Description Usage Arguments Value Note References Examples

View source: R/fuzzyforest_fit.R

Fits fuzzy forest algorithm using WGCNA. Returns fuzzy forest object.

1 2 3 4 | ```
wff(X, y, Z = NULL, WGCNA_params = WGCNA_control(p = 6),
screen_params = screen_control(min_ntree = 5000),
select_params = select_control(min_ntree = 5000), final_ntree = 500,
num_processors = 1, nodesize, test_features = NULL, test_y = NULL)
``` |

`X` |
A data.frame. Each column corresponds to a feature vector. WGCNA will be used to cluster the features in X. As a result, the features should be all be numeric. Non-numeric features may be input via Z. |

`y` |
Response vector. For classification, y should be a factor or a character. For regression, y should be numeric. |

`Z` |
Additional features that are not to be screened out at the screening step. WGCNA is not carried out on features in Z. |

`WGCNA_params` |
Parameters for WGCNA.
See |

`screen_params` |
Parameters for screening step of fuzzy forests.
See |

`select_params` |
Parameters for selection step of fuzzy forests.
See |

`final_ntree` |
Number trees grown in the final random forest. This random forest contains all selected features. |

`num_processors` |
Number of processors used to fit random forests. |

`nodesize` |
Minimum terminal nodesize. 1 if classification.
5 if regression. If the sample size is very large,
the trees will be grown extremely deep.
This may lead to issues with memory usage and may
lead to significant increases in the time it takes
the algorithm to run. In this case,
it may be useful to increase |

`test_features` |
A data.frame containing features from a test set. The data.frame should contain the features in both X and Z. |

`test_y` |
The responses for the test set. |

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.

This work was partially funded by NSF IIS 1251151.

Leo Breiman (2001). Random Forests. Machine Learning, 45(1), 5-32.

Daniel Conn, Tuck Ngun, Christina M. Ramirez (2015). Fuzzy Forests: a New WGCNA Based Random Forest Algorithm for Correlated, High-Dimensional Data, Journal of Statistical Software, Manuscript in progress.

Bin Zhang and Steve Horvath (2005) "A General Framework for Weighted Gene Co-Expression Network Analysis", Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17

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 | ```
library(WGCNA)
library(randomForest)
library(fuzzyforest)
data(ctg)
y <- ctg$NSP
X <- ctg[, 2:22]
WGCNA_params <- WGCNA_control(p = 6, minModuleSize = 1, nThreads = 1)
mtry_factor <- 1; min_ntree <- 500; drop_fraction <- .5; ntree_factor <- 1
screen_params <- screen_control(drop_fraction = drop_fraction,
keep_fraction = .25, min_ntree = min_ntree,
ntree_factor = ntree_factor,
mtry_factor = mtry_factor)
select_params <- select_control(drop_fraction = drop_fraction,
number_selected = 5,
min_ntree = min_ntree,
ntree_factor = ntree_factor,
mtry_factor = mtry_factor)
wff_fit <- wff(X, y, WGCNA_params = WGCNA_params,
screen_params = screen_params,
select_params = select_params,
final_ntree = 500)
#extract variable importance rankings
vims <- wff_fit$feature_list
#plot results
modplot(wff_fit)
``` |

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