Calculates a cross-tabulation of observed and predicted classes with associated statistics.

`confusionMatrix(data, ...)`# S3 method for default
confusionMatrix(data, reference, positive = NULL,
dnn = c("Prediction", "Reference"), prevalence = NULL,
mode = "sens_spec", ...)

# S3 method for table
confusionMatrix(data, positive = NULL,
prevalence = NULL, mode = "sens_spec", ...)

data

a factor of predicted classes (for the default method) or an
object of class `table`

.

…

options to be passed to `table`

. NOTE: do not include
`dnn`

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 (if that makes sense for your data). If
there are only two factor levels, the first level will be used as the
"positive" result. When `mode = "prec_recall"`

, `positive`

is the
same value used for `relevant`

for functions `precision`

,
`recall`

, and `F_meas.table`

.

dnn

a character vector of dimnames for the table

prevalence

a numeric value or matrix for the rate of the "positive"
class of the data. When `data`

has two levels, `prevalence`

should
be a single numeric value. Otherwise, it should be a vector of numeric
values with elements for each class. The vector should have names
corresponding to the classes.

mode

a single character string either "sens_spec", "prec_recall", or "everything"

a list with elements

the results of `table`

on
`data`

and `reference`

the positive result level

a numeric vector with overall accuracy and Kappa statistic values

the sensitivity, specificity, positive predictive
value, negative predictive value, precision, recall, F1, prevalence,
detection rate, detection prevalence and balanced accuracy for each class.
For two class systems, this is calculated once using the `positive`

argument

The functions requires that the factors have exactly the same levels.

For two class problems, the sensitivity, specificity, positive predictive
value and negative predictive value is calculated using the `positive`

argument. Also, the prevalence of the "event" is computed from the data
(unless passed in as an argument), the detection rate (the rate of true
events also predicted to be events) and the detection prevalence (the
prevalence of predicted events).

Suppose a 2x2 table with notation

Reference | ||

Predicted | Event | No Event |

Event | A | B |

No Event | C | D |

The formulas used here are: $$Sensitivity = A/(A+C)$$ $$Specificity = D/(B+D)$$ $$Prevalence = (A+C)/(A+B+C+D)$$ $$PPV = (sensitivity * prevalence)/((sensitivity*prevalence) + ((1-specificity)*(1-prevalence)))$$ $$NPV = (specificity * (1-prevalence))/(((1-sensitivity)*prevalence) + ((specificity)*(1-prevalence)))$$ $$Detection Rate = A/(A+B+C+D)$$ $$Detection Prevalence = (A+B)/(A+B+C+D)$$ $$Balanced Accuracy = (sensitivity+specificity)/2$$

$$Precision = A/(A+B)$$ $$Recall = A/(A+C)$$ $$F1 = (1+beta^2)*precision*recall/((beta^2 * precision)+recall)$$

where `beta = 1`

for this function.

See the references for discussions of the first five formulas.

For more than two classes, these results are calculated comparing each factor level to the remaining levels (i.e. a "one versus all" approach).

The overall accuracy and unweighted Kappa statistic are calculated. A
p-value from McNemar's test is also computed using
`mcnemar.test`

(which can produce `NA`

values with
sparse tables).

The overall accuracy rate is computed along with a 95 percent confidence
interval for this rate (using `binom.test`

) and a
one-sided test to see if the accuracy is better than the "no information
rate," which is taken to be the largest class percentage in the data.

Kuhn, M. (2008), ``Building predictive models in R using the
caret package, '' *Journal of Statistical Software*,
(http://www.jstatsoft.org/article/view/v028i05/v28i05.pdf).

Altman, D.G., Bland, J.M. (1994) ``Diagnostic tests 1: sensitivity and
specificity,'' *British Medical Journal*, vol 308, 1552.

Altman, D.G., Bland, J.M. (1994) ``Diagnostic tests 2: predictive values,''
*British Medical Journal*, vol 309, 102.

Velez, D.R., et. al. (2008) ``A balanced accuracy function for epistasis
modeling in imbalanced datasets using multifactor dimensionality
reduction.,'' *Genetic Epidemiology*, vol 4, 306.

`as.table.confusionMatrix`

,
`as.matrix.confusionMatrix`

, `sensitivity`

,
`specificity`

, `posPredValue`

,
`negPredValue`

, `print.confusionMatrix`

,
`binom.test`

# NOT RUN { ################### ## 2 class example lvs <- c("normal", "abnormal") truth <- factor(rep(lvs, times = c(86, 258)), levels = rev(lvs)) pred <- factor( c( rep(lvs, times = c(54, 32)), rep(lvs, times = c(27, 231))), levels = rev(lvs)) xtab <- table(pred, truth) confusionMatrix(xtab) confusionMatrix(pred, truth) confusionMatrix(xtab, prevalence = 0.25) ################### ## 3 class example confusionMatrix(iris$Species, sample(iris$Species)) newPrior <- c(.05, .8, .15) names(newPrior) <- levels(iris$Species) confusionMatrix(iris$Species, sample(iris$Species)) # }