The PCHC Bayesian network learning algorithm.
pchc(x, method = "pearson", alpha = 0.05, robust = FALSE, ini.stat = NULL,
R = NULL, restart = 10, score = "bic-g", blacklist = NULL, whitelist = NULL)A numerical matrix with the variables. If you have a data.frame (i.e. categorical data) turn them into a matrix
using data.frame.to_matrix. Note, that for the categorical case data, the numbers must start from 0.
No missing data are allowed.
If you have continuous data, you can choose either "pearson" or "spearman". If you have categorical data though,
this must be "cat". In this case, make sure the minimum value of each variable is zero. The g2test
and the relevant functions work that way.
The significance level for assessing the p-values.
Do you want outliers to be removed prior to applying the PCHC algorithm? If yes, set this to TRUE to utilise the MCD.
If the initial test statistics (univariate associations) are available, pass them through this parameter.
If the correlation matrix is available, pass it here.
An integer, the number of random restarts.
A character string, the label of the network score to be used in the algorithm. If none is specified, the default score is the Bayesian Information Criterion for both discrete and continuous data sets. The available score for continuous variables are: "bic-g" (default), "loglik-g", "aic-g", "bic-g" or "bge". The available score categorical variables are: "bde", "loglik" or "bic".
A data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs not to be included in the graph.
A data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs to be included in the graph.
A list including:
A list including the output of the pchc.skel function.
A "bn" class output. A list including the outcome of the Hill-Climbing phase. See the package "bnlearn" for more details.
The score value.
The duration of the algorithm.
The PC algorithm as proposed by Spirtes et al. (2000) is first implemented followed by a scoring phase, such as hill climbing.
Tsagris M. (2021). A new scalable Bayesian network learning algorithm with applications to economics. Computational Economics (Accepted for publication).
Spirtes P., Glymour C. and Scheines R. (2001). Causation, Prediction, and Search. The MIT Press, Cambridge, MA, USA, 3nd edition.
Tsamardinos I. and Borboudakis G. (2010) Permutation Testing Improves Bayesian Network Learning. In Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. 322-337.
Tsamardinos I., Brown E.L. and Aliferis F.C. (2006). The max-min hill-climbing Bayesian network structure learning algorithm. Machine learning 65(1): 31-78.
# NOT RUN {
# simulate a dataset with continuous data
x <- matrix( rnorm(400 * 30, 1, 10), nrow = 400 )
a <- pchc(x)
# }
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