RRBoost (version 0.1)

cal_imp_func: Variable importance scores for the robust boosting algorithm RRBoost

Description

This function calculates variable importance scores for a previously computed RRBoost fit.

Usage

cal_imp_func(model, x_val, y_val, trace = FALSE)

Arguments

model

an object returned by Boost

x_val

predictor matrix for validation data (matrix/dataframe)

y_val

response vector for validation data (vector/dataframe)

trace

logical indicating whether to print the variable under calculation for monitoring progress (defaults to FALSE)

Value

a vector of permutation variable importance scores (one per explanatory variable)

Details

This function computes permutation variable importance scores given an object returned by Boost and a validation data set.

Examples

# NOT RUN {
data(airfoil)
n <- nrow(airfoil)
n0 <- floor( 0.2 * n )
set.seed(123)
idx_test <- sample(n, n0)
idx_train <- sample((1:n)[-idx_test], floor( 0.6 * n ) )
idx_val <- (1:n)[ -c(idx_test, idx_train) ]
xx <- airfoil[, -6]
yy <- airfoil$y
xtrain <- xx[ idx_train, ]
ytrain <- yy[ idx_train ]
xval <- xx[ idx_val, ]
yval <- yy[ idx_val ]
xtest <- xx[ idx_test, ]
ytest <- yy[ idx_test ]
model = Boost(x_train = xtrain, y_train = ytrain,
     x_val = xval, y_val = yval,
     type = "RRBoost", error = "rmse",
     y_init = "LADTree", max_depth = 1, niter = 1000,
     control = Boost.control(max_depth_init = 2,
           min_leaf_size_init = 20, save_tree = TRUE,
           make_prediction =  FALSE, cal_imp = FALSE))
var_importance <-  cal_imp_func(model, x_val = xval, y_val= yval)
# }
# NOT RUN {
# }