Ich verwende das Paket „Glossare“ in LaTeX. In der Präambel habe ich
\newglossaryentry{error}
{
name = error,
description = {the difference between the actual value and the predicted value}
}
Und im Text habe ich
und $e$ ist ein $nx 1$ Vektor von Fehlern
Ich hätte gerne einen Glossareintrag zum Thema „Fehler“ (im Singular).
Wenn ich \gls{errors} verwende, wird ganz sinnvollerweise angezeigt, dass kein Eintrag vorhanden ist. Wenn ich \gls{error}s verwende, wird kein Glossareintrag angezeigt.
Wie kann ich tun, was ich will?
Hier ist ein MWE (es funktioniert aufgrund der oben beschriebenen Probleme nicht).
\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}