具有多行的 Booktabs 表：垂直规则的替代？

Question

这是呈现信息的一种相当不同的方式，可能合适也可能不合适。与修改后的表格不同，这种方式适合文本区域的范围。（另一种方式会产生过满的框。）

这本质上是一forest棵树，也是justtrees的实验性包装器forest。这可能使其无法用于提交目的，但您可以根据需要包含代码，然后forest直接加载。某处有的副本justtrees.sty，但如果您真的想尝试一下，请向我索要 0.04 版本。

\documentclass[11pt]{article}
\usepackage[margin=0.75in]{geometry}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{rotating,lmodern,justtrees}% version 0.04 of justtrees
\usetikzlibrary{calc,decorations.pathreplacing}

\begin{document}
\thispagestyle{empty}
\begin{sidewaysfigure}
  \caption{\textbf{Summary of the modeling approaches included in the evaluation}}\label{fig2}% it would be better to use \captionsetup to format captions globally
  \begin{center}
    \begin{justtree}{
        right justifiers,
        for tree={
          edge path={
            \noexpand\path [\forestoption{edge}] (!u.parent anchor) -- +(0,-10pt) -| (.child anchor)\forestoption{edge label};
          },
          just format={font=\bfseries},
          l sep+=5pt,
          s sep+=5pt,
          if n children=3{
            calign child=2,
            calign=child
          }{},
          if level=3{font=\itshape}{},
          if level=5{math content}{},
        },
      }
      [Modelling Approaches,
        [{Frequentist ($l_{1}, l_{2}$ penalties)}, just=Paradigm
          [{quadrupen, glmnet}, just=R Package
            [None, just=Model Space Construction
              [\lambda_{ENC}
                [ENC,
                tikz={
                  \draw (.south) |- ($(.south)!1/2!(iv2.south) - (10pt,35pt)$) coordinate (a) -- ++(0,-15pt) coordinate (b) ++(10pt,0) node (iv) [anchor=north] {Influential variables};
                  \draw (!>.south) |- ([xshift=10pt, yshift=10pt]a) -- ([xshift=10pt]b);
                  \draw (iv2.south) |- ([xshift=20pt]a) -- ([xshift=20pt]b);
                  \node [anchor=mid west, justifier format] at (right just 2.mid west |- iv.mid) {Output};
                }
                ]
              ]
            ]
            [Subsampling
              [\lambda_{MB}
                [PS
                ]
              ]
              [\lambda_{ENC}
                [LS,
                tikz={
                  \draw (.south) |- ($(.south)!1/2!(ip2.south) - (25pt,25pt)$) coordinate (c) -- ++(0,-25pt) coordinate (d) ++(25pt,0) node (ip) [anchor=north] {Inclusion probabilities};
                  \draw (!>.south) |- ([xshift=10pt, yshift=10pt]c) -- ([xshift=10pt]d);
                  \draw (!>>>.south) |- ([xshift=20pt, yshift=20pt]c) -- ([xshift=20pt]d);
                  \draw (!>>>>.south) |- ([xshift=30pt, yshift=10pt]c) -- ([xshift=30pt]d);
                  \draw (!>>>>>.south) |- ([xshift=40pt]c) -- ([xshift=40pt]d);
                  \draw (ip2.south) |- ([xshift=50pt,yshift=-10pt]c) -- ([xshift=50pt]d);
                }
                ]
              ]
              [\Lambda
                [SS
                ]
              ]
            ]
            [Resampling
              [\lambda_{MB}
                [PR, name=iv2
                ]
              ]
              [\lambda_{ENC}
                [LR
                ]
              ]
              [\Lambda
                [SR
                ]
              ]
            ]
          ]
        ]
        [Bayesian (Spike \& slab prior)
          [BoomSpikeSlab
            [MCMC
              [{EMS=1}
                [BMA, just=Model
                ]
              ]
              [EMS_{CV}, just=Tuning parameter
                [BMAC, name=ip2,
                tikz={
                  \draw [decorate, decoration={brace, amplitude=5pt}, thick]  (right just 4.north east) +(5pt,0) coordinate (e) -- (e |- right just 5.south) node [midway, right, xshift=5pt, justifier format, align=left] {Ensemble\\Characteristics};
                }
                ]
              ]
            ]
          ]
        ]
      ]
    \end{justtree}
  \end{center}
  \footnotesize% note that this is a switch - it does not take an argument
  \textbf{ENC:} The baseline penalized regression model. Elastic net with $\lambda_{optimal} =\lambda_{ENC}$ derived from cross-validation (CV), \textbf{Ensembles based on 100 subsamples:} \textbf{PS:} Meinshausen \& B{\"u}hlmann's algorithm with a single $\lambda_{optimal} = \lambda_{MB}$ selected to minimize the expected number of false positives, \textbf{LS:} Single $\lambda_{optimal} = \lambda_{ENC}$ with no variable selection, \textbf{SS:} Stability selection across the entire 100 $\lambda \in \Lambda$ grid with no variable selection, \textbf{Ensembles based on 100 resamples: }\textbf{PR, LR, SR:} Identical to PS, PR and LR, respectively, with model space constructed through resampling. \textbf{BMA:} Bayesian model averaging with expected model size ($EMS$) = 1, \textbf{BMAC:} BMA with EMS determined by CV ($EMS_{CV}$).
\end{sidewaysfigure}

\end{document}

Answer 1

这是呈现信息的一种相当不同的方式，可能合适也可能不合适。与修改后的表格不同，这种方式适合文本区域的范围。（另一种方式会产生过满的框。）

这本质上是一forest棵树，也是justtrees的实验性包装器forest。这可能使其无法用于提交目的，但您可以根据需要包含代码，然后forest直接加载。某处有的副本justtrees.sty，但如果您真的想尝试一下，请向我索要 0.04 版本。

\documentclass[11pt]{article}
\usepackage[margin=0.75in]{geometry}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{rotating,lmodern,justtrees}% version 0.04 of justtrees
\usetikzlibrary{calc,decorations.pathreplacing}

\begin{document}
\thispagestyle{empty}
\begin{sidewaysfigure}
  \caption{\textbf{Summary of the modeling approaches included in the evaluation}}\label{fig2}% it would be better to use \captionsetup to format captions globally
  \begin{center}
    \begin{justtree}{
        right justifiers,
        for tree={
          edge path={
            \noexpand\path [\forestoption{edge}] (!u.parent anchor) -- +(0,-10pt) -| (.child anchor)\forestoption{edge label};
          },
          just format={font=\bfseries},
          l sep+=5pt,
          s sep+=5pt,
          if n children=3{
            calign child=2,
            calign=child
          }{},
          if level=3{font=\itshape}{},
          if level=5{math content}{},
        },
      }
      [Modelling Approaches,
        [{Frequentist ($l_{1}, l_{2}$ penalties)}, just=Paradigm
          [{quadrupen, glmnet}, just=R Package
            [None, just=Model Space Construction
              [\lambda_{ENC}
                [ENC,
                tikz={
                  \draw (.south) |- ($(.south)!1/2!(iv2.south) - (10pt,35pt)$) coordinate (a) -- ++(0,-15pt) coordinate (b) ++(10pt,0) node (iv) [anchor=north] {Influential variables};
                  \draw (!>.south) |- ([xshift=10pt, yshift=10pt]a) -- ([xshift=10pt]b);
                  \draw (iv2.south) |- ([xshift=20pt]a) -- ([xshift=20pt]b);
                  \node [anchor=mid west, justifier format] at (right just 2.mid west |- iv.mid) {Output};
                }
                ]
              ]
            ]
            [Subsampling
              [\lambda_{MB}
                [PS
                ]
              ]
              [\lambda_{ENC}
                [LS,
                tikz={
                  \draw (.south) |- ($(.south)!1/2!(ip2.south) - (25pt,25pt)$) coordinate (c) -- ++(0,-25pt) coordinate (d) ++(25pt,0) node (ip) [anchor=north] {Inclusion probabilities};
                  \draw (!>.south) |- ([xshift=10pt, yshift=10pt]c) -- ([xshift=10pt]d);
                  \draw (!>>>.south) |- ([xshift=20pt, yshift=20pt]c) -- ([xshift=20pt]d);
                  \draw (!>>>>.south) |- ([xshift=30pt, yshift=10pt]c) -- ([xshift=30pt]d);
                  \draw (!>>>>>.south) |- ([xshift=40pt]c) -- ([xshift=40pt]d);
                  \draw (ip2.south) |- ([xshift=50pt,yshift=-10pt]c) -- ([xshift=50pt]d);
                }
                ]
              ]
              [\Lambda
                [SS
                ]
              ]
            ]
            [Resampling
              [\lambda_{MB}
                [PR, name=iv2
                ]
              ]
              [\lambda_{ENC}
                [LR
                ]
              ]
              [\Lambda
                [SR
                ]
              ]
            ]
          ]
        ]
        [Bayesian (Spike \& slab prior)
          [BoomSpikeSlab
            [MCMC
              [{EMS=1}
                [BMA, just=Model
                ]
              ]
              [EMS_{CV}, just=Tuning parameter
                [BMAC, name=ip2,
                tikz={
                  \draw [decorate, decoration={brace, amplitude=5pt}, thick]  (right just 4.north east) +(5pt,0) coordinate (e) -- (e |- right just 5.south) node [midway, right, xshift=5pt, justifier format, align=left] {Ensemble\\Characteristics};
                }
                ]
              ]
            ]
          ]
        ]
      ]
    \end{justtree}
  \end{center}
  \footnotesize% note that this is a switch - it does not take an argument
  \textbf{ENC:} The baseline penalized regression model. Elastic net with $\lambda_{optimal} =\lambda_{ENC}$ derived from cross-validation (CV), \textbf{Ensembles based on 100 subsamples:} \textbf{PS:} Meinshausen \& B{\"u}hlmann's algorithm with a single $\lambda_{optimal} = \lambda_{MB}$ selected to minimize the expected number of false positives, \textbf{LS:} Single $\lambda_{optimal} = \lambda_{ENC}$ with no variable selection, \textbf{SS:} Stability selection across the entire 100 $\lambda \in \Lambda$ grid with no variable selection, \textbf{Ensembles based on 100 resamples: }\textbf{PR, LR, SR:} Identical to PS, PR and LR, respectively, with model space constructed through resampling. \textbf{BMA:} Bayesian model averaging with expected model size ($EMS$) = 1, \textbf{BMAC:} BMA with EMS determined by CV ($EMS_{CV}$).
\end{sidewaysfigure}

\end{document}

具有多行的 Booktabs 表：垂直规则的替代？

答案1

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