cv.varpro.RdSelects Cutoff Value for Variable Priority (VarPro).
cv.varpro(f, data, nvar = 30, ntree = 150,
local.std = TRUE, zcut = seq(0.1, 2, length = 50), nblocks = 10,
split.weight = TRUE, split.weight.method = NULL, sparse = TRUE,
nodesize = NULL, max.rules.tree = 150, max.tree = min(150, ntree),
papply = mclapply, verbose = FALSE, seed = NULL,
fast = FALSE, crps = FALSE, ...)Formula describing the model to be fit.
Training data set (a data frame).
Upper limit to number of variables returned.
Number of trees to grow.
Use locally standardized importance?
Grid of positive pre-specified cutoff values.
This is like the number of folds used in M-fold validation.
Use guided tree-splitting? Features selected to split a tree node are selected with probability according to a split-weight value, the latter by default being acquired in a preliminary lasso+tree step.
Optional character string (or vector) specifying the method used to generate the split-weights. If not specified, defaults to a lasso+tree step.
Use sparse split-weights?
Nodesize of trees. If not specified, value is set using an internal function optimized for sample size and dimension.
Maximum number of rules per tree.
Maximum number of trees used for extracting rules.
Use mclapply or lapply.
Print verbose output?
Seed for repeatability.
Use fast random forests, rfsrc.fast, in place of rfsrc?
Improves speed but may be less accurate.
Use CRPS (continuous rank probability score) for estimating survival performance (default is to use Harrel's C-index)? Only applies to survival families.
Further arguments to be passed to varpro.
Applies VarPro and then selects from a grid of cutoff values the cutoff value for identifying variables that minimizes out-of-sample performance (error rate) of a random forest where the forest is fit to the top variables identified by the given cutoff value.
Additionally, a "conservative" and "liberal" list of variables are returned using a one standard deviation rule. The conservative list comprises variables using the largest cutoff with error rate within one standard deviation from the optimal cutoff error rate, whereas the liberal list uses the smallest cutoff value with error rate within one standard deviation of the optimal cutoff error rate.
For class imbalanced settings (two class problems where relative frequency of labels is skewed towards one class) the code automatically switches to random forest quantile classification (RFQ; see O'Brien and Ishwaran, 2019) under the gmean (geometric mean) performance metric.
Output containing importance values for the optimized cutoff value. A conservative and liberal list of variables is also returned.
Note that importance values are returned in terms of the original features
and not their hot-encodings. For importance in terms of
hot-encodings, use the built-in wrapper get.vimp (see
example below).
Lu, M. and Ishwaran, H. (2024). Model-independent variable selection via the rule-based variable priority. arXiv e-prints, pp.arXiv-2409.
O'Brien R. and Ishwaran H. (2019). A random forests quantile classifier for class imbalanced data. Pattern Recognition, 90, 232-249.
# \donttest{
## ------------------------------------------------------------
## van de Vijver microarray breast cancer survival data
## high dimensional example
## ------------------------------------------------------------
data(vdv, package = "randomForestSRC")
o <- cv.varpro(Surv(Time, Censoring) ~ ., vdv)
print(o)
## ------------------------------------------------------------
## boston housing
## ------------------------------------------------------------
data(BostonHousing, package = "mlbench")
print(cv.varpro(medv~., BostonHousing))
## ------------------------------------------------------------
## boston housing - original/hot-encoded vimp
## ------------------------------------------------------------
## load the data
data(BostonHousing, package = "mlbench")
## convert some of the features to factors
Boston <- BostonHousing
Boston$zn <- factor(Boston$zn)
Boston$chas <- factor(Boston$chas)
Boston$lstat <- factor(round(0.2 * Boston$lstat))
Boston$nox <- factor(round(20 * Boston$nox))
Boston$rm <- factor(round(Boston$rm))
## make cv call
o <-cv.varpro(medv~., Boston)
print(o)
## importance original variables (default)
print(get.orgvimp(o, pretty = FALSE))
## importance for hot-encoded variables
print(get.vimp(o, pretty = FALSE))
## ------------------------------------------------------------
## multivariate regression example: boston housing
## vimp is collapsed across the outcomes
## ------------------------------------------------------------
data(BostonHousing, package = "mlbench")
print(cv.varpro(cbind(lstat, nox) ~., BostonHousing))
## ------------------------------------------------------------
## iris
## ------------------------------------------------------------
print(cv.varpro(Species~., iris))
## ------------------------------------------------------------
## friedman 1
## ------------------------------------------------------------
print(cv.varpro(y~., data.frame(mlbench::mlbench.friedman1(1000))))
##----------------------------------------------------------------
## class imbalanced problem
##
## - simulation example using the caret R-package
## - creates imbalanced data by randomly sampling the class 1 values
##
##----------------------------------------------------------------
if (library("caret", logical.return = TRUE)) {
## experimental settings
n <- 5000
q <- 20
ir <- 6
f <- as.formula(Class ~ .)
## simulate the data, create minority class data
d <- twoClassSim(n, linearVars = 15, noiseVars = q)
d$Class <- factor(as.numeric(d$Class) - 1)
idx.0 <- which(d$Class == 0)
idx.1 <- sample(which(d$Class == 1), sum(d$Class == 1) / ir , replace = FALSE)
d <- d[c(idx.0,idx.1),, drop = FALSE]
d <- d[sample(1:nrow(d)), ]
## cv.varpro call
print(cv.varpro(f, d))
}
## ------------------------------------------------------------
## pbc survival with rmst vector
## note that vimp is collapsed across the rmst values
## similar to mv-regression
## ------------------------------------------------------------
data(pbc, package = "randomForestSRC")
print(cv.varpro(Surv(days, status)~., pbc, rmst = c(500, 1000)))
## ------------------------------------------------------------
## peak VO2 with cutoff selected using fast option
## (a) C-index (default) (b) CRPS performance metric
## ------------------------------------------------------------
data(peakVO2, package = "randomForestSRC")
f <- as.formula(Surv(ttodead, died)~.)
## Harrel's C-index (default)
print(cv.varpro(f, peakVO2, ntree = 100, fast = TRUE))
## Harrel's C-index with smaller bootstrap
print(cv.varpro(f, peakVO2, ntree = 100, fast = TRUE, sampsize = 100))
## CRPS with smaller bootstrap
print(cv.varpro(f, peakVO2, crps = TRUE, ntree = 100, fast = TRUE, sampsize = 100))
## ------------------------------------------------------------
## largish data set: illustrates various options to speed up calculations
## ------------------------------------------------------------
## roughly impute the data
data(housing, package = "randomForestSRC")
housing2 <- randomForestSRC:::get.na.roughfix(housing)
## use bigger nodesize
print(cv.varpro(SalePrice~., housing2, fast = TRUE, ntree = 50, nodesize = 150))
## use smaller bootstrap
print(cv.varpro(SalePrice~., housing2, fast = TRUE, ntree = 50, nodesize = 150, sampsize = 250))
# }