confMat: Confusion Matrix

Description Usage Arguments Details Value Note Author(s) See Also Examples

View source: R/confMat.R

Description

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

Usage

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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 - FN \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

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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 June 26, 2018, 5:02 p.m.