VarImp {BAGofT} R Documentation

## Variable Importance

### Description

VarImp averages the variable importance generated by "parRF" from different splittings.

### Usage

VarImp(TestRes)


### Arguments

 TestRes an output from "BAGofT".

### Value

 Var.imp the averaged variable importance from multiple splittings. A high variable importance indicates that the corresponding covariate is likely to be related to the possible underfitting. When the number of partition covariates is larger than 5, output the result of 5 covariates with the largest averaged variable importance. preVar.imp the averaged variable importance for all of the variables. Output only when the number of partition covariates is larger than 5.

### References

Zhang, Ding and Yang (2021) "Is a Classification Procedure Good Enough?-A Goodness-of-Fit Assessment Tool for Classification Learning" arXiv preprint arXiv:1911.03063v2 (2021).

### Examples

## Not run:
###################################################
# Generate a sample dataset.
###################################################
# set the random seed
set.seed(20)
# set the number of observations
n <- 200

# generate covariates data
x1dat <- runif(n, -3, 3)
x2dat <- rnorm(n, 0, 1)
x3dat <- rchisq(n, 4)

# set coefficients
beta1 <- 1
beta2 <- 1
beta3 <- 1

# calculate the linear predictor data
lindat <- x1dat * beta1 + x2dat * beta2 + x3dat * beta3
# calculate the probabilities by inverse logit link
pdat <- 1/(1 + exp(-lindat))

# generate the response data
ydat <- sapply(pdat, function(x) stats :: rbinom(1, 1, x))

# generate the dataset
dat <- data.frame(y = ydat, x1 = x1dat, x2 = x2dat,
x3 = x3dat)

###################################################
# Obtain the testing result
###################################################
# Test a logistic regression that misses 'x3'. The partition
# variables are 'x1', 'x2', and 'x3'.
testRes <- BAGofT(testModel =testGlmBi(formula = y ~ x1 + x2 , link = "logit"),
parFun = parRF(parVar = c("x1", "x2", "x3")),
data = dat)

# the bootstrap p-value is 0. Therefore, the test is rejected
print(testRes\$p.value)

# the variable importance from the adaptive partion shows that x3 is likely
# to be the reason for the overfitting (,which is correct since the formula
# fm misses the x3).
print(VarImp(testRes))

## End(Not run)


[Package BAGofT version 1.0.0 Index]