LaTeX에서 패키지 용어집을 사용하고 있습니다. 내가 가지고 있는 서문에는
\newglossaryentry{error}
{
name = error,
description = {the difference between the actual value and the predicted value}
}
그리고 내가 가지고 있는 텍스트에는
$e$는 $nx 1$ 오류 벡터입니다.
오류(단수형)에 대한 용어집 항목을 갖고 싶습니다.
\gls{errors}를 사용하면 항목이 없다는 것이 꽤 합리적으로 표시됩니다. \gls{error}s를 사용하면 용어집 항목이 나타나지 않습니다.
내가 원하는 것을 어떻게 할 수 있나요?
다음은 MWE입니다(위에서 설명한 문제로 인해 작동하지 않습니다).
\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}