bayesF {analogue}R Documentation

Bayes factors

Description

Calculates Bayes factors or likelihood ratios of analogue and no-analogue results.

Usage

bayesF(x, prior = NULL)

Arguments

x an object of class “roc”.
prior numeric; the prior probabilities of analogue and no-analogue, provided as a vector of length 2 whose elements sum to 1. If not provided, the function will use the relative occurences of analogue and no analogue situations used to evaluate the ROC curve.

Details

LR(+), is the likelihood ratio of a positive test result, that the value of d assigns the sample to the group it belongs to. LR(-) is the likelihood ratio of a negative test result, that the value of d assigns the sample to the wrong group.

LR(+) is defined as LR(+) = TPF / FPF (or sensitivity / (1 - specificity)), and LR(-) is defined as LR(-) = FPF / TNF (or (1 - sensitivity) / specificity), in Henderson (1993).

The posterior probability of analogue given a dissimilarity is the LR(+) likelihood ratio values multiplied by the prior odds of analogue, for given values of the dissimilarity, and is then converted to a probability.

Value

A list with the followin components, some of which may be NULL depending on argument which:

pos Bayes factor or likelihood ratio of a positive event (analogue).
neg Bayes factor or likelihood ratio of anegative event (analogue).
posterior list with components pos and neg containing the posterior probabilities of positive and negative events, respectively.
prior list with components pos and neg containing the prior probabilities of positive and negative events, respectively.
roc.points vector of points at which the ROC curve was evaluated and for which Bayes factors and prior and posterior probabilities are available.
optimal numeric; the optimal dissimilarity, as assessed by the ROC curve.
object name of the object passed as argument x.

Author(s)

Gavin L. Simpson

References

Brown, C.D., and Davis, H.T. (2006) Receiver operating characteristics curves and related decision measures: A tutorial. Chemometrics and Intelligent Laboratory Systems 80, 24–38.

Gavin, D.G., Oswald, W.W., Wahl, E.R. and Williams, J.W. (2003) A statistical approach to evaluating distance metrics and analog assignments for pollen records. Quaternary Research 60, 356–367.

Henderson, A.R. (1993) Assessing test accuracy and its clinical consequences: a primer for receiver operating characteristic curve analysis. Annals of Clinical Biochemistry 30, 834–846.

See Also

roc and plot.bayesF.

Examples

## continue the example from ?roc
example(roc)

## calculate the Bayes factors of analogue and no-analogue
## (uses observed probabilities of analogue/no-analogue
swap.bayes <- bayesF(swap.roc)
swap.bayes

## plot the probability of analogue
plot(swap.bayes)

## calculate the Bayes factors of analogue and no-analogue
## with prior probabilities c(0.5, 0.05)
swap.bayes2 <- bayesF(swap.roc, prior = c(0.5, 0.05))
swap.bayes

## plot the probability of analogue
plot(swap.bayes2)

[Package analogue version 0.3-3 Index]