bayesF {analogue} | R Documentation |
Calculates Bayes factors or likelihood ratios of analogue and no-analogue results.
bayesF(x, prior = NULL)
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. |
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.
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 . |
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) ## 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)