gg_vimp | R Documentation |
gg_vimp
Extracts the variable importance (VIMP) information from a
a rfsrc
object.
gg_vimp(object, nvar, ...)
object |
A |
nvar |
argument to control the number of variables included in the output. |
... |
arguments passed to the |
gg_vimp
object. A data.frame
of VIMP measures, in rank
order.
Ishwaran H. (2007). Variable importance in binary regression trees and forests, Electronic J. Statist., 1:519-537.
plot.gg_vimp
rfsrc
vimp
## ------------------------------------------------------------ ## classification example ## ------------------------------------------------------------ ## -------- iris data rfsrc_iris <- rfsrc(Species ~ ., data = iris, importance = TRUE) gg_dta <- gg_vimp(rfsrc_iris) plot(gg_dta) ## ------------------------------------------------------------ ## regression example ## ------------------------------------------------------------ ## Not run: ## -------- air quality data rfsrc_airq <- rfsrc(Ozone ~ ., airquality, importance = TRUE) gg_dta <- gg_vimp(rfsrc_airq) plot(gg_dta) ## End(Not run) ## -------- Boston data data(Boston, package="MASS") rfsrc_boston <- randomForestSRC::rfsrc(medv~., Boston, importance = TRUE) gg_dta <- gg_vimp(rfsrc_boston) plot(gg_dta) ## -------- Boston data rf_boston <- randomForest::randomForest(medv~., Boston) gg_dta <- gg_vimp(rf_boston) plot(gg_dta) ## Not run: ## -------- mtcars data rfsrc_mtcars <- rfsrc(mpg ~ ., data = mtcars, importance = TRUE) gg_dta <- gg_vimp(rfsrc_mtcars) plot(gg_dta) ## End(Not run) ## ------------------------------------------------------------ ## survival example ## ------------------------------------------------------------ ## Not run: ## -------- veteran data data(veteran, package = "randomForestSRC") rfsrc_veteran <- rfsrc(Surv(time, status) ~ ., data = veteran, ntree = 100, importance = TRUE) gg_dta <- gg_vimp(rfsrc_veteran) plot(gg_dta) ## -------- pbc data # We need to create this dataset data(pbc, package = "randomForestSRC",) # For whatever reason, the age variable is in days... # makes no sense to me for (ind in seq_len(dim(pbc)[2])) { if (!is.factor(pbc[, ind])) { if (length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 2) { if (sum(range(pbc[, ind], na.rm = TRUE) == c(0, 1)) == 2) { pbc[, ind] <- as.logical(pbc[, ind]) } } } else { if (length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 2) { if (sum(sort(unique(pbc[, ind])) == c(0, 1)) == 2) { pbc[, ind] <- as.logical(pbc[, ind]) } if (sum(sort(unique(pbc[, ind])) == c(FALSE, TRUE)) == 2) { pbc[, ind] <- as.logical(pbc[, ind]) } } } if (!is.logical(pbc[, ind]) & length(unique(pbc[which(!is.na(pbc[, ind])), ind])) <= 5) { pbc[, ind] <- factor(pbc[, ind]) } } #Convert age to years pbc$age <- pbc$age / 364.24 pbc$years <- pbc$days / 364.24 pbc <- pbc[, -which(colnames(pbc) == "days")] pbc$treatment <- as.numeric(pbc$treatment) pbc$treatment[which(pbc$treatment == 1)] <- "DPCA" pbc$treatment[which(pbc$treatment == 2)] <- "placebo" pbc$treatment <- factor(pbc$treatment) dta_train <- pbc[-which(is.na(pbc$treatment)), ] # Create a test set from the remaining patients pbc_test <- pbc[which(is.na(pbc$treatment)), ] #======== # build the forest: rfsrc_pbc <- randomForestSRC::rfsrc( Surv(years, status) ~ ., dta_train, nsplit = 10, na.action = "na.impute", forest = TRUE, importance = TRUE, save.memory = TRUE ) gg_dta <- gg_vimp(rfsrc_pbc) plot(gg_dta) # Restrict to only the top 10. gg_dta <- gg_vimp(rfsrc_pbc, nvar=10) plot(gg_dta) ## End(Not run)
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