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.
1 2 3 | L1Selection(Data, L1_set, B = 100, n_b = 0.5, V_set = NULL,
n_threshold = 0, cor_method = "s", cor_pairing = "pair",
Forced_zeros = NULL, Stratify = NULL, Rand_seed = NULL)
|
Data |
The dataset containing the MICs |
L1_set |
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. |
V_set |
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_threshold |
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. |
Stratify |
The rule for stratifying the data, if desired. |
Rand_seed |
Allows a random seed to be set subsampling results may be consistently regenerated. |
A vector of D statistics, corresponding the tested L1 values.
1 2 3 4 5 6 7 | EC_all_L1Selection <- L1Selection(
Data = NARMS_EC_DATA,
L1_set = seq(0.05, 0.50, 0.05),
n_b = 1500,
V_set = ABX_LIST
)
round(EC_all_L1Selection@StARS_D, 4)
|
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