%\VignetteIndexEntry{affyPLM: Fitting Probe Level Models}
%\VignettePackage{affyPLM}

\documentclass[12pt]{article}
\usepackage{amsmath,pstricks}
\usepackage{hyperref}
\usepackage[authoryear,round]{natbib}

\textwidth=6.2in
\textheight=8.5in
%\parskip=.3cm
\oddsidemargin=.1in
\evensidemargin=.1in
\headheight=-.3in

\newcommand{\scscst}{\scriptscriptstyle}
\newcommand{\scst}{\scriptstyle}
\author{Ben Bolstad \\ {\tt bolstad@stat.berkeley.edu}}
\usepackage{/homes/madman/R-1.9.0/share/texmf/Sweave}
\begin{document}
\title{affyPLM: Methods for fitting probe level models to Affy data}
\maketitle
\tableofcontents
\section{Introduction}

This document describes an R \cite{ihak:gent:1996} package that has been written to fit robust probe level models to affy data.

After starting R, the package should be loaded using
this will load {\it affyPLM} as well as the affy package and its dependencies.

\section{Fitting Probe level models}

\subsection{What is a probelevel model and what is a PLMset?}

A probelevel model is a linear model fit to probe data. In particular it is where we fit a model with probe level and chip level parameters for each probeset. It is easy to arrange the data for a probeset so that the rows are probes and the columns are chips. In this case our probe level parameters are usually a factor variable for each probe. The chip level parameters could be, a factor with a level for each array, factor variables grouping the chips into treatment groups or perhaps some sort of covariate (pH, temperature). 

A \verb+PLMset+ is an R object that holds the results of a fitted probelevel model. Some of the items that are kept are parameter estimates, their standard errors and robust fit weights.

\subsection{Using the software to fit probelevel models}

\subsubsection{Some examples}

The main function for fitting Probe level models is the function \verb+fitPLM+. The most basic way to call the function is to call passing an \verb+AffyBatch+ objects, this will fit a linear model with an effect estimated for each chip.
\begin{Schunk}
\begin{Sinput}
> data(Dilution)
> Pset <- fitPLM(Dilution)