Description Usage Arguments Value Note See Also Examples

Implements formula interface for `wff`

.

1 2 |

`formula` |
Formula object. |

`data` |
data used in the analysis. |

`...` |
Additional arguments |

An object of type `fuzzy_forest`

. This
object is a list containing useful output of fuzzy forests.
In particular it contains a data.frame with list of selected features.
It also includes the random forest fit using the selected features.

See `ff`

for additional arguments.
Note that the matrix, `Z`

, of features that do not go through
the screening step must specified separately from the formula.
`test_features`

and `test_y`

are not supported in formula
interface. As in the `randomForest`

package, for large data sets
the formula interface may be substantially slower.

This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.

`wff`

,
`print.fuzzy_forest`

,
`predict.fuzzy_forest`

,
`modplot`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
data(ctg)
y <- ctg$NSP
X <- ctg[, 2:22]
dat <- as.data.frame(cbind(y, X))
WGCNA_params <- WGCNA_control(p = 6, minModuleSize = 1, nThreads = 1)
mtry_factor <- 1; min_ntree <- 500; drop_fraction <- .5; ntree_factor <- 1
screen_params <- screen_control(drop_fraction = drop_fraction,
keep_fraction = .25, min_ntree = min_ntree,
ntree_factor = ntree_factor,
mtry_factor = mtry_factor)
select_params <- select_control(drop_fraction = drop_fraction,
number_selected = 5,
min_ntree = min_ntree,
ntree_factor = ntree_factor,
mtry_factor = mtry_factor)
library(WGCNA)
wff_fit <- wff(y ~ ., data=dat,
WGCNA_params = WGCNA_params,
screen_params = screen_params,
select_params = select_params,
final_ntree = 500)
#extract variable importance rankings
vims <- wff_fit$feature_list
#plot results
modplot(wff_fit)
``` |

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