# Predict method for fuzzy_forest object. Obtains predictions from fuzzy forest algorithm. ```r ## S3 method for class 'fuzzy_forest' predict(object, new_data, ...) ``` ## Arguments - `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. ## Returns A vector of predictions ## Note This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC. ## Examples ```r 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) ``` ## See Also `ff`, `wff`, `ff.formula`, `wff.formula`

predict.fuzzy_forest function