# Fuzzy forests algorithm Fits the fuzzy forests algorithm. Note that a formula interface for fuzzy forests also exists: `ff.formula`. ```r ## 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, ...) ``` ## Arguments - `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 `screen_control` for details. `screen_params` is an object of type `screen_control`. - `select_params`: Parameters for selection step of fuzzy forests. See `select_control` for details. `select_params` is an object of type `select_control`. - `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 `nodesize`. - `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. ## Returns 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. ## Note This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC. ## Examples ```r #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) ``` ## References 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). tools:::Rd_expr_doi("doi:10.18637/jss.v091.i09") Breiman, L. (2001). "Random Forests." **Machine Learning**, 45 (1), 5-32. tools:::Rd_expr_doi("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). tools:::Rd_expr_doi("doi:10.2202/1544-6115.1128") ## See Also `ff.formula`, `print.fuzzy_forest`, `predict.fuzzy_forest`, `modplot`

ff function