benchmarkFS: Run end-to-end Benchmark for comparison of feature selection...
In pavhrablis/benchmarkFS: What the Package Does (One Line, Title Case)

Description Usage Arguments Details Value Examples

Run end-to-end Benchmark for comparison of feature selection methods.

benchmarkFS(
  x,
  y,
  methods = c("fs.utest"),
  method.cv = "kfoldcv",
  params.cv = list(k = 3, niter = 5),
  level.cor = 1,
  params = list(adjust = "holm", feature.number = 10, alpha = 0.05),
  asm = c("fs.utest"),
  model = c("fs.utest")
)

`x`	input data where columns are variables and rows are observations (all numeric)
`y`	decision variable as a boolean vector of length equal to number of observations
`methods`	A `vector` with feature selection methods available in this library for comparison
`method.cv`	validation method `kfoldcv` for cross-validation `k-fold` or `rsampling` for `random sampling`
`params.cv`	A `list` with the following fields: `k` – the number of groups that a given data sample is to be split into, not less than 3 `test.size` – testing set size for random sampling validation `iter` – the number of validation repetitions
`level.cor`	cutoff level of correlated variables. If equal to 1 is not performed
`params`	A `list` with the following fields: `adjust` – method as accepted by `p.adjust` (`"BY"` is recommended for FDR, see Details) or `SGoF` for MDFS1D, MDFS2D and U-test `mrmr.feature.number` – number of attributes to select. Must not exceed `ncol(x)` `alpha` – significance level for MDFS1D, MDFS2D and U-test
`asm`	A `vector` with enumeration method for which to calculate Lustgarten’s stability measure
`model`	A `vector` with enumeration method for which to training and testing model `RandomForest`

Benchmark for comparison of feature selection methods dedicated to high-throughput sequencing data.

selected.feature – A list with the result of feature selection for the selected feature selection method
ranking.feature – A list with the result of the rating of the variables that were most often performed in each iteration of cross-validation
stability – A data.frame with the result of stability of selection of feature for the selected selection method
model – A data.frame with the result of constructing a random forest model for the selected feature selection method

decisions <- data$class
data$class <- NULL

benchmarkFS(data,
           decisions,
           methods = c('fs.utest', 'fs.mrmr'),
           method.cv = 'cv.kfold',
           params.cv = list(k = 3, iter = 10),
           level.cor = 0.75,
           params = list(adjust = 'SGoF', mrmr.feature.number = 10, alpha = 0.05),
           asm = c('fs.utest', 'fs.mrmr'),
           model = c('fs.utest', 'fs.mrmr')
           )