私はLaTeXの用語集パッケージを使用しています。
\newglossaryentry{error}
{
name = error,
description = {the difference between the actual value and the predicted value}
}
そして私が書いた本文には
そして$e$は$nx1$の誤差ベクトルである
「error（単数形）」の用語集項目を追加したいと思います。
\gls{errors} を使用すると、エントリがないことが明確に示されます。\gls{error}s を使用すると、用語集エントリは表示されません。
どうすれば自分のやりたいことができるのでしょうか？
ここに MWE があります (上記の問題のため動作しません)。
\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}