带乳胶的组织结构图

Question

使用 Latex Tikz 制作的第三级组织结构图

在控制员之下直接添加分支机构来设立他的秘书处以及直接在他之下的某些职责——我该怎么做？

Answer 1

你可以随时添加一些 Ti钾Z 命令用于向树中添加特征。在本例中，这是\draw最后的命令。我还使用 为根节点命名。要生成更多空间，您需要通过调整并添加一些来alias稍微移动树。fork sepyshift

\documentclass[border=20pt,tikz]{standalone}
\usepackage[edges]{forest}

\forestset{
  direction switch/.style={
    for tree={edge+=thick, font=\sffamily},
    where level>=1{folder, grow'=0}{for children=forked edge},
    where level=3{}{draw},
  },
}
\begin{document}

\begin{forest}
  % forest preamble: determine layout and format of tree
  direction switch,
  for tree={fork sep=3em}
  [Life Prediction Approaches and Techniques,yshift=3em,alias=LP
    [Model-base
      [Physics-of-Failure
      ]
      [Satistical Model
        [Proportion Hazard Model]
        [Logistics Regression Model]
        [Cumulative Damage Model]
      ]
      [Kalman/Particle Filtering
      ]
      [Nonlinear Dynamics
      ]
    ]
    [Knowledge-base
      [Expert Systems
        [Rule-Based]
        [Model-Based]
        [Case-Based]
      ]
      [Fuzzy Logics
      ]
    ]
    [Experience-base
      [Parametric Distribution
        [Location Scale \& Log-Location Scale]
        [Normal \& Lognormal]
        [Smallest \& Largest Extreme Values]
        [Something Beginning with W]
        [Logistic \& Log-Logistic]
      ]
      [Nonparametric Distribution
      ]
    ]
    [Data-driven
      [Multivariate Statistical Method
        [Principal Component Analysis]
        [Something \& Something Else]
        [Another Thing]
        [A Final Thing Here]
      ]
      [Black-Box Methods
        [Decision Trees]
        [Multilayer Perceptions]
        [Neural Networks]
        [Radial Basis Functions]
        [Vector Quantification]
      ]
      [Signal Analysis
        [Auto-Something Here]
        [Fourier Transform]
        [Filters]
        [Tidal Functions]
      ]
      [Graphical Model
        [Bayesian Network]
        [Hidden Markov Networks]
      ]
      [Self-Organising Feature Maps
      ]
    ]
  ]
\draw[thick]
([yshift=-1.5em]LP.south)  -- ++(-6em,0) node[left,draw,font=\sffamily,thin]{ABC}
([yshift=-1.5em]LP.south)  -- ++(6em,0) node[right,draw,font=\sffamily,thin]{XYZ};
\end{forest}
\end{document}

旧答案：这就像添加一个级别并将子句中的数字移动where1 一样简单。

\documentclass[border=20pt,tikz]{standalone}
% ateb: https://tex.stackexchange.com/a/271349/ addaswyd o gwestiwn OOzy Pal: https://tex.stackexchange.com/q/271170/
% https://tex.stackexchange.com/a/271349/194703
\usepackage[edges]{forest}

\forestset{
  direction switch/.style={
    for tree={edge+=thick, font=\sffamily},
    where level>=2{folder, grow'=0}{for children=forked edge},
    where level=4{}{draw},
  },
}
\begin{document}

\begin{forest}
  % forest preamble: determine layout and format of tree
  direction switch,
  [Life Prediction Approaches and Techniques
   [ABC
    [Model-base
      [Physics-of-Failure
      ]
      [Satistical Model
        [Proportion Hazard Model]
        [Logistics Regression Model]
        [Cumulative Damage Model]
      ]
      [Kalman/Particle Filtering
      ]
      [Nonlinear Dynamics
      ]
    ]
    [Knowledge-base
      [Expert Systems
        [Rule-Based]
        [Model-Based]
        [Case-Based]
      ]
      [Fuzzy Logics
      ]
    ]
   ]
  [XYZ  
    [Experience-base
      [Parametric Distribution
        [Location Scale \& Log-Location Scale]
        [Normal \& Lognormal]
        [Smallest \& Largest Extreme Values]
        [Something Beginning with W]
        [Logistic \& Log-Logistic]
      ]
      [Nonparametric Distribution
      ]
    ]
    [Data-driven
      [Multivariate Statistical Method
        [Principal Component Analysis]
        [Something \& Something Else]
        [Another Thing]
        [A Final Thing Here]
      ]
      [Black-Box Methods
        [Decision Trees]
        [Multilayer Perceptions]
        [Neural Networks]
        [Radial Basis Functions]
        [Vector Quantification]
      ]
      [Signal Analysis
        [Auto-Something Here]
        [Fourier Transform]
        [Filters]
        [Tidal Functions]
      ]
      [Graphical Model
        [Bayesian Network]
        [Hidden Markov Networks]
      ]
      [Self-Organising Feature Maps
      ]
    ]
   ]    
  ]
\end{forest}
\end{document}