Description Usage Arguments Details Value Examples
The main function for scale-free forest density estimation.
1 | scalefreeForest(xtrain, xheld, lambda = seq(0.005, 0.15, 0.005), iter.max = 100, range = NULL, verbose = TRUE)
|
xtrain |
An |
xheld |
An |
lambda |
A sequence of positive numbers to control the regularization of the log degree penalty. The default value is |
iter.max |
The maximal number of steps in the iterative reweighted Kruskal's algorithm. The default value is |
range |
The range for each of the variables. The default value is |
verbose |
If |
The training data set is used to construct scale-free forests and the corresponding density estimators, while the held-out data set is then used to determine the optimal scale-free forest by maximizing the held-out log-likelihood.
loglike |
Maximal held-out log-likelihood for the optimal scale-free forest density estimators corresponding to the regularization parameters. |
adj |
Adjacency matrices of the optimal scale-free forests corresponding to the regularization parameters. |
best.loglike |
Maximal held-out log-likelihood over all the regularization parameters. |
best.adj |
Adjacency matrix of the optimal scale-free forest corresponding to the maximum of the held-out log-likelihood over all the regularization parameters. |
1 2 3 4 5 | library(igraph)
fit <- scalefreeForest(xtrain, xheld)
sf <- fit$best.adj
plot(sf)
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