bayesF {analogue}  R Documentation 
Calculates Bayes factors or likelihood ratios of analogue and noanalogue 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", abline.lty = "dashed", ...)
x 
for 
prior 
numeric; the prior probabilities of analogue and noanalogue, 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 
xlab,ylab 
the x and yaxis 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. 
abline.lty 
Line type for indicator of optimal ROC dissimilarity
threshold. See 
... 
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 nonanalogue events. Each components is a list
with two components; 
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

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
.
## load the example data data(swapdiat, swappH, rlgh) ## merge training and test set on columns dat < join(swapdiat, rlgh, verbose = TRUE) ## extract the merged data sets and convert to proportions swapdiat < dat[[1]] / 100 rlgh < dat[[2]] / 100 ## fit an analogue matching (AM) model using the squared chord distance ## measure  need to keep the training set dissimilarities swap.ana < analog(swapdiat, rlgh, method = "SQchord", keep.train = TRUE) ## fit the ROC curve to the SWAP diatom data using the AM results ## Generate a grouping for the SWAP lakes METHOD < if (getRversion() < "3.1.0") {"ward"} else {"ward.D"} clust < hclust(as.dist(swap.ana$train), method = METHOD) grps < cutree(clust, 12) ## fit the ROC curve swap.roc < roc(swap.ana, groups = grps) swap.roc ## calculate the Bayes factors of analogue and noanalogue ## (uses observed probabilities of analogue/noanalogue swap.bayes < bayesF(swap.roc) swap.bayes ## plot the probability of analogue plot(swap.bayes) ## Not run: ## calculate the Bayes factors of analogue and noanalogue ## 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)