Rnets (version 1.2.1)

L1Selection: L1 Selection for Rnets

Description

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

Usage

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)

Arguments

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.

Value

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

Examples

# NOT RUN {
 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)
# }