我正在使用 widetext.sty 将方程式扩展到两列。输出结果非常完美
我想使用 lineno.sty 添加行号，如果这样做，行号将与内边距合并（见下面的屏幕截图）。
有人能指导我如何解决这个问题吗？
我的代码是：
\documentclass[twocolumn]{article}
\usepackage{amsmath,multicol}
\usepackage[switch,right]{lineno}
\linenumbers
\usepackage{widetext}
\begin{document}
Line 1 In practice, we often have little prior information
implying that the models for regressing the response
variable on the covariates are linear or any other
parametric family. Compared with a parametric model, more
flexibility is possible by using a nonparametric model,
such as the additive model.
Line 2 In practice, we often have little prior information
implying that the models for regressing the response
variable on the covariates are linear or any other
parametric family. Compared with a parametric model, more
flexibility is possible by using a nonparametric model,
such as the additive model.
Line 3 In practice, we often have little prior information
implying that the models for regressing the response
variable on the covariates are linear or any other
parametric family. Compared with a parametric model, more
flexibility is possible by using a nonparametric model,
such as the additive model.\par
\begin{widetext}
\begin{equation}
\mathcal{R}^{(\text{d})}=
g_{\sigma_2}^e
\left(
\frac{[\Gamma^Z(3,21)]_{\sigma_1}}{Q_{12}^2-M_W^2}
+\frac{[\Gamma^Z(13,2)]_{\sigma_1}}{Q_{13}^2-M_W^2}
\right)
+ x_WQ_e
\left(
\frac{[\Gamma^\gamma(3,21)]_{\sigma_1}}{Q_{12}^2-M_W^2}
+\frac{[\Gamma^\gamma(13,2)]_{\sigma_1}}{Q_{13}^2-M_W^2}
\right)\;. \label{eq:wideeq}
\end{equation}
\end{widetext}
Line 4 In practice, we often have little prior information
implying that the models for regressing the response
variable on the covariates are linear or any other
parametric family. Compared with a parametric model, more
flexibility is possible by using a nonparametric model,
such as the additive model.
Line 5 In practice, we often have little prior information
implying that the models for regressing the response
variable on the covariates are linear or any other
parametric family. Compared with a parametric model, more
flexibility is possible by using a nonparametric model,
such as the additive model.
Line 6 In practice, we often have little prior information
implying that the models for regressing the response
variable on the covariates are linear or any other
parametric family. Compared with a parametric model, more
flexibility is possible by using a nonparametric model,
such as the additive model.
Line 7 In practice, we often have little prior information
implying that the models for regressing the response
variable on the covariates are linear or any other
parametric family. Compared with a parametric model, more
flexibility is possible by using a nonparametric model,
such as the additive model.
Line 8 In practice, we often have little prior information
implying that the models for regressing the response
variable on the covariates are linear or any other
parametric family. Compared with a parametric model, more
flexibility is possible by using a nonparametric model,
such as the additive model.
Line 9 In practice, we often have little prior information
implying that the models for regressing the response
variable on the covariates are linear or any other
parametric family. Compared with a parametric model, more
flexibility is possible by using a nonparametric model,
such as the additive model.
Line 10 In practice, we often have little prior information
implying that the models for regressing the response
variable on the covariates are linear or any other
parametric family. Compared with a parametric model, more
flexibility is possible by using a nonparametric model,
such as the additive model.
Line 11 In practice, we often have little prior information
implying that the models for regressing the response
variable on the covariates are linear or any other
parametric family. Compared with a parametric model, more
flexibility is possible by using a nonparametric model,
such as the additive model.
\clearpage
\end{document}
谢谢
S. Vinayagamurthy