BinaryClass {RHclust}

Binary Classification

Description

A confusion matrix but allows for anaylsis of non-equal level data classifications.

Usage

BinaryClass(x)

Arguments

x

Can be a data frame dimensions at least 2 rows and 2 columns meant to represent observed and predicted values where the observed (true) values are in the first column and predicted columns in the second column.

Details

BinaryClass() is similar to a confusion matrix with binary classification outputs. The true positive values per column are identified based on the maximum number of assignments per category.

Value

Table

the results of 'table()' on 'x'

Accuracy

overall accuracy of classification

CI

confidence interval of overall accuracy using Clopper-Pearson Interval

Group Measures

the sensitivity, specificity, positive predictive value, negative predictive value, prevelance detection rate, detection prevalence, and balanced accuracy for each class

Author(s)

[e-mail]

Examples

# Basic example
true = c(rep(1,5), rep(2,5), rep(3,5), rep(4,5))
pred = c(rep(1,4),4,rep(2,5),2,rep(3,4),1,rep(4,4))
df = cbind(true,pred)
BinaryClass(df)


true = c(rep(1,5), rep(2,5), rep(3,5), rep(4,5))
pred = c(rep(1,5),rep(2,5),rep(3,10))
df = cbind(true,pred)
BinaryClass(df)



sd = SimData(k = c(10,40,50))
out = VIP(sd, v = 3, optimize = 'elbow', nstart = 5)
df = out$`BC Test`
BinaryClass(df)


## Looping through different clusters

sd = SimData(seed = 1, gene = 1)
acc = NULL
for (i in 1:5){
 out = VIP(sd, v = i, optimize = 'off', nstart = 5)
 acc[i] = BinaryClass(out$`BC Test`)$Accuracy
}

plot(acc, type = 'b', main = 'Accuracy Comparison', xlab = 'Clusters', ylab = 'Acc')