# Plot a `gg_minimal_vimp` object for comparing the Minimal Depth and VIMP variable rankings. ```r ## S3 method for class 'gg_minimal_vimp' plot(x, nvar, lbls, ...) ``` ## Arguments - `x`: `gg_minimal_depth` object created from a `var.select` object - `nvar`: should the figure be restricted to a subset of the points. - `lbls`: a vector of alternative variable names. - `...`: optional arguments (not used) ## Returns `ggplot` object ## Examples ```r ## Not run: ## Examples from RFSRC package... ## ------------------------------------------------------------ ## classification example ## ------------------------------------------------------------ ## -------- iris data ## You can build a randomForest rfsrc_iris <- rfsrc(Species ~ ., data = iris) varsel_iris <- var.select(rfsrc_iris) # Get a data.frame containing minimaldepth measures gg_dta<- gg_minimal_vimp(varsel_iris) # Plot the gg_minimal_depth object plot(gg_dta) ## ------------------------------------------------------------ ## Regression example ## ------------------------------------------------------------ ## -------- air quality data rfsrc_airq <- rfsrc(Ozone ~ ., data = airquality, na.action = "na.impute") varsel_airq <- var.select(rfsrc_airq) # Get a data.frame containing error rates gg_dta<- gg_minimal_vimp(varsel_airq) # Plot the gg_minimal_vimp object plot(gg_dta) ## -------- Boston data data(Boston, package="MASS") rfsrc_boston <- randomForestSRC::rfsrc(medv~., Boston) varsel_boston <- var.select(rfsrc_boston) # Get a data.frame containing error rates gg_dta<- gg_minimal_vimp(varsel_boston) # Plot the gg_minimal_vimp object plot(gg_dta) ## -------- mtcars data rfsrc_mtcars <- rfsrc(mpg ~ ., data = mtcars) varsel_mtcars <- var.select(rfsrc_mtcars) # Get a data.frame containing error rates gg_dta<- gg_minimal_vimp(varsel_mtcars) # Plot the gg_minimal_vimp object plot(gg_dta) ## ------------------------------------------------------------ ## Survival example ## ------------------------------------------------------------ ## -------- veteran data ## randomized trial of two treatment regimens for lung cancer data(veteran, package = "randomForestSRC") rfsrc_veteran <- rfsrc(Surv(time, status) ~ ., data = veteran, ntree = 100) varsel_veteran <- var.select(rfsrc_veteran) gg_dta <- gg_minimal_vimp(varsel_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 ) varsel_pbc <- var.select(rfsrc_pbc) gg_dta <- gg_minimal_vimp(varsel_pbc) plot(gg_dta) ## End(Not run) ``` ## See Also `gg_minimal_vimp` `var.select`

plot.gg_minimal_vimp function