See "L1_selection" documentation for more information.
A dataframe containing the original dataset.
The type of correlation matrix to use. Must be a partial match to one of the following strings: 'pearson', 'spearman', 'kendall'.
Method for handling how missing data is handled in pairs. See 'pair' argument in function 'cor' for more information.
The minimum number of valid pair-wise observations that must exist for an edge to be estimated. Vertex pairs with fewer valid pair-wise observations are assumed to be conditiontally independent.
The declared L1 penalty to be used when estimating rnet topology
The declared set of k variables to be included in the rnet as vertices
A matrix with 2 columns containing pairs of vertices to force to be conditionally independent in the rnet
A k x 2 matrix x & y coordinates of each vertex in the graph.
A dataframe containing the dataset
a numeric vector containing the candidate L1 penalties
The number of subsamples to draw from the data to evaluate topologic stability
Assigned either "proportionate" or "Total number" depending on how subsample size is determined.
A matrix (n_B x B) containing the rownumbers of the subsamples
An arrary (n_b x k x B) containing the data from the B_sets matrix
The size of the subsample B as a proportion of the complete dataset
The size of the subsample
An array (k x k x B x L1) containg all the weighted adjacency matrices generated by the all of subsamples over the L1 penalties
An array (k x k x B x L1) containg all the adjacency matrices generated by the all of subsamples over the L1 penalties
A dataframe with with graphical density data over the set of generated networks.
A dataframe showing edge stability over the set of generated networks.
A vector of D_b values used for L1 selection.
The suggested maximum D value for selection.
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