Estou usando os glossários de pacotes em LaTeX. No preâmbulo eu tenho
\newglossaryentry{error}
{
name = error,
description = {the difference between the actual value and the predicted value}
}
E no texto eu tenho
e $e$ é um vetor de erros $nx 1$
Gostaria de ter um item de glossário para erro (no singular).
Se eu usar \gls{errors} isso diz sensatamente que não tem entrada. Se eu usar \gls{error}s então nenhuma entrada no glossário aparecerá.
Como posso fazer o que quero?
Aqui está um MWE (não funciona devido aos problemas descritos acima).
\documentclass{book}
\usepackage{fancyvrb}%Verbatim
\usepackage[acronym]{glossaries}
\usepackage{natbib}
\usepackage{latexsym}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage[dvipdf]{graphicx}
\usepackage{mathptmx}
\usepackage{alltt}
\usepackage{color}
\usepackage{float}
\usepackage{fancyhdr}
\pagestyle{fancy}
\fancyhf{}
\fancyhead[LE,LO]{\thechapter}
\fancyhead[RE,RO]{\thesection}
\fancyfoot[CE,CO]{\thepage}
\pagestyle{plain}
\title{The General Linear Model: Assumptions, violations and remedies or What to do when your dependent variable won't behave}
\author{Peter Flom}
\makeglossaries
\newglossaryentry{error}
{
name = error,
description = {the difference between the actual value and the predicted value}
}
\begin{document}
\maketitle
\addcontentsline{toc}{chapter}{Contents}
\pagenumbering{roman}
\tableofcontents
\listoffigures
\listoftables
\chapter*{Preface}\normalsize
\addcontentsline{toc}{chapter}{Preface}
\pagestyle{plain}
This is a book about regression.
\pagestyle{fancy}
\pagenumbering{arabic}
\chapter{Introduction: The General Linear Model and its Assumptions}
\section{The model}
The general linear model (GLM) subsumes linear regression and ANOVA (these models are equivalent, if you do not know why, see Appendix A; in this book I will use the regression framework). It is one of the most commonly used statistical methods, used in thousands of papers and analyses in every field of science and business. The idea is that we have one dependent (or target, or outcome) variable that we want to model as a linear function of one or more independent variables. The dependent variable (DV) must be continuous. The independent variables (IV) can be categorical or continuous. The model can be written:
\[
Y = b_0 + b_1x_1 + b_2x_2 + \dots b_px_p + e
\]
where there are p independent variables.
In matrix terms (for all the matrix knowledge you will need in this book see appendix B)
\[
Y = XB + e
\]
where $Y$ is an $n x 1$ vector of dependent variable, $X$ is an $n x p$ matrix of independent variables, $B$ is a $p x 1$ vector of parameters to be estimated and $e$ is an $n x 1$ vector of \gls{errors}.
\chapter{Glossary}
\clearpage
\printglossary[type=\acronymtype]
\printglossary
\end{document}