Description Usage Arguments Details Value See Also Examples
Genetic algorithm for gemmR
.
1 | genAlg(metricbeta, nbeta, nsuperelites, p, gens, bestmodels, seedmetric)
|
metricbeta |
Weights derived using multiple
regression. Overwritten if |
nbeta |
Number of candidate weight vectors in each generation. |
nsuperelites |
Number of candidate weight vectors to involve in permutation for reps > 1. |
p |
Number of potential predictors. |
gens |
Generation number. For gens == 1, entirely new weights are generated. When gens > 1, bestmodels are used to generate permutations. |
bestmodels |
Matrix of best candidate weight vectors from previous generation. |
seedmetric |
If TRUE, multiple regression weights are used to seed the genetic algorithm. Otherwise, random weights are used. |
Currently has fixed scaling factors so predictors should be normalized. Heavily seeded with zero values to interact properly with AIC/BIC calculation.
allbetas |
returns a matrix of candidate weights with rows for each predictor and columns for each unique vector of betas. |
gemm
for full model-fitting function, tauTest
for
quick Kendall's tau
.
1 2 |
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