View source: R/accuracyMeasures.R
accuracyMeasures  R Documentation 
The function calculates various prediction accuracy statistics for predictions of binary or quantitative (continuous) responses. For binary classification, the function calculates the error rate, accuracy, sensitivity, specificity, positive predictive value, and other accuracy measures. For quantitative prediction, the function calculates correlation, Rsquared, error measures, and the Cindex.
accuracyMeasures(
predicted,
observed = NULL,
type = c("auto", "binary", "quantitative"),
levels = if (isTRUE(all.equal(dim(predicted), c(2,2)))) colnames(predicted)
else if (is.factor(predicted))
sort(unique(c(as.character(predicted), as.character(observed))))
else sort(unique(c(observed, predicted))),
negativeLevel = levels[2],
positiveLevel = levels[1] )
predicted 
either a a 2x2 confusion matrix (table) whose entries contain nonnegative
integers, or a vector of predicted values. Predicted values can be binary or quantitative (see 
observed 
if 
type 
character string specifying the type of the prediction problem (i.e., values in the

levels 
a 2element vector specifying the two levels of binary variables. Only used if 
negativeLevel 
the binary value (level) that corresponds to the negative outcome. Note that the
default is the second of the sorted levels (for example, if levels are 1,2, the default negative level is
2). Only used if 
positiveLevel 
the binary value (level) that corresponds to the positive outcome. Note that the
default is the second of the sorted levels (for example, if levels are 1,2, the default negative level is
2). Only used if 
The rows of the 2x2 table tab must correspond to a test (or predicted) outcome and the columns to a true outcome ("gold standard"). A table that relates a predicted outcome to a true test outcome is also known as confusion matrix. Warning: To correctly calculate sensitivity and specificity, the positive and negative outcome must be properly specified so they can be matched to the appropriate rows and columns in the confusion table.
Interchanging the negative and positive levels swaps the estimates of the sensitivity and specificity
but has no effect on the error rate or
accuracy. Specifically, denote by pos
the index of the positive level in the confusion table, and by
neg
th eindex of the negative level in the confusion table.
The function then defines number of true positives=TP=tab[pos, pos], no.false positives
=FP=tab[pos, neg], no.false negatives=FN=tab[neg, pos], no.true negatives=TN=tab[neg, neg].
Then Specificity= TN/(FP+TN)
Sensitivity= TP/(TP+FN) NegativePredictiveValue= TN/(FN + TN) PositivePredictiveValue= TP/(TP + FP)
FalsePositiveRate = 1Specificity FalseNegativeRate = 1Sensitivity Power = Sensitivity
LikelihoodRatioPositive = Sensitivity / (1Specificity) LikelihoodRatioNegative =
(1Sensitivity)/Specificity. The naive error rate is the error rate of a constant (naive) predictor that
assigns the same outcome to all samples. The prediction of the naive predictor equals the most frequenly
observed outcome. Example: Assume you want to predict disease status and 70 percent of the observed samples
have the disease. Then the naive predictor has an error rate of 30 percent (since it only misclassifies 30
percent of the healthy individuals).
Data frame with two columns:
Measure 
this column contais character strings that specify name of the accuracy measure. 
Value 
this column contains the numeric estimates of the corresponding accuracy measures. 
Steve Horvath and Peter Langfelder
http://en.wikipedia.org/wiki/Sensitivity_and_specificity
m=100
trueOutcome=sample( c(1,2),m,replace=TRUE)
predictedOutcome=trueOutcome
# now we noise half of the entries of the predicted outcome
predictedOutcome[ 1:(m/2)] =sample(predictedOutcome[ 1:(m/2)] )
tab=table(predictedOutcome, trueOutcome)
accuracyMeasures(tab)
# Same result:
accuracyMeasures(predictedOutcome, trueOutcome)
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