variable and the selected components (see
Figure~\ref{fig:corrplot}). These
plots are scatter plots of two sets of scores with concentric circles
of radii given by \code{radii}. Each point corresponds to an X variable.
The squared distance between the point and the origin equals the fraction
of the variance of the variable explained by the components in the
panel. The default values for \code{radii} correspond to 50\% and
100\% explained variance, respectively.
\begin{figure}
\begin{center}
\setkeys{Gin}{width=3.5in}
<>=
par(mar = c(4, 4, 0, 1) + 0.1)
plot(gas1, plottype = "correlation")
@
\setkeys{Gin}{width=5in}
\caption{Correlation loadings plot for the \code{gasoline} data}\label{fig:corrplot}
\end{center}
\end{figure}
The plot functions accept most of the ordinary plot parameters, such as
\code{col} and \code{pch}.
If the model has several responses or one selects more than one
model size, e.g.\ \code{ncomp = 4:6}, in some plot functions (notably
prediction plots (see below), validation plots and coefficient plots) the
plot window will be divided and one plot will be shown for
each combination of response and model size. The number of rows and columns
are chosen automatically, but can be specified explicitly with arguments
\code{nRows} and \code{nCols}. If there are more plots than fit the plot
window, one will be asked to press return to see the rest of the plots.
\subsection{Extraction}
%%%%%%%%%%%%%%%%
Regression coefficients can be extracted using the generic function
\code{coef}; the function takes several arguments, indicating the
number of components to take into account, and whether the intercept is
needed (default is \code{FALSE}).
Scores and loadings can be extracted using functions \code{scores} and
\code{loadings} for X, and \code{Yscores} and
\code{Yloadings} for Y. These also return the percentage of variance
explained as attributes. In PLSR, weights can be extracted using the function
\code{loading.weights}. When applied to a PCR model, the function
returns \code{NULL}.
Note that commands like \code{plot(scores(gas1))} are perfectly
correct, and lead to exactly the same plots as using \code{scoreplot}.
\subsection{Summaries}
%%%%%%%%%%%%%%%%
The \code{print} method for an object of class \code{"mvr"} shows the
regression type used, perhaps indicating the form of validation
employed, and shows the function call. The \code{summary} method gives
more information: it also shows the amount of variance explained by
the model (for all choices of $a$, the number of latent
variables). The \code{summary} method has an additional argument
(\code{what}) to be able to focus on the training phase or validation
phase, respectively. Default is to print both types of information.
\section{Predicting new observations}\label{sec:predicting}
%%%%%%%%%%%%%%%%
Fitted models are often used to predict future observations, and
\pkg{pls} implements a \code{predict} method for PLSR and PCR models. The
most common way of calling this function is
\code{predict(mymod, ncomp = myncomp, newdata = mynewdata)}, where
\code{mymod} is a fitted model, \code{myncomp} specifies the model size(s) to
use, and \code{mynewdata} is a data frame with new X observations. The
data frame can also contain response measurements for the new observations,
which can be used to compare the predicted values to the measured ones, or to
estimate the overall prediction ability of the model. If \code{newdata} is
missing, \code{predict} uses the data used to fit the model, i.e., it returns
fitted values.
If the argument \code{ncomp} is missing, \code{predict} returns predictions
for models with 1 component, 2 components, $\ldots$, $A$ components, where
$A$ is the number of components used when fitting the model. Otherwise, the
model size(s) listed in \code{ncomp} are used. For instance, to get
predictions from the model built in Section~\ref{sec:example-session}, with
two and three components, one would use
<<>>=
predict(gas1, ncomp = 2:3, newdata = gasTest[1:5,])
@
(We predict only the five first test observations, to save space.) The
predictions with two and three components are quite similar. This could be
expected, given that the regression vectors (Figure~\ref{fig:gascoefs})
as well as the estimated RMSEPs for the two model sizes were similar.
One can also specify explicitly which components to use when predicting.
This is done by specifying the components in the argument \code{comps}. (If
both \code{ncomp} and \code{comps} are specified, \code{comps} takes
precedence over \code{ncomp}.) For instance, to get predictions from a
model with only component 2, one can use
<<>>=
predict(gas1, comps = 2, newdata = gasTest[1:5,])
@
The results are different from the predictions with two components
(i.e., components one and two) above. (The intercept is always included in
the predictions. It can be removed by subtracting \code{mymod\$Ymeans}
from the predicted values.)
The \code{predict} method returns a three-dimensional array, in which the
entry $(i,j,k)$ is the predicted value for observation $i$, response $j$ and
model size $k$. Note that singleton dimensions are not dropped, so
predicting five observations for a uni-response model with \code{ncomp = 3}
gives an $5 \times 1 \times 1$ array, not a vector of length five. This is
to make it easier to distinguish between predictions from models with one
response and predictions with one model size. (When using the \code{comps}
argument, the last dimension is dropped, because the predictions are always
from a single model.) One can drop the singleton dimensions explicitly by
using \code{drop(predict(...))}:
<<>>=
drop(predict(gas1, ncomp = 2:3, newdata = gasTest[1:5,]))
@
Missing values in \code{newdata} are propagated to \code{NA}s in the predicted
response, by default. This can be changed with the \code{na.action}
argument. See \code{?na.omit} for details.
The \code{newdata} does not have to be a data frame. Recognising the fact
that the right hand side of PLSR and PCR formulas very often are a single
matrix term, the \code{predict} method allows one to use a matrix as
\code{newdata}, so instead of
\begin{verbatim}
newdataframe <- data.frame(X = newmatrix)
predict(..., newdata = newdataframe)
\end{verbatim}
one can simply say
\begin{verbatim}
predict(..., newdata = newmatrix)
\end{verbatim}
However, there are a couple of caveats: First, this \emph{only} works in
\code{predict}. Other functions that take a \code{newdata} argument (such
as \code{RMSEP}) must have a data frame (because they also need the response
values). Second, when \code{newdata} is a data frame, \code{predict} is
able to perform more tests on the supplied data, such as the dimensions and
types of variables. Third, with the exception of scaling (specified with
the \code{scale} argument when fitting the model), any transformations or
coding of factors and interactions have to be performed manually if
\code{newdata} is a matrix.
It is often interesting to predict scores from new observations, instead of
response values. This can be done by specifying the argument \code{type =
"scores"} in \code{predict}. One will then get a matrix with the scores
corresponding to the components specified in \code{comps} (\code{ncomp} is
accepted as a synonym for \code{comps} when predicting scores).
Predictions can be plotted with the function \code{predplot}. This function
is generic, and can also be used for plotting predictions from other types
of models, such as \code{lm}. Typically, \code{predplot} is called like this:
<>=
predplot(gas1, ncomp = 2, newdata = gasTest, asp = 1, line = TRUE)
@
%
\begin{figure}
\begin{center}
\setkeys{Gin}{width=3.5in}
<>=
par(mar = c(4, 4, 2.5, 1))
predplot(gas1, ncomp = 2, newdata = gasTest, asp = 1, line = TRUE)
@
\setkeys{Gin}{width=5in}
\caption{Test set predictions}\label{fig:testPreds}
\end{center}
\end{figure}
This plots predicted (with 2 components) versus measured response values.
(Note that \code{newdata} must be a data frame with both X and Y
variables.)
\section{Further topics}\label{sec:advanced}
%%%%%%%%%%%%%%%%
This section presents a couple of slightly technical topics for more
advanced use of the package.
\subsection{Selecting fit algorithms}\label{sec:select-fit-alg}
%%%%%%%%%%%%%%%%
There are several PLSR algorithms, and the \pkg{pls} package currently
implements three of them: the kernel algorithm for tall matrices (many
observations, few variables)~\cite{DayMacGre:ImprPlsAlg}, the classic
orthogonal scores algorithm (A.K.A.\ NIPALS algorithm)~\cite{MarNaes:MultCal}
and the SIMPLS algorithm~\cite{Jong1993a}. The kernel and orthogonal
scores algorithms produce the same results (the kernel algorithm being the
fastest of them for most problems). SIMPLS produces the same fit for
single-response models, but slightly different results for multi-response
models. It is also usually faster than the NIPALS algorithm.
The factory default is to use the kernel algorithm. One can specify a
different algorithm with the \code{method} argument; i.e., \code{method =
"oscorespls"}.
If one's personal taste of algorithms does not coincide with the defaults in
\pkg{pls}, it can be quite tedious (and error prone) having to write e.g.\
\code{method = "oscorespls"} every time (even though it can be shortened to
e.g.\ \code{me = "o"} due to partial matching). Therefore, the defaults can
be changed, with the function \code{pls.options}. Called without arguments,
it returns the current settings as a named list:
<<>>=
pls.options()
@
The options specify the default fit algorithm of \code{mvr}, \code{plsr},
and \code{pcr}. To return only a specific option, one can use
\code{pls.options("plsralg")}. To change the default PLSR algorithm for the
rest of the session, one can use, e.g.
<<>>=
pls.options(plsralg = "oscorespls")
@
Note that changes to the options only last until \proglang{R} exits. (Earlier
versions of \pkg{pls} stored the changes in the global environment so they
could be saved and restored, but current CRAN policies do not allow this.)
\subsection{Parallel cross-validation}\label{sec:parallel-cv}
%%%%%%%%%%%%%%%%
Cross-validation is a computationally demanding procedure. A new model has to
be fitted for each segment. The underlying fit functions have been optimised,
and the implementation of cross-validation that is used when specifying
the \code{validation} argument to \code{mvr} tries to avoid any unneeded
calculations (and house-keeping things like the formula handling, which can be
surprisingly expensive). Even so, cross-validation can take a long time, for
models with large matrices, many components or many segments.
By default, the cross-validation calculations in \pkg{pls} is performed
serially, on one CPU (core). (In the following, we will use `CPU' to denote
both CPUs and cores.)
Since version 2.14.0, \proglang{R} has shipped with a package \pkg{parallel}
for running calculations in parallel, on multi-CPU machines or on
several machines. The \pkg{pls} package can use the facilities of
\pkg{parallel} to run the cross-validations in parallel.
The \pkg{parallel} package has several ways of running calculations in
parallel, and not all of them are available on all systems. Therefore, the
support in \pkg{pls} is quite general, so one can select the ways that work
well on the given system.
To specify how to run calculations in parallel, one sets the option
\code{parallel} in \code{pls.options}. After setting the option, one simply
runs cross-validatons as before, and the calculations will be performed in
parallel. This works both when using the \code{crossval} function and the
\code{validation} argument to \code{mvr}. The parallel specification has
effect until it is changed.
The default value for \code{parallel} is \code{NULL}, which specifies that the
calculations are done serially, using one CPU. Specifying the value 1 has the
same effect.
Specifying an integer $> 1$ makes the calculations use the function
\code{mclapply} with the given number as the number of CPUs to use. Note:
\code{mclapply} depends on `forking' which does not exist on MS Windows, so
\code{mclapply} cannot be used there.
Example:
\begin{verbatim}
pls.options(parallel = 4) # Use mclapply with 4 CPUs
gas1.cv <- plsr(octane ~ NIR, ncomp = 10, data = gasTrain, validation = "LOO")
\end{verbatim}
The \code{parallel} option can also be specified as a cluster object created
by the \code{makeCluster} function from the package \code{parallel}.
Any following cross-validation will then be performed with the function
\code{parLapply} on that cluster. Any valid cluster specification can be
used. The user should stop the cluster with
\code{stopCluster(pls.options()\$parallel)} when it is no longer needed.
\begin{verbatim}
library(parallel) # Needed for the makeCluster call
pls.options(parallel = makeCluster(4, type = "PSOCK")) # PSOCK cluster, 4 CPUs
gas1.cv <- plsr(octane ~ NIR, ncomp = 10, data = gasTrain, validation = "LOO")
## later:
stopCluster(pls.options()$parallel)
\end{verbatim}
Several types of clusters are available: FORK uses forking, so starting the
cluster is very quick, however it is not available on MS Windows. PSOCK
starts \proglang{R} processes with the \code{Rscript} command, which is
slower, but is supported on MS Windows. It can also start worker processes on
different machines (see ?makeCluster for how). MPI uses MPI to start and
communicate with processes. This is the most flexible, but is often slower to
start up than the other types. It also dependens on the packages \pkg{snow}
and \pkg{Rmpi} to be installed and working. It is especially useful when
running batch jobs on a computing cluster, because MPI can interact with the
queue system on the cluster to find out which machines to use when the job
starts.
Here is an example of running a batch job on a cluster using MPI:
R script (myscript.R):
\begin{verbatim}
library(parallel) # for the makeCluster call
pls.options(parallel = makeCluster(16, type = "MPI") # MPI cluster, 16 CPUs
gas1.cv <- plsr(octane ~ NIR, ncomp = 10, data = gasTrain, validation = "LOO")
## later:
save.image(file = "results.RData")
stopCluster(pls.options()$parallel)
mpi.exit() # stop Rmpi
\end{verbatim}
To run the job:
\begin{verbatim}
mpirun -np 1 R --slave --file=myscript.R
\end{verbatim}
The details of how to run \code{mpirun} varies between the different MPI
implementations and how they interact with the queue system used (if any).
The above should work for OpenMPI or Intel MPI running under the Slurm queue
system. In other situations, one might have to specify which machines to use
with, e.g., the \code{-host} or \code{-machinefile} switch.
\subsection{Package design}\label{sec:package-design}
%%%%%%%%%%%%%%%%
The \pkg{pls} package is designed such that an interface function \code{mvr}
handles the formula and data, and calls an underlying fit function (and
possibly a cross-validation function) to do the real work. There are
several reasons for this design: it makes it easier to implement new
algorithms, one can easily skip the time-consuming formula and data handling
in computing-intensive applications (simulations, etc.), and it makes it
easier to use the \code{pls} package as a building block in other packages.
The plotting facilities are implemented similarly: the \code{plot} method
simply calls the correct plot function based on the \code{plottype}
argument. Here, however, the separate plot functions are meant to be
callable interactively, because some people like to use the generic
\code{plot} function, while others like to use separate functions for each
plot type. There are also \code{plot} methods for some of the components of
fitted models that can be extracted with extract functions, like score and
loading matrices. Thus there are several ways to get some plots, e.g.:
\begin{verbatim}
plot(mymod, plottype = "scores", ...)
scoreplot(mymod, ...)
plot(scores(mymod), ...)
\end{verbatim}
One example of a package that uses \pkg{pls} is \pkg{lspls}, available on
CRAN. In that package LS is combined with PLS in a regression procedure.
It calls the fit functions of \pkg{pls} directly, and also uses the plot
functions to construct score and loading plots. There is also the
\code{plsgenomics} package, which includes a modified version of (an earlier
version of) the SIMPLS fit function \code{simpls.fit}.
\subsection{Calling fit functions directly}\label{sec:call-fit-func}
%%%%%%%%%%%%%%%%
The underlying fit functions are called \code{kernelpls.fit},
\code{oscorespls.fit}, and \code{simpls.fit} for the PLSR methods, and
\code{svdpc.fit} for the PCR method. They all take arguments \code{X},
\code{Y}, \code{ncomp}, and \code{stripped}. Arguments \code{X}, \code{Y},
and \code{ncomp} specify $\bX$ and $\bY$ (as matrices, not data
frames), and the number of components to fit, respectively. The argument
\code{stripped} defaults to \code{FALSE}. When it is \code{TRUE}, the
calculations are stripped down to the bare minimum required for returning
the $\bX$ means, $\bY$ means, and the regression coefficients. This is used
to speed up cross-validation procedures.
The fit functions can be called directly, for instance when one wants to
avoid the overhead of formula and data handling in repeated fits. As an
example, this is how a simple leave-one-out cross-validation for a
uni-response-model could be implemented, using the SIMPLS:
<<>>=
X <- gasTrain$NIR
Y <- gasTrain$octane
ncomp <- 5
cvPreds <- matrix(nrow = nrow(X), ncol = ncomp)
for (i in 1:nrow(X)) {
fit <- simpls.fit(X[-i,], Y[-i], ncomp = ncomp, stripped = TRUE)
cvPreds[i,] <- (X[i,] - fit$Xmeans) %*% drop(fit$coefficients) +
fit$Ymeans
}
@
The RMSEP of the cross-validated predictions are
<<>>=
sqrt(colMeans((cvPreds - Y)^2))
@
which can be seen to be the same as the (unadjusted) CV results for the
\code{gas1} model in Section~\ref{sec:example-session}.
\subsection{Formula handling in more detail}\label{sec:formula-handling}
%%%%%%%%%%%%%%%%
The handling of formulas and variables in the model fitting is very similar
to what happens in the function \code{lm}: The variables specified in the
formula are looked up in the data frame given in the \code{data} argument of
the fit function (\code{plsr}, \code{pcr} or \code{mvr}), or in the calling
environment if not found in the data frame. Factors are coded into one or
more of columns, depending on the number of levels, and on the contrasts
option. All (possibly coded) variables are then collected in a numerical
model matrix. This matrix is then handed to the underlying fit or
cross-validation functions. A similar handling is used in the \code{predict}
method.
The intercept is treated specially in \pkg{pls}. After the model matrix has
been constructed, the intercept column is removed. This ensures that any
factors are coded as if the intercept was present. The underlying fit
functions then center the rest of the variables as a part of the fitting
process. (This is intrinsic to the PLSR and PCR algorithms.) The intercept
is handled separately. A consequence of this is that explicitly specifying
formulas without the intercept (e.g., \code{y \textasciitilde\ a + b - 1})
will only result in the coding of any factors to change; the intercept will
still be fitted.
%%%%%%%%%%%%%%%%
\bibliographystyle{plain}
\bibliography{pls-manual}
%%%%%%%%%%%%%%%%
\end{document}