See "L1_selection" documentation for more information.
raw_data
A dataframe containing the original dataset.
cor_method
The type of correlation matrix to use. Must be a partial match to one of the following strings: 'pearson', 'spearman', 'kendall'.
cor_pairing
Method for handling how missing data is handled in pairs. See 'pair' argument in function 'cor' for more information.
n_threshold
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.
L1_orig
The declared L1 penalty to be used when estimating rnet topology
V_orig
The declared set of k variables to be included in the rnet as vertices
forced_zeros
A matrix with 2 columns containing pairs of vertices to force to be conditionally independent in the rnet
layout_master
A k x 2 matrix x & y coordinates of each vertex in the graph.
x
A dataframe containing the dataset
L1_values
a numeric vector containing the candidate L1 penalties
B
The number of subsamples to draw from the data to evaluate topologic stability
method_b
Assigned either "proportionate" or "Total number" depending on how subsample size is determined.
sets_b
A matrix (n_B x B) containing the rownumbers of the subsamples
array_b
An arrary (n_b x k x B) containing the data from the B_sets matrix
pr_b
The size of the subsample B as a proportion of the complete dataset
n_b
The size of the subsample
W_aggr
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
A_aggr
An array (k x k x B x L1) containg all the adjacency matrices generated by the all of subsamples over the L1 penalties
M
A dataframe with with graphical density data over the set of generated networks.
stability
A dataframe showing edge stability over the set of generated networks.
D
A vector of D_b values used for L1 selection.
D_thresh
The suggested maximum D value for selection.
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