bayesF {analogue}R Documentation

Bayes factors

Description

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

Usage

bayesF(x, prior = NULL)

## S3 method for class 'bayesF':
plot(x, xlab = NULL, ylab = "Pr (A+ | d)",
        col = "red", abline.col = "lightgrey", ...)

Arguments

x for bayesF an object of class roc. For the plot method, an object of class bayesF, usually the result of a call to bayesF.
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.
xlab,ylab the x- and y-axis labels for the plot.
col colour of the line used to draw the posterior probability.
abline.col colour of the vertical line drawn to indicate the optimal dissimilarity determined from the ROC curve.
... other plot arguments passed to plotting functions. Currently ignored.

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.

The plotting function currently only draws the posterior probability of analogue based on the Bayes factor or likelihood ratio of a positive event (analogue).

Value

For plot.bayesF a plot on the currently active device.
For bayesF, a list with the following components:

pos Bayes factor or likelihood ratio of a positive event (analogue).
neg Bayes factor or likelihood ratio of a negative 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)

## Not run: 
## 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)
## End(Not run)

[Package analogue version 0.5-1 Index]