yager (version 0.1.0)

grnn.x_imp: Derive the importance of a predictor used in the GRNN

Description

The function grnn.x_imp derives the importance of a predictor used in the GRNN by using the loss of predictability after eliminating the impact of the predictor in interest.

Usage

grnn.x_imp(net, i, class = FALSE)

Arguments

net

The GRNN object generated by grnn.fit()

i

The ith predictor in the GRNN

class

TRUE or FALSE, whether it is for the classification or not

Value

A vector with the variable name and two values of importance measurements, namely "imp1" and "imp2". The "imp1" measures the loss of predictability after replacing all values of the predictor with its mean. The "imp2" measures the loss of predictability after dropping the predictor from the GRNN.

See Also

grnn.x_pfi

Examples

# NOT RUN {
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
grnn.x_imp(net = gnet, 1)
# }