testGlmBi {BAGofT}

Testing binomial regressions

Description

testGlmBi specifies a binomial regression as the classifier to test. It returns a function that can be taken as the input of ‘testModel’.

Usage

testGlmBi(formula, link)

Arguments

formula

an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to test.

link

a specification for the model link function. Can be one of "logit", "probit", "cloglog".

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)