Symbolic Regression Framework
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Documentation for package ‘symbolicr’ version 1.0.0
Help Pages
| analyze.variables | Analyze Variables Get information on the relative importance of each variable included in your formula. |
| comb.search | Explore given combinations of formula terms |
| cross.validate | Test a non-linear formula |
| dataset.min.maxs | Compute dataset column-wise statistics: min, absmin, absmax, projzero |
| empty.sample | Return an empty data.frame with the columns returned by 'random.search' |
| exaustive.search | Explore all combinations of formula terms |
| genetic.search | Genetic Algorithm for non-linear formula optimization |
| monitor.formula.fun | Default formula to monitor genetic evolution |
| normalize | Normalize a dataset |
| normalize.test | Normalize a dataset with pre-defined column mean and standard deviation |
| parse.vars | Parse text representation of a non-linear formula term |
| pe.r.squared.formula.len.fitness | Default fitness function for 'genetic.search' |
| pred.vs.obs | Plot "predicted vs observed" using a given formula |
| random.search | Random search for non-linear formula optimization |
| regressors | Compute regressors of a given non-linear formula |
| regressors.names | Compute all possible formula terms from a given dataset |
| serialize.vars | Parse text representation of a non-linear formula term |
| test.formula | Test a non-linear formula, get an aggregated result. |
| transformations.names | Get transformed regressors names |