bayesF {analogue} | R Documentation |
Calculates Bayes factors or likelihood ratios of analogue and no-analogue results.
bayesF(x, prior = rep(0.5, 2)) ## S3 method for class 'bayesF': plot(x, group = "all", xlab = NULL, ylab = "Pr (A+ | d)", col = "red", abline.col = "lightgrey", ...)
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. |
group |
character vector of length 1 giving the name of the group
to plot, or "all" to plot all groups in x . |
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. |
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).
For plot.bayesF
a plot on the currently active device.
For bayesF
, a list containing the results of computing Bayes
factors for each group in x
. Each component of this list is
itself a list with the following components:
bayesF, posterior.odds, posterior.probs, prior.prob |
Bayes
factors, posterior odds and probabilities and prior probabilities of
true analogue and true non-analogue events. Each components is a list
with two components; pos (for true analogue events) and
neg (for true non-analogue events). The components of
prior.prob are vectors of length 1, whilst components of the
other lists are numeric vectors. |
roc.points |
numeric; the points at which the ROC curve was evaluated. |
optimal |
numeric; the optimal dissimilarity as assessed by the ROC curve. |
max.roc |
numeric; the position along the ROC curve at which the
slope of the ROC curve is maximal. This is the index of this point
on the curve, and can be used to extract the element of
bayesF , posterior.odds and posterior.probs for
the optimal dissimilarity. |
Gavin L. Simpson
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.
roc
and plot.bayesF
.
## 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)