wff.formula {fuzzyforest}

WGCNA based fuzzy forest algorithm

Description

Implements formula interface for wff.

Usage

## S3 method for class 'formula'
wff(formula, data = NULL, ...)

Arguments

formula

Formula object.

data

data used in the analysis.

...

Additional arguments

Value

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.

Note

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.

See Also

wff, print.fuzzy_forest, predict.fuzzy_forest, modplot

Examples

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)