ests: Inference for Accuracy Measures based on Bootstrap

Description

compute the bootstrap standard error and confidence interval for the classification accuracy for a single classification model.

Usage

ests(y, d, acc="hum", level=0.95, method="multinom", B=250, balance=FALSE, …)

Arguments

The multinomial response vector with two, three or four categories. It can be factor or integer-valued.

The set of candidate markers, including one or more columns. Can be a data frame or a matrix; if the method is "prob", then d should be the probability matrix.

acc

Accuracy measure to be evaluated. Allow four choices: "hum", "pdi", "ccp" and "rsq".

level

The confidence level. Default value is 0.95.

method

Specifies what method is used to construct the classifier based on the marker set in d. Available option includes the following methods:"multinom": Multinomial Logistic Regression which is the default method, requiring R package nnet;"tree": Classification Tree method, requiring R package rpart; "svm": Support Vector Machine (C-classification and radial basis as default), requiring R package e1071; "lda": Linear Discriminant Analysis, requiring R package lda; "label": d is a label vector resulted from any external classification algorithm obtained by the user, should be encoded from 1; "prob": d is a probability matrix resulted from any external classification algorithm obtained by the user.

Number of bootstrap resamples.

balance

Logical, if TRUE, the class prevalence of the bootstrap sample is forced to be identical to the class prevalence of the original sample. Otherwise the prevalence of the bootstrap sample may be random.

…

Additional arguments in the chosen method's function.

Value

value

The specific value of the classification using a particular learning method on a set of marker(s).

The standard error of the value.

interval

The confidence interval of the value.

Details

The function returns the standard error and confidence interval for a single model evaluation method. All the other arguments are the same as the function hum.

Examples

Run this code

# NOT RUN {
str(iris)
data <- iris[, 1:4]
label <- iris[, 5]
ests(y = label, d = data,acc="hum",level=0.95,method = "multinom",B=10,trace=FALSE)

## $value
## [1] 0.9972

## $se
## [1] 0.002051529

## $interval
## [1] 0.9935662 1.0000000

ests(y = label, d = data,acc="pdi",level=0.85,method = "tree",B=10)

## $value
## [1] 0.9213333

## $se
## [1] 0.02148812

## $interval
## [1] 0.9019608 0.9629630

table(mtcars$carb)
for (i in (1:length(mtcars$carb))) {
  if (mtcars$carb[i] == 3 | mtcars$carb[i] == 6 | mtcars$carb[i] == 8) {
    mtcars$carb_new[i] = 9
  }else{
    mtcars$carb_new[i] = mtcars$carb[i]
  }
}
data <- data.matrix(mtcars[, c(1:2)])
mtcars$carb_new <- factor(mtcars$carb_new)
label <- mtcars$carb_new
str(mtcars)

ests(y = label, d = data,acc="hum",level=0.95,method = "multinom",trace=FALSE,B=5)

## $value
## [1] 0.2822857

## $se
## [1] 0.170327

## $interval
## [1] 0.2662500 0.4494643

# }