我正在尝试对齐表格中的右箭头。有人能告诉我怎么做吗?最好保留表格的结构。(见下面的示例)
\documentclass[a4paper, 12pt]{article}
\usepackage{threeparttable}
\usepackage{longtable, booktabs, tabularx}
\begin{document}
\begin{table}[h]
\centering
\caption{Transfer Entropy Results}
\label{tab1:correlation}
\begin{threeparttable}
\begin{tabular*}{\textwidth}{l@{\extracolsep{\fill}}*{5}{c}}
\toprule
\multicolumn{1}{l}{Direction} & \multicolumn{1}{c}{TE} & \multicolumn{1}{c}{ETE} & \multicolumn{1}{c}{STD} & \multicolumn{1}{c}{P-value} \\
\midrule
Bitcoin $\rightarrow$ IPImicro & 0.015 & 0.000 & 0.023 & 0.473 \\
\addlinespace
IPImicro $\rightarrow$ Bitcoin & 0.091 & 0.066 & 0.025 & 0.027** \\
\addlinespace
Bitcoin $\rightarrow$ IPImacro & 0.007 & 0.000 & 0.028 & 0.650 \\
\addlinespace
IPImacro $\rightarrow$ Bitcoin & 0.073 & 0.044 & 0.027 & 0.066** \\
\bottomrule
\end{tabular*}
\begin{tablenotes}[para,flushleft]
\footnotesize
\item\hspace{-2.5pt}\noindent\textit{Note:} This table presents the Transfer Entropy estimation results. H0: No information flow. Statistical significance is based on a bootstrapped Markov chain of the transfer entropy estimates with 300 bootstrap replications. Note that the sign and the numerical value of the transfer Entropy cannot be compared, i.e. determining the magnitude and dominant direction of the information flow is not possible (\cite{behrendt2019rtransferentropy}). We also test a vector autoregressive (VAR) model and test for Granger causality (Table \ref{tab1:var} and \ref{tab1:granger} in the Appendix). However, because it is limited to linear relationships, the VAR model could not reveal any relationship between IPImicro, IPImacro and Bitcoin. Standard deviation in parentheses; *** p < 0.01; ** p < 0.05; * p < 0.10.
\end{tablenotes}
\end{threeparttable}
\end{table}
\end{document}
答案1
我建议将箭头前面的内容放在 – 中,并通过和\eqmakebox
添加各种改进:siunitx
caption
\documentclass{article}
\usepackage{array, threeparttable, booktabs}
\usepackage{eqparbox, siunitx}
\usepackage[skip =6pt]{caption}
\begin{document}
\begin{table}[h]
\centering
\sisetup{table-format=1.3, table-number-alignment=center, table-space-text-post=**, table-align-text-post=false}
\begin{threeparttable}
\caption{Transfer Entropy Results}
\label{tab1:correlation}
\begin{tabular*}{\textwidth}{@{}l@{\extracolsep{\fill}}*{4}{S}}
\toprule
Direction & {TE} & {ETE} & {STD} & {P-value} \\
\midrule
\eqmakebox[D][l]{Bitcoin} $\rightarrow$ IPImicro & 0.015 & 0.000 & 0.023 & 0.473 \\
\addlinespace
\eqmakebox[D][l]{IPImicro} $\rightarrow$ Bitcoin & 0.091 & 0.066 & 0.025 & 0.027** \\
\addlinespace
\eqmakebox[D][l]{Bitcoin} $\rightarrow$ IPImacro & 0.007 & 0.000 & 0.028 & 0.650 \\
\addlinespace
\eqmakebox[D][l]{IPImacro} $\rightarrow$ Bitcoin & 0.073 & 0.044 & 0.027 & 0.066** \\
\bottomrule
\end{tabular*}
\begin{tablenotes}[para,flushleft]
\footnotesize\smallskip
\item\hspace{-2.5pt}\noindent\textit{Note:} This table presents the Transfer Entropy estimation results. H0: No information flow. Statistical significance is based on a bootstrapped Markov chain of the transfer entropy estimates with 300 bootstrap replications. Note that the sign and the numerical value of the transfer Entropy cannot be compared, i.e. determining the magnitude and dominant direction of the information flow is not possible (\cite{behrendt2019rtransferentropy}). We also test a vector autoregressive (VAR) model and test for Granger causality (Table \ref{tab1:var} and \ref{tab1:granger} in the Appendix). However, because it is limited to linear relationships, the VAR model could not reveal any relationship between IPImicro, IPImacro and Bitcoin. Standard deviation in parentheses; *** $ p < 0.01 $; ** $p < 0.05 $; * $ p < 0.10 $.
\end{tablenotes}
\end{threeparttable}
\end{table}
\end{document}
答案2
再举一个例子,使用siunitx
和threeparttablex
包:
\documentclass{article}
\usepackage{booktabs}
\usepackage[referable]{threeparttablex}
\usepackage{siunitx}
\usepackage[skip =6pt]{caption}
\begin{document}
\begin{table}[ht]
\centering
\sisetup{table-format=1.3,
table-space-text-post=**}
\setlength\tabcolsep{0pt}
\begin{threeparttable}
\caption{Transfer Entropy Results}
\label{tab1:correlation}
\begin{tabular*}{\linewidth}{l>{\ $\rightarrow$\ }l
@{\extracolsep{\fill}} *{4}{S}}
\toprule
\multicolumn{2}{c}{Direction} & {TE} & {ETE} & {STD} & {P-value} \\
\midrule
Bitcoin & IPImicro & 0.015 & 0.000 & 0.023 & 0.473 \\
\addlinespace
IPImicro & Bitcoin & 0.091 & 0.066 & 0.025 & 0.027** \\
\addlinespace
Bitcoin & IPImacro & 0.007 & 0.000 & 0.028 & 0.650 \\
\addlinespace
IPImacro & Bitcoin & 0.073 & 0.044 & 0.027 & 0.066** \\
\bottomrule
\end{tabular*}
\begin{tablenotes}[para,flushleft]\footnotesize\smallskip
\note This table presents the Transfer Entropy estimation results. H0: No information flow. Statistical significance is based on a bootstrapped Markov chain of the transfer entropy estimates with 300 bootstrap replications. Note that the sign and the numerical value of the transfer Entropy cannot be compared, i.e. determining the magnitude and dominant direction of the information flow is not possible (\cite{behrendt2019rtransferentropy}). We also test a vector autoregressive (VAR) model and test for Granger causality (Table \ref{tab1:var} and \ref{tab1:granger} in the Appendix). However, because it is limited to linear relationships, the VAR model could not reveal any relationship between IPImicro, IPImacro and Bitcoin.
\item[***] $p < 0.01 $;
\item[**] $p < 0.05 $;
\item[*] $p < 0.10 $.
\end{tablenotes}
\end{threeparttable}
\end{table}
\end{document}
答案3
一些建议和意见:
正如@daleif 在评论中建议的那样,为符号设置一个专用列
\rightarrow
。您的代码有太多
\multicolumn
包装器;请毫不留情地剔除它们。由于环境的宽度设置为,因此不需要语句
\centering
前面的指令。\caption
tabular*
\textwidth
环境
tabular*
有 4 个数据列,而不是 5 个。考虑将数据列中的数字左对齐而不是居中。
语句
\caption
应该在环境内部,而不是threeparttable
环境外部。(环境的三个正式部分threepartable
是标题、表格环境和tablenotes
环境。)由于您的代码中
threeparttable
没有指令,因此似乎没有必要使用机器。\tnote
可选:从图例中删除“括号中的标准差”这句话,因为括号中没有材料。
\documentclass[a4paper, 12pt]{article}
\usepackage[T1]{fontenc}
%\usepackage{threeparttable}
\usepackage{%longtable,
booktabs, %tabularx
array}
\newcolumntype{C}{>{${}}c<{{}$}} % for math symbols such as "\to"
\begin{document}
\begin{table}[h]
\setlength\tabcolsep{0pt}
%\begin{threeparttable}
%%\centering % is redundant
\caption{Transfer Entropy Results}
\label{tab1:correlation}
\begin{tabular*}{\textwidth}{ lCl @{\extracolsep{\fill}} *{4}{l}}
\toprule
\multicolumn{3}{l}{Direction} & TE & ETE & STD & P-value \\
\midrule
Bitcoin &\to& IPImicro & 0.015 & 0.000 & 0.023 & 0.473 \\
\addlinespace
IPImicro &\to& Bitcoin & 0.091 & 0.066 & 0.025 & 0.027** \\
\addlinespace
Bitcoin &\to& IPImacro & 0.007 & 0.000 & 0.028 & 0.650 \\
\addlinespace
IPImacro &\to& Bitcoin & 0.073 & 0.044 & 0.027 & 0.066** \\
\bottomrule
\end{tabular*}
%\begin{tablenotes}[para,flushleft]
\medskip
\footnotesize
\textit{Note:} This table presents the Transfer Entropy estimation results. H0: No information flow. Statistical significance is based on a bootstrapped Markov chain of the transfer entropy estimates with 300 bootstrap replications. Note that the sign and the numerical value of the transfer Entropy cannot be compared, i.e. determining the magnitude and dominant direction of the information flow is not possible (\cite{behrendt2019rtransferentropy}). We also estimate a vector autoregressive (VAR) model and test for Granger causality (Table \ref{tab1:var} and \ref{tab1:granger} in the Appendix). However, because it is limited to linear relationships, the VAR model could not reveal any relationship between IPImicro, IPImacro and Bitcoin. Standard deviations in parentheses; *** p < 0.01; ** p < 0.05; * p < 0.10.
% \end{tablenotes}
% \end{threeparttable}
\end{table}
\end{document}