L1Selection: L1 Selection for Rnets
In Rnets: Resistance Relationship Networks using Graphical LASSO

Description Usage Arguments Value Examples

An implementation of the Stability Approach to Regularization Selection (StARS) method for L1 penalty selection for use with Rnets method.

L1Selection(x, L1_values, B = 100, n_b = 0.5, vertices = NULL,
  n_min = 1, cor_method = "s", cor_pairing = "pair",
  forced_zeros = NULL, subset = NULL, D_thresh = 0.05,
  verbose = TRUE)

`x`	The dataset containing the MICs
`L1_values`	The set of candidate L1 penalties to be evaluated for creating a sparse precision matrix. Must be non-negative.
`B`	The number of subsamples to evaluate network stability. Defaults to 100 subsamples.
`n_b`	The size of the subsample to be drawn from the data. If 0 < n_b < 1, this is interpreted as a proportion of the data set; if n_b > 1, it is interpreted as a set sample size. Defaults to 50% of sample size.
`vertices`	A character vector corresponding to the names of the antibiotics to include in the Rnet. Defaults to an empty list, in which case all columns in 'MIC_data' will be included in the Rnet
`n_min`	The minimum number of observations required for an an estimated correlation to be valid. Defaults to 0, in which case any number of observations will be sufficient to estimate a valid correlation
`cor_method`	The method used to estimate the correlation matrix. Must be 'pearson', 'spearman', or 'kendall'. Partial matches allowed. Defaults to 'spearman'.
`cor_pairing`	The method used to determine how NAs are handled when determining which pairs are used to estimate correlations. See 'cor' function documentation for additional information.
`forced_zeros`	Edges to be omitted from the Rnet.
`subset`	The rule for stratifying the data, if desired.
`D_thresh`	Maximum tolerated D value. Suggested D_thresh = 0.05.
`verbose`	Logical that tells the function to list progress in estimating Rnets from subsets.

A vector of D statistics, corresponding the tested L1 values.

 EC_all_L1Selection <- L1Selection(
                             x = NARMS_EC_DATA, 
                             L1_values = seq(0.05, 0.50, 0.05),
                             n_b = 1500,
                             v = ABX_LIST
                             )
print(EC_all_L1Selection)