| Title: | Fuzzy Forests |
|---|---|
| Description: | Fuzzy forests, a new algorithm based on random forests, is designed to reduce the bias seen in random forest feature selection caused by the presence of correlated features. Fuzzy forests uses recursive feature elimination random forests to select features from separate blocks of correlated features where the correlation within each block of features is high and the correlation between blocks of features is low. One final random forest is fit using the surviving features. This package fits random forests using the 'randomForest' package and allows for easy use of 'WGCNA' to split features into distinct blocks. See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) <doi:10.18637/jss.v091.i09> for further details. |
| Authors: | Daniel Conn [aut, cre], Tuck Ngun [aut], Christina M. Ramirez [aut] |
| Maintainer: | Daniel Conn <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.8 |
| Built: | 2024-09-17 03:58:16 UTC |
| Source: | https://github.com/cran/fuzzyforest |
A data set containing measurements of fetal heart rate and uterine contraction from cardiotocograms. This data set was obtained from the [UCI machine learning repository](https://archive.ics.uci.edu/ml/index.html) For our examples we extract a random sub sample of 100 observations.
data(ctg)data(ctg)
A data frame with 100 rows and 21.
An example of a fuzzy_forest object derived from fitting fuzzy forests on the ctg data set. The source code used to produce example_ff can be seen in the vignette "fuzzyforest_introduction".
.RData
Fits the fuzzy forests algorithm. Note that a formula interface for
fuzzy forests also exists: ff.formula.
## Default S3 method: ff(X, y, Z = NULL, module_membership, screen_params = screen_control(min_ntree = 500), select_params = select_control(min_ntree = 500), final_ntree = 5000, num_processors = 1, nodesize, test_features = NULL, test_y = NULL, ...) ff(X, ...)## Default S3 method: ff(X, y, Z = NULL, module_membership, screen_params = screen_control(min_ntree = 500), select_params = select_control(min_ntree = 500), final_ntree = 5000, num_processors = 1, nodesize, test_features = NULL, test_y = NULL, ...) ff(X, ...)
X |
A data.frame. Each column corresponds to a feature vectors. |
y |
Response vector. For classification, y should be a factor. For regression, y should be numeric. |
Z |
A data.frame. Additional features that are not to be screened out at the screening step. |
module_membership |
A character vector giving the module membership of each feature. |
screen_params |
Parameters for screening step of fuzzy forests.
See |
select_params |
Parameters for selection step of fuzzy forests.
See |
final_ntree |
Number of 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. |
... |
Additional arguments currently not used. |
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 a list of selected the features.
It also includes a random forest fit using the selected features.
This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.
Conn, D., Ngun, T., Ramirez C.M., Li, G. (2019). "Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data." Journal of Statistical Software, 91(9). doi:10.18637/jss.v091.i09
Breiman, L. (2001). "Random Forests." Machine Learning, 45(1), 5-32. doi:10.1023/A:1010933404324
Zhang, B. and Horvath, S. (2005). "A General Framework for Weighted Gene Co-Expression Network Analysis." Statistical Applications in Genetics and Molecular Biology, 4(1). doi:10.2202/1544-6115.1128
ff.formula,
print.fuzzy_forest,
predict.fuzzy_forest,
modplot
#ff requires that the partition of the covariates be previously determined. #ff is also handy if the user wants to test out multiple settings of WGCNA #prior to running fuzzy forests. library(mvtnorm) gen_mod <- function(n, p, corr) { sigma <- matrix(corr, nrow=p, ncol=p) diag(sigma) <- 1 X <- rmvnorm(n, sigma=sigma) return(X) } gen_X <- function(n, mod_sizes, corr){ m <- length(mod_sizes) X_list <- vector("list", length = m) for(i in 1:m){ X_list[[i]] <- gen_mod(n, mod_sizes[i], corr[i]) } X <- do.call("cbind", X_list) return(X) } err_sd <- .5 n <- 500 mod_sizes <- rep(25, 4) corr <- rep(.8, 4) X <- gen_X(n, mod_sizes, corr) beta <- rep(0, 100) beta[c(1:4, 76:79)] <- 5 y <- X%*%beta + rnorm(n, sd=err_sd) X <- as.data.frame(X) Xtest <- gen_X(n, mod_sizes, corr) ytest <- Xtest%*%beta + rnorm(n, sd=err_sd) Xtest <- as.data.frame(Xtest) cdist <- as.dist(1 - cor(X)) hclust_fit <- hclust(cdist, method="ward.D") groups <- cutree(hclust_fit, k=4) screen_c <- screen_control(keep_fraction = .25, ntree_factor = 1, min_ntree = 250) select_c <- select_control(number_selected = 10, ntree_factor = 1, min_ntree = 250) ff_fit <- ff(X, y, module_membership = groups, screen_params = screen_c, select_params = select_c, final_ntree = 250) #extract variable importance rankings vims <- ff_fit$feature_list #plot results modplot(ff_fit) #obtain predicted values for a new test set preds <- predict(ff_fit, new_data=Xtest) #estimate test set error test_err <- sqrt(sum((ytest - preds)^2)/n)#ff requires that the partition of the covariates be previously determined. #ff is also handy if the user wants to test out multiple settings of WGCNA #prior to running fuzzy forests. library(mvtnorm) gen_mod <- function(n, p, corr) { sigma <- matrix(corr, nrow=p, ncol=p) diag(sigma) <- 1 X <- rmvnorm(n, sigma=sigma) return(X) } gen_X <- function(n, mod_sizes, corr){ m <- length(mod_sizes) X_list <- vector("list", length = m) for(i in 1:m){ X_list[[i]] <- gen_mod(n, mod_sizes[i], corr[i]) } X <- do.call("cbind", X_list) return(X) } err_sd <- .5 n <- 500 mod_sizes <- rep(25, 4) corr <- rep(.8, 4) X <- gen_X(n, mod_sizes, corr) beta <- rep(0, 100) beta[c(1:4, 76:79)] <- 5 y <- X%*%beta + rnorm(n, sd=err_sd) X <- as.data.frame(X) Xtest <- gen_X(n, mod_sizes, corr) ytest <- Xtest%*%beta + rnorm(n, sd=err_sd) Xtest <- as.data.frame(Xtest) cdist <- as.dist(1 - cor(X)) hclust_fit <- hclust(cdist, method="ward.D") groups <- cutree(hclust_fit, k=4) screen_c <- screen_control(keep_fraction = .25, ntree_factor = 1, min_ntree = 250) select_c <- select_control(number_selected = 10, ntree_factor = 1, min_ntree = 250) ff_fit <- ff(X, y, module_membership = groups, screen_params = screen_c, select_params = select_c, final_ntree = 250) #extract variable importance rankings vims <- ff_fit$feature_list #plot results modplot(ff_fit) #obtain predicted values for a new test set preds <- predict(ff_fit, new_data=Xtest) #estimate test set error test_err <- sqrt(sum((ytest - preds)^2)/n)
Implements formula interface for ff.
## S3 method for class 'formula' ff(formula, data = NULL, module_membership, ...)## S3 method for class 'formula' ff(formula, data = NULL, module_membership, ...)
formula |
Formula object. |
data |
data used in the analysis. |
module_membership |
A character vector giving the module membership of each feature. |
... |
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.
Conn, D., Ngun, T., Ramirez C.M., Li, G. (2019). "Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data." Journal of Statistical Software, 91(9). doi:10.18637/jss.v091.i09
Breiman, L. (2001). "Random Forests." Machine Learning, 45(1), 5-32. doi:10.1023/A:1010933404324
Zhang, B. and Horvath, S. (2005). "A General Framework for Weighted Gene Co-Expression Network Analysis." Statistical Applications in Genetics and Molecular Biology, 4(1). doi:10.2202/1544-6115.1128
ff,
print.fuzzy_forest,
predict.fuzzy_forest,
modplot
#ff requires that the partition of the covariates be previously determined. #ff is also handy if the user wants to test out multiple settings of WGCNA #prior to running fuzzy forests. library(mvtnorm) gen_mod <- function(n, p, corr) { sigma <- matrix(corr, nrow=p, ncol=p) diag(sigma) <- 1 X <- rmvnorm(n, sigma=sigma) return(X) } gen_X <- function(n, mod_sizes, corr){ m <- length(mod_sizes) X_list <- vector("list", length = m) for(i in 1:m){ X_list[[i]] <- gen_mod(n, mod_sizes[i], corr[i]) } X <- do.call("cbind", X_list) return(X) } err_sd <- .5 n <- 500 mod_sizes <- rep(25, 4) corr <- rep(.8, 4) X <- gen_X(n, mod_sizes, corr) beta <- rep(0, 100) beta[c(1:4, 76:79)] <- 5 y <- X%*%beta + rnorm(n, sd=err_sd) X <- as.data.frame(X) dat <- as.data.frame(cbind(y, X)) Xtest <- gen_X(n, mod_sizes, corr) ytest <- Xtest%*%beta + rnorm(n, sd=err_sd) Xtest <- as.data.frame(Xtest) cdist <- as.dist(1 - cor(X)) hclust_fit <- hclust(cdist, method="ward.D") groups <- cutree(hclust_fit, k=4) screen_c <- screen_control(keep_fraction = .25, ntree_factor = 1, min_ntree = 250) select_c <- select_control(number_selected = 10, ntree_factor = 1, min_ntree = 250) ff_fit <- ff(y ~ ., data=dat, module_membership = groups, screen_params = screen_c, select_params = select_c, final_ntree = 250) #extract variable importance rankings vims <- ff_fit$feature_list #plot results modplot(ff_fit) #obtain predicted values for a new test set preds <- predict(ff_fit, new_data=Xtest) #estimate test set error test_err <- sqrt(sum((ytest - preds)^2)/n)#ff requires that the partition of the covariates be previously determined. #ff is also handy if the user wants to test out multiple settings of WGCNA #prior to running fuzzy forests. library(mvtnorm) gen_mod <- function(n, p, corr) { sigma <- matrix(corr, nrow=p, ncol=p) diag(sigma) <- 1 X <- rmvnorm(n, sigma=sigma) return(X) } gen_X <- function(n, mod_sizes, corr){ m <- length(mod_sizes) X_list <- vector("list", length = m) for(i in 1:m){ X_list[[i]] <- gen_mod(n, mod_sizes[i], corr[i]) } X <- do.call("cbind", X_list) return(X) } err_sd <- .5 n <- 500 mod_sizes <- rep(25, 4) corr <- rep(.8, 4) X <- gen_X(n, mod_sizes, corr) beta <- rep(0, 100) beta[c(1:4, 76:79)] <- 5 y <- X%*%beta + rnorm(n, sd=err_sd) X <- as.data.frame(X) dat <- as.data.frame(cbind(y, X)) Xtest <- gen_X(n, mod_sizes, corr) ytest <- Xtest%*%beta + rnorm(n, sd=err_sd) Xtest <- as.data.frame(Xtest) cdist <- as.dist(1 - cor(X)) hclust_fit <- hclust(cdist, method="ward.D") groups <- cutree(hclust_fit, k=4) screen_c <- screen_control(keep_fraction = .25, ntree_factor = 1, min_ntree = 250) select_c <- select_control(number_selected = 10, ntree_factor = 1, min_ntree = 250) ff_fit <- ff(y ~ ., data=dat, module_membership = groups, screen_params = screen_c, select_params = select_c, final_ntree = 250) #extract variable importance rankings vims <- ff_fit$feature_list #plot results modplot(ff_fit) #obtain predicted values for a new test set preds <- predict(ff_fit, new_data=Xtest) #estimate test set error test_err <- sqrt(sum((ytest - preds)^2)/n)
Fuzzy forests returns an object of type fuzzyforest.
fuzzy_forest(feature_list, final_rf, module_membership, WGCNA_object = NULL, survivor_list, selection_list)fuzzy_forest(feature_list, final_rf, module_membership, WGCNA_object = NULL, survivor_list, selection_list)
feature_list |
List of selected features along with variable importance measures. |
final_rf |
A final random forest fit using the features selected by fuzzy forests. |
module_membership |
Module membership of each feature. |
WGCNA_object |
If applicable, output of WGCNA analysis. |
survivor_list |
List of features that have survived screening step. |
selection_list |
List of features retained at each iteration of selection step. |
An object of type fuzzy_forest.
This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.
This package implements fuzzy forests and integrates the fuzzy forests algorithm with the package, WGCNA.
This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.
Fits iterative random forest algorithm. Returns data.frame with variable importances and top rated features. For now this is an internal function that I've used to explore how recursive feature elimination works in simulations. It may be exported at a later time.
iterative_RF(X, y, drop_fraction, keep_fraction, mtry_factor, ntree_factor = 10, min_ntree = 5000, num_processors = 1, nodesize)iterative_RF(X, y, drop_fraction, keep_fraction, mtry_factor, ntree_factor = 10, min_ntree = 5000, num_processors = 1, nodesize)
X |
A data.frame. Each column corresponds to a feature vectors. |
y |
Response vector. |
drop_fraction |
A number between 0 and 1. Percentage of features dropped at each iteration. |
keep_fraction |
A number between 0 and 1. Proportion features from each module to retain at screening step. |
mtry_factor |
A positive number. Mtry for each random forest
is set to
|
ntree_factor |
A number greater than 1. |
min_ntree |
Minimum number of trees grown in each random forest. |
num_processors |
Number of processors used to fit random forests. |
nodesize |
Minimum nodesize. |
A data.frame with the top ranked features.
This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.
A data set containing gene expression levels in liver tissue from female mice. This data set is a subset of the liver expression data set from the WGCNA tutorial https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/. The tutorial contains further information about the data set as well as extensive examples of WGCNA.
data(Liver_Expr)data(Liver_Expr)
A data frame with 66 rows and 3601
The first column contains weight (g) for the 66 mice.
The other 3600 columns contain the liver expression levels.
The plot is designed to depict the size of each module and what percentage of selected features fall into each module. In particular, it is easy to determine which module is over-represented in the group of selected features.
modplot(object, main = NULL, xlab = NULL, ylab = NULL, module_labels = NULL)modplot(object, main = NULL, xlab = NULL, ylab = NULL, module_labels = NULL)
object |
A fuzzy_forest object. |
main |
Title of plot. |
xlab |
Title for the x axis. |
ylab |
Title for the y axis. |
module_labels |
Labels for the modules. A data.frame or character matrix with first column giving the current name of module and second column giving the assigned name of each module. |
This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.
ff,
wff,
ff.formula,
wff.formula
Function to generate multi-class data from a multinomial logistic regression. Assumes there are 5 classes. Only supports two modules for now. Currently this function is used for testing.
multi_class_lr(n, mod1_size = 10, mod2_size = 10, rho = 0.8, beta = NULL)multi_class_lr(n, mod1_size = 10, mod2_size = 10, rho = 0.8, beta = NULL)
n |
Sample size. |
mod1_size |
Size of first module. |
mod2_size |
Size of second module. |
rho |
Correlation of covariates. |
beta |
A matrix of parameters. |
list with design matrix X, outcome y, and beta.
This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.
Predict method for fuzzy_forest object. Obtains predictions from fuzzy forest algorithm.
## S3 method for class 'fuzzy_forest' predict(object, new_data, ...)## S3 method for class 'fuzzy_forest' predict(object, new_data, ...)
object |
A fuzzy_forest object. |
new_data |
A matrix or data.frame containing new_data. Pay close attention to ensure feature names match between training set and test set data.frame. |
... |
Additional arguments not in use. |
A vector of predictions
This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.
ff,
wff,
ff.formula,
wff.formula
library(mvtnorm) gen_mod <- function(n, p, corr) { sigma <- matrix(corr, nrow=p, ncol=p) diag(sigma) <- 1 X <- rmvnorm(n, sigma=sigma) return(X) } gen_X <- function(n, mod_sizes, corr){ m <- length(mod_sizes) X_list <- vector("list", length = m) for(i in 1:m){ X_list[[i]] <- gen_mod(n, mod_sizes[i], corr[i]) } X <- do.call("cbind", X_list) return(X) } err_sd <- .5 n <- 500 mod_sizes <- rep(25, 4) corr <- rep(.8, 4) X <- gen_X(n, mod_sizes, corr) beta <- rep(0, 100) beta[c(1:4, 76:79)] <- 5 y <- X%*%beta + rnorm(n, sd=err_sd) X <- as.data.frame(X) Xtest <- gen_X(n, mod_sizes, corr) ytest <- Xtest%*%beta + rnorm(n, sd=err_sd) Xtest <- as.data.frame(Xtest) cdist <- as.dist(1 - cor(X)) hclust_fit <- hclust(cdist, method="ward.D") groups <- cutree(hclust_fit, k=4) screen_c <- screen_control(keep_fraction = .25, ntree_factor = 1, min_ntree = 250) select_c <- select_control(number_selected = 10, ntree_factor = 1, min_ntree = 250) ff_fit <- ff(X, y, module_membership = groups, screen_params = screen_c, select_params = select_c, final_ntree = 250) #extract variable importance rankings vims <- ff_fit$feature_list #plot results modplot(ff_fit) #obtain predicted values for a new test set preds <- predict(ff_fit, new_data=Xtest) #estimate test set error test_err <- sqrt(sum((ytest - preds)^2)/n)library(mvtnorm) gen_mod <- function(n, p, corr) { sigma <- matrix(corr, nrow=p, ncol=p) diag(sigma) <- 1 X <- rmvnorm(n, sigma=sigma) return(X) } gen_X <- function(n, mod_sizes, corr){ m <- length(mod_sizes) X_list <- vector("list", length = m) for(i in 1:m){ X_list[[i]] <- gen_mod(n, mod_sizes[i], corr[i]) } X <- do.call("cbind", X_list) return(X) } err_sd <- .5 n <- 500 mod_sizes <- rep(25, 4) corr <- rep(.8, 4) X <- gen_X(n, mod_sizes, corr) beta <- rep(0, 100) beta[c(1:4, 76:79)] <- 5 y <- X%*%beta + rnorm(n, sd=err_sd) X <- as.data.frame(X) Xtest <- gen_X(n, mod_sizes, corr) ytest <- Xtest%*%beta + rnorm(n, sd=err_sd) Xtest <- as.data.frame(Xtest) cdist <- as.dist(1 - cor(X)) hclust_fit <- hclust(cdist, method="ward.D") groups <- cutree(hclust_fit, k=4) screen_c <- screen_control(keep_fraction = .25, ntree_factor = 1, min_ntree = 250) select_c <- select_control(number_selected = 10, ntree_factor = 1, min_ntree = 250) ff_fit <- ff(X, y, module_membership = groups, screen_params = screen_c, select_params = select_c, final_ntree = 250) #extract variable importance rankings vims <- ff_fit$feature_list #plot results modplot(ff_fit) #obtain predicted values for a new test set preds <- predict(ff_fit, new_data=Xtest) #estimate test set error test_err <- sqrt(sum((ytest - preds)^2)/n)
Print fuzzy_forest object. Prints output from fuzzy forests algorithm.
## S3 method for class 'fuzzy_forest' print(x, ...)## S3 method for class 'fuzzy_forest' print(x, ...)
x |
A fuzzy_forest object. |
... |
Additional arguments not in use. |
data.frame with list of selected features and variable importance measures.
This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.
Creates screen_control object for
controlling how feature selection
will be carried out on each module.
screen_control(drop_fraction = 0.25, keep_fraction = 0.05, mtry_factor = 1, min_ntree = 500, ntree_factor = 1)screen_control(drop_fraction = 0.25, keep_fraction = 0.05, mtry_factor = 1, min_ntree = 500, ntree_factor = 1)
drop_fraction |
A number between 0 and 1. Percentage of features dropped at each iteration. |
keep_fraction |
A number between 0 and 1. Proportion of features from each module that are retained from screening step. |
mtry_factor |
In the case of regression, |
min_ntree |
Minimum number of trees grown in each random forest. |
ntree_factor |
A number greater than 1. |
An object of type screen_control.
This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.
Conn, D., Ngun, T., Ramirez C.M., Li, G. (2019). "Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data." Journal of Statistical Software, 91(9). doi:10.18637/jss.v091.i09
drop_fraction <- .25 keep_fraction <- .1 mtry_factor <- 1 min_ntree <- 5000 ntree_factor <- 5 screen_params <- screen_control(drop_fraction=drop_fraction, keep_fraction=keep_fraction, mtry_factor=mtry_factor, min_ntree=min_ntree, ntree_factor=ntree_factor)drop_fraction <- .25 keep_fraction <- .1 mtry_factor <- 1 min_ntree <- 5000 ntree_factor <- 5 screen_params <- screen_control(drop_fraction=drop_fraction, keep_fraction=keep_fraction, mtry_factor=mtry_factor, min_ntree=min_ntree, ntree_factor=ntree_factor)
Creates selection_control object for
controlling how feature selection
will be carried out after features from different
modules have been combined.
select_control(drop_fraction = 0.25, number_selected = 5, mtry_factor = 1, min_ntree = 500, ntree_factor = 1)select_control(drop_fraction = 0.25, number_selected = 5, mtry_factor = 1, min_ntree = 500, ntree_factor = 1)
drop_fraction |
A number between 0 and 1. Percentage of features dropped at each iteration. |
number_selected |
A positive number. Number of features that will be selected by fuzzyforests. |
mtry_factor |
In the case of regression, |
min_ntree |
Minimum number of trees grown in each random forest. |
ntree_factor |
A number greater than 1. |
An object of type selection_control.
This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.
Conn, D., Ngun, T., Ramirez C.M., Li, G. (2019). "Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data." Journal of Statistical Software, 91(9). doi:10.18637/jss.v091.i09
drop_fraction <- .25 number_selected <- 10 mtry_factor <- 1 min_ntree <- 5000 ntree_factor <- 5 select_params <- select_control(drop_fraction=drop_fraction, number_selected=number_selected, mtry_factor=mtry_factor, min_ntree=min_ntree, ntree_factor=ntree_factor)drop_fraction <- .25 number_selected <- 10 mtry_factor <- 1 min_ntree <- 5000 ntree_factor <- 5 select_params <- select_control(drop_fraction=drop_fraction, number_selected=number_selected, mtry_factor=mtry_factor, min_ntree=min_ntree, ntree_factor=ntree_factor)
Carries out the selection step of fuzzyforest algorithm. Returns data.frame with variable importances and top rated features.
select_RF(X, y, drop_fraction, number_selected, mtry_factor, ntree_factor, min_ntree, num_processors, nodesize)select_RF(X, y, drop_fraction, number_selected, mtry_factor, ntree_factor, min_ntree, num_processors, nodesize)
X |
A data.frame. Each column corresponds to a feature vectors. Could include additional covariates not a part of the original modules. |
y |
Response vector. |
drop_fraction |
A number between 0 and 1. Percentage of features dropped at each iteration. |
number_selected |
Number of features selected by fuzzyforest. |
mtry_factor |
In the case of regression, |
ntree_factor |
A number greater than 1. |
min_ntree |
Minimum number of trees grown in each random forest. |
num_processors |
Number of processors used to fit random forests. |
nodesize |
Minimum nodesize |
A data.frame with the top ranked features.
This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.
Fits fuzzy forests using WGCNA to cluster features into
distinct modules. Requires installation of WGCNA package. Note that a formula interface for
WGCNA based fuzzy forests also exists: wff.formula.
## Default S3 method: wff(X, y, Z = NULL, WGCNA_params = WGCNA_control(power = 6), screen_params = screen_control(min_ntree = 500), select_params = select_control(min_ntree = 500), final_ntree = 5000, num_processors = 1, nodesize, test_features = NULL, test_y = NULL, ...) wff(X, ...)## Default S3 method: wff(X, y, Z = NULL, WGCNA_params = WGCNA_control(power = 6), screen_params = screen_control(min_ntree = 500), select_params = select_control(min_ntree = 500), final_ntree = 5000, num_processors = 1, nodesize, test_features = NULL, test_y = NULL, ...) wff(X, ...)
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. 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 blockwiseModules function from WGCNA and
|
screen_params |
Parameters for screening step of fuzzy forests.
See |
select_params |
Parameters for selection step of fuzzy forests.
See |
final_ntree |
Number of 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. |
... |
Additional arguments currently not used. |
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 and AMFAR 8721SC.
Conn, D., Ngun, T., Ramirez C.M., Li, G. (2019). "Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data." Journal of Statistical Software, 91(9). doi:10.18637/jss.v091.i09
Breiman, L. (2001). "Random Forests." Machine Learning, 45(1), 5-32. doi:10.1023/A:1010933404324
Zhang, B. and Horvath, S. (2005). "A General Framework for Weighted Gene Co-Expression Network Analysis." Statistical Applications in Genetics and Molecular Biology, 4(1). doi:10.2202/1544-6115.1128
wff.formula,
print.fuzzy_forest,
predict.fuzzy_forest,
modplot
data(ctg) y <- ctg$NSP X <- ctg[, 2:22] WGCNA_params <- WGCNA_control(p = 6, minModuleSize = 1, nThreads = 1) mtry_factor <- 1; min_ntree <- 500; drop_fraction <- .5; ntree_factor <- 1 screen_params <- screen_control(drop_fraction = drop_fraction, keep_fraction = .25, min_ntree = min_ntree, ntree_factor = ntree_factor, mtry_factor = mtry_factor) select_params <- select_control(drop_fraction = drop_fraction, number_selected = 5, min_ntree = min_ntree, ntree_factor = ntree_factor, mtry_factor = mtry_factor) library(WGCNA) wff_fit <- wff(X, y, WGCNA_params = WGCNA_params, screen_params = screen_params, select_params = select_params, final_ntree = 500) #extract variable importance rankings vims <- wff_fit$feature_list #plot results modplot(wff_fit)data(ctg) y <- ctg$NSP X <- ctg[, 2:22] WGCNA_params <- WGCNA_control(p = 6, minModuleSize = 1, nThreads = 1) mtry_factor <- 1; min_ntree <- 500; drop_fraction <- .5; ntree_factor <- 1 screen_params <- screen_control(drop_fraction = drop_fraction, keep_fraction = .25, min_ntree = min_ntree, ntree_factor = ntree_factor, mtry_factor = mtry_factor) select_params <- select_control(drop_fraction = drop_fraction, number_selected = 5, min_ntree = min_ntree, ntree_factor = ntree_factor, mtry_factor = mtry_factor) library(WGCNA) wff_fit <- wff(X, y, WGCNA_params = WGCNA_params, screen_params = screen_params, select_params = select_params, final_ntree = 500) #extract variable importance rankings vims <- wff_fit$feature_list #plot results modplot(wff_fit)
Implements formula interface for wff.
## S3 method for class 'formula' wff(formula, data = NULL, ...)## S3 method for class 'formula' wff(formula, data = NULL, ...)
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
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)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)
Creates WGCNA_control object for
controlling WGCNA will be carried out.
WGCNA_control(power = 6, ...)WGCNA_control(power = 6, ...)
power |
Power of adjacency function. |
... |
Additional arguments. See blockwiseModules from the WGCNA package for details. |
An object of type WGCNA_control.
This work was partially funded by NSF IIS 1251151.
Conn, D., Ngun, T., Ramirez C.M., Li, G. (2019). "Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data." Journal of Statistical Software, 91(9). doi:10.18637/jss.v091.i09
Zhang, B. and Horvath, S. (2005). "A General Framework for Weighted Gene Co-Expression Network Analysis." Statistical Applications in Genetics and Molecular Biology, 4(1). doi:10.2202/1544-6115.1128
WGCNA_params <- WGCNA_control(p=7, minModuleSize=30, TOMType = "unsigned", reassignThreshold = 0, mergeCutHeight = 0.25, numericLabels = TRUE, pamRespectsDendro = FALSE)WGCNA_params <- WGCNA_control(p=7, minModuleSize=30, TOMType = "unsigned", reassignThreshold = 0, mergeCutHeight = 0.25, numericLabels = TRUE, pamRespectsDendro = FALSE)