cal_imp_func {RRBoost}

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)

Details

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

Value

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

Author(s)

Xiaomeng Ju, [e-mail]

Examples

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)