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我的代码有问题:(
我听说 {cases} 环境不能在数学模式下使用,
所以我改为$$
,\(\)
但仍然出现各种错误。
\begin{frame}{ZIP regression models with covariates}
\begin{itemize}
\item Regression-type models can adjust for covariate effects and assess relationships between key predictors and the response
\item Covariates enter ZIP regression model at both the Bernoulli zero-inflation and Poison count stages\\
$\rar$ 2 sets of parameters corresponding to p and $\lambda$\\
\(\begin{cases}
\(\lambda\) : loglinear model \(\rar log(\frac{p}{1-p}) = \textbf{X_1\alpha} = \alpha_0 + \alpha_1X_{11} + \alpha_2X_{12} + \dots + \alpha_mX_{1m}\)\\
p : logit model \(\rar log(\lambda) = \textbf{X_2\beta} = \beta_0 + \beta_1X_{21} + \beta_2X_{22} + \dots + \beta_lX_{2l}\)
\end{cases}\)
\vspace{0.2cm}
\item \(\boldsymbol{X_1} = (1, X_{11}, X_{12}, \dots, X_{1m})\) : covariate vector included in the zero stage\\
\(\boldsymbol{X_2} = (1, X_{21}, X_{22}, \dots, X_{2l})\) : covariate vector included in the Poisson stage
\item \(\boldsymbol{\alpha} = (\alpha_0, \alpha_1, \dots, \alpha_m)^{T}, \boldsymbol{\beta} = (\beta_0, \beta_1, \dots, \beta_l)^{T}\)\\
$\rar$ corresponding coefficient vectors
\end{itemize}
\end{frame}
我不知道哪里出了问题...请帮帮我!
答案1
首先,和$...$
都是\(...\)
内联数学。
就您的案例环境而言,以下内容对我有用(如评论中所述,案例环境保留数学模式):
\documentclass{beamer}
\usepackage{amsmath}
\begin{document}
\begin{frame}{hallo}
\begin{itemize}
\item Regression-type models can adjust for covariate effects and assess relationships between key predictors and the response
\item Covariates enter ZIP regression model at both the Bernoulli zero-inflation and Poison count stages\\
2 sets of parameters corresponding to p and $\lambda$\\
\(
\begin{cases}
\lambda\text{:} & \text{loglinear model ...} \\
p\text{:} & \text{logit model ...}
\end{cases}
\)
\end{itemize}
\end{frame}
\end{document}
但是,更改此设置后,您的代码仍然无法编译。然后我将 `\textbf{X_1 \alpha}` 替换为 `\bm{X_1 \alpha}`(并包含 bm 包),从而修复了此问题。您的完整编译示例将变为:
\documentclass{beamer}
\usepackage{amsmath}
\usepackage{bm}
\newcommand{\rar}{\alpha}
\begin{document}
\begin{frame}{hallo}
\begin{itemize}
\item Regression-type models can adjust for covariate effects and assess relationships between key predictors and the response
\item Covariates enter ZIP regression model at both the Bernoulli zero-inflation and Poison count stages\\
$\rar$ 2 sets of parameters corresponding to p and $\lambda$\\
\(
\begin{cases}
\lambda\text{:} & \text{loglinear model } \rar \log(\frac{p}{1-p}) = \bm{X_1\alpha} = \alpha_0 + \alpha_1X_{11} + \alpha_2X_{12} + \dots + \alpha_mX_{1m} \\
p\text{:} & \text{logit model } \rar \log(\lambda) = \bm{X_2\beta} = \beta_0 + \beta_1X_{21} + \beta_2X_{22} + \dots + \beta_lX_{2l}
\end{cases}
\)
\item \(\boldsymbol{X_1} = (1, X_{11}, X_{12}, \dots, X_{1m})\) : covariate vector included in the zero stage\\
\(\boldsymbol{X_2} = (1, X_{21}, X_{22}, \dots, X_{2l})\) : covariate vector included in the Poisson stage
\item \(\boldsymbol{\alpha} = (\alpha_0, \alpha_1, \dots, \alpha_m)^{T}, \boldsymbol{\beta} = (\beta_0, \beta_1, \dots, \beta_l)^{T}\)\\
$\rar$ corresponding coefficient vectors
\end{itemize}
\end{frame}
\end{document}
但请注意,您的案例环境中的数学并不适合幻灯片。