Description Usage Arguments Value Examples
Estimates an adjacencey matrix for a DAG based on l1 penalized negative likelihood minimization given a partitioning of the nodes into two groups
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X |
a matrix of size n by p containing n observations an p variables |
l |
penalization parameter |
m1 |
the node at which the first partition occurs |
m2 |
the node at which the second partition occurs |
m3 |
the node at which the third partition occurs |
m4 |
the node at which the fourth partition occurs |
m5 |
the node at which the fifth partition occurs |
m6 |
the node at which the sixth partition occurs |
m7 |
the node at which the seventh partition occurs |
m8 |
the node at which the eighth partition occurs |
m9 |
the node at which the ninth partition occurs |
eps |
tolerance parameter to decide whether algorithm has converved or not |
maxitr |
maximum number of iterations to run before returning output |
init |
initial estimate of graph adjacency B |
graph adjacency B
1 | partial9(X = X, l = 2, m1 = 12, m2 = 24, m3 = 36, m4 = 48, m5 = 60, m6 = 72, m7 = 84, m8 = 96)
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