Description Usage Arguments Value Author(s) References Examples
This function learns a mixed directed acyclic graph based on both continuous and categorical data.
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data |
A n x p matrix. Each row is a sample; each column is a variable. |
type |
A string vector of length p, indicating the type of variable for each column in |
level |
A vector of length p, indicating the number of categories of each variable. For continuous variables, set it to 1. |
SNP |
A vector of length p, indicating which variable is a SNP. |
lambdaGam |
Hyperparameter γ in the EBIC if |
ruleReg |
Default is |
threshold |
Default is |
weights |
A vector of length n, indicating weights for observations. |
alpha |
Significance level for permutation test of conditional independece. Default is 0.05. |
nperm |
The number of permutations in the permutation test of conditional independece. Default is 10000. |
A list of the following components:
arcs
: A two-column matrix, indicating arcs of the DAG.
nodes
: A list. Each element is named after a node and contains the following elements.
nbr
: a string vector indicating the neighbourhood of the node.
parents
: a string vector indicating the parents of the node.
children
: a string vector indicating the children of the node.
skeleton
: A p x p adjacency matrix. If there is an edge from node i to node j, its (i,j)
th entry = 1; otherwise = 0.
Wujuan Zhong, Li Dong, Quefeng Li, Xiaojing Zheng
Jonas M. B. Haslbeck, Lourens J. Waldorp (2016). mgm: Structure Estimation for Time-Varying Mixed Graphical Models in high-dimensional Data arXiv preprint:1510.06871v2
Markus Kalisch, Martin Maechler, Diego Colombo, Marloes H. Maathuis, Peter Buehlmann (2012). Causal Inference Using Graphical Models with the R Package pcalg. Journal of Statistical Software, 47(11), 1-26.
Loh, P. L., & Wainwright, M. J. (2012, December). Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses. In NIPS (pp. 2096-2104).
Haslbeck, J., & Waldorp, L. J. (2016). mgm: Structure Estimation for time-varying Mixed Graphical Models in high-dimensional Data. arXiv preprint arXiv:1510.06871.
Marco Scutari (2010). Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software, 35(3), 1-22.
Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0
Georg Heinze and Meinhard Ploner (2018). logistf: Firth's Bias-Reduced Logistic Regression. R package version 1.23.
Min Jin Ha (2013). PenPC: A Two-step Approach to Estimate the Skeletons of High Dimensional Directed Acyclic Graphs. R package version 0.99.1.
1 2 3 4 5 6 7 8 9 10 11 | # load package
library(mDAG)
type=c("g","g","g","g","c")
level=c(1,1,1,1,2)
# To save time for running example, we set nperm as 150.
# Use default nperm=10000 to generate a more reliable DAG for your own data.
dag=mDAG(data=example_data, type=type, level=level, nperm=150)
print(dag$skeleton)
# draw the DAG
# library(bnlearn)
# bnlearn:::graphviz.backend(nodes=names(dag$nodes),arcs=dag$arcs,shape="rectangle")
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