confMat: Confusion Matrix

In GMDH2: Binary Classification via GMDH-Type Neural Network Algorithms

Confusion Matrix

Description

confMat constructs a 2\times2 confusion matrix and returns some statistics related to confusion matrix.

Usage

confMat(data, ...)

## Default S3 method:
confMat(data, reference, positive = NULL, verbose = TRUE, ...)

## S3 method for class 'table'
confMat(data, positive = NULL, verbose = TRUE, ...)

Arguments

data	a factor of predicted classes (for the default method) or an object of class table.
...	option to be passed to table. Note: do not include reference here.
reference	a factor of classes to be used as the true results.
positive	an optional character string for the factor level that corresponds to a "positive" result.
verbose	a logical for printing output to R console.

Details

The confMat function requires that the factors have exactly the same levels. The function constructs 2\times2 confusion matrix and calculates accuracy, no information rate (NIR), unweighted Kappa statistic, Matthews correlation coefficient, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), prevalence, balanced accuracy, youden index, detection rate, detection prevalence, precision, recall and F1 measure.

Suppose a 2\times2 table with notation

	Reference
Predicted	Event	No Event
Event	TP	FP
No Event	FN	TN

TP is the number of true positives, FP is the number of false positives, FN is the number of false negatives and TN is the number of true negatives.

Accuracy = \frac{TP + TN}{TP + FP + FN + TN}

NIR = max(Prevalence, 1 - Prevalence)

Kappa = \frac{Accuracy - \frac{(TP + FP)(TP + FN)+(FN + TN)(FP + TN)}{(TP + FP + FN + TN)^2}}{1 - \frac{(TP + FP)(TP + FN)+(FN + TN)(FP + TN)}{(TP + FP + FN + TN)^2}}

MCC = \frac{TP \times TN - FP \times FN}{√{(TP+FP) \times (FN+TN) \times (TP+FN) \times (FP+TN)}}

Sensitivity = \frac{TP}{TP+FN}

Specificity = \frac{TN}{TN+FP}

PPV = \frac{TP}{TP+FP}

NPV = \frac{TN}{TN+FN}

Prevalence = \frac{TP + FN}{TP + FP + FN + TN}

Balanced\ accuracy = \frac{Sensitivity + Specificity}{2}

Youden\ index = Sensitivity + Specificity -1

Detection\ rate = \frac{TP}{TP + FP + FN + TN}

Detection\ prevalence = \frac{TP+FP}{TP + FP + FN + TN}

Precision = \frac{TP}{TP + FP}

Recall = \frac{TP}{TP+FN}

F1 = \frac{2}{\frac{1}{Recall}+\frac{1}{Precision}}

Value

Returns a list containing following elements:

table	confusion matrix
accuracy	accuracy
NIR	no information rate
kappa	unweighted kappa
MCC	Matthews correlation coefficient
sensitivity	sensitivity
specificity	specificity
PPV	positive predictive value
NPV	negative predictive value
prevalence	prevalence
baccuracy	balanced accuracy
youden	youden index
detectRate	detection rate
detectPrev	detection prevalence
precision	precision
recall	recall
F1	F1 measure
all	returns a matrix containing all statistics

Note

If the factors, reference and data, have the same levels, but in the incorrect order, the confMat will reorder data with the order of reference.

Author(s)

Osman Dag

See Also

confusionMatrix

Examples

library(GMDH2)

library(mlbench)
data(BreastCancer)

data <- BreastCancer

# to obtain complete observations
completeObs <- complete.cases(data)
data <- data[completeObs,]

x <- data.matrix(data[,2:10])
y <- data[,11]

seed <- 12345
set.seed(seed)
nobs <- length(y)

# to split train, validation and test sets

indices <- sample(1:nobs)

ntrain <- round(nobs*0.6,0)
nvalid <- round(nobs*0.2,0)
ntest <- nobs-(ntrain+nvalid)

train.indices <- sort(indices[1:ntrain])
valid.indices <- sort(indices[(ntrain+1):(ntrain+nvalid)])
test.indices <- sort(indices[(ntrain+nvalid+1):nobs])


x.train <- x[train.indices,]
y.train <- y[train.indices]

x.valid <- x[valid.indices,]
y.valid <- y[valid.indices]

x.test <- x[test.indices,]
y.test <- y[test.indices]


set.seed(seed)
# to construct model via dce-GMDH algorithm
model <- dceGMDH(x.train, y.train, x.valid, y.valid)

# to obtain predicted classes for test set
y.test_pred <- predict(model, x.test, type = "class")

# to obtain confusion matrix and some statistics for test set
confMat(y.test_pred, y.test, positive = "malignant")

# to obtain statistics from table
result <- table(y.test_pred, y.test)
confMat(result, positive = "malignant")

GMDH2 documentation built on Oct. 26, 2022, 5:06 p.m.