Package 'ROCket'

Calculate the AUC

Description

Calculate the AUC

Usage

auc(x, ...)

## S3 method for class ''function''
auc(x, ...)

## S3 method for class 'curve'
auc(x, lower, upper, n = 10000, ...)

## S3 method for class 'rkt_roc'
auc(x, exact = TRUE, ...)

Arguments

x	An R object.
...	Further parameters.
lower, upper	The limits of integration.
n	The number of integration points.
exact	Logical. If the exact formula should be used for calculating the AUC instead of numerical approximation.

Value

The area under the curve as a numeric value.

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Mann-Whitney U test

Description

Performs the Mann-Whitney U test with a normal approximation.

Usage

mwu.test(prep, alternative = c("two.sided", "less", "greater"), correct = TRUE)

Arguments

prep	A rkt_prep object.
alternative	The alternative hypothesis type. One of: "two.sided", "less", "greater".
correct	Logical. Whether to apply continuity correction.

Value

A list of the class "htest".

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Empirical estimate of the CDF

Description

Calculate an empirical cumulative distribution function based on a sample x and optionally a vector w of weights.

Usage

rkt_ecdf(x, w)

## S3 method for class 'rkt_ecdf'
print(x, ...)

## S3 method for class 'rkt_ecdf'
mean(x, ...)

## S3 method for class 'rkt_ecdf'
variance(x, ...)

## S3 method for class 'rkt_ecdf'
plot(x, ...)

Arguments

x	Numeric vector containing the sample. Alternatively, if w is supplied, distinct values within the sample. For S3 methods, a function of class rkt_ecdf.
w	Optional. Numeric vector containing the weights of each value in x.
...	Further parameters.

Details

The weights vector w can contain the counts of each distinct value in x, this is the most natural use case. In general the weights are describing the jumps of the final ecdf. Normalization is handled internally.

If x contains duplicates, corresponding values in w will be summed up. Only positive weights are allowed. Elements in x with non-positive weights will be ignored.

Value

A function of class rkt_ecdf.

Examples

require(ROCket)

plot(rkt_ecdf(rnorm(100)))
plot(rkt_ecdf(c(0, 1)))
plot(rkt_ecdf(c(0, 1), c(1, 10)))

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ROC points

Description

Calculate the ROC points for all meaningful cutoff values based on predicted scores.

Usage

rkt_prep(scores, positives, negatives = totals - positives, totals = 1)

## S3 method for class 'rkt_prep'
print(x, ...)

## S3 method for class 'rkt_prep'
plot(x, ...)

Arguments

scores	Numeric vector containing the predicted scores.
positives	Numeric vector of the same length as scores. The number of positive entities associated with each score. If data is not aggregated, a vector of 0's and 1's.
negatives	Similar to positives. Defaults to totals - positives.
totals	How many times each score was predicted. Defaults to 1 (assuming data is not aggregated). If any value in positives is greater than 1 (aggregated data), totals must be a vector. Not needed if negatives is supplied.
x	An environment of class rkt_prep for S3 methods.
...	Further parameters.

Details

In a situation where many of the predicted scores have the same value it might be easier and faster to use aggregated data.

Value

An environment of class rkt_prep.

Examples

require(ROCket)

plot(rkt_prep(1:4, c(0, 1, 0, 1)))
plot(rkt_prep(1:4, c(0, 1000, 0, 1000), totals = 1000))
plot(rkt_prep(1:4, c(100, 200, 300, 400), totals = c(1000, 800, 600, 400)))

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Empirical estimate of the ROC

Description

Calculate the empirical estimate of the ROC from raw sample or aggregated data.

Usage

rkt_roc(prep, method = 1)

## S3 method for class 'rkt_roc'
print(x, ...)

## S3 method for class 'rkt_roc'
plot(x, ...)

Arguments

prep	A rkt_prep object.
method	A number specifying the type of ROC estimate. Possible values can be viewed with show_methods().
x	An object of class rkt_roc.
...	Further parameters passed to plot and lines

Value

An object of class rkt_roc, i.e. a function or a list of two functions (for method = 1).

Examples

require(ROCket)

scores <- c(1, 2, 3, 4)
positives <- c(0, 1, 0, 1)
prep <- rkt_prep(scores, positives)

roc1 <- rkt_roc(prep, method = 1)
roc2 <- rkt_roc(prep, method = 2)
roc3 <- rkt_roc(prep, method = 3)

plot(roc1)
plot(roc2)
plot(roc3)

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Available ROC estimation methods

Description

Show the implemented ROC estimation methods.

Usage

show_methods()

Value

A data.table containing the number and a short description of each implemented method.

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Sample Variance

Description

Sample Variance

Usage

variance(x, ...)

## Default S3 method:
variance(x, ...)

Arguments

x	An R object.
...	Further parameters.

Value

The (biased) sample variance as a numeric value.

See Also

variance.rkt_ecdf, var

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