stepBF is a function to determine the overall best fitting number of shifts using Bayes factor evidence.
stepBF(BFmat, step.size = 20, expectedNumberOfShifts = 1, inputType = "matrix")
square Bayes factor matrix or a named vector of posterior probabilities
how much Bayes factor support is required to accept a more complex model, see Details
expected number of shifts under the prior (only needed for
describes the input:
stepBF takes either a square Bayes factor matrix (such as output by
computeBayesFactors) or a named
vector of posterior probabilities. If posterior probabilities are supplied, the model prior
expectedNumberOfShifts) must also be provided.
If the input is a Bayes factor matrix, specify
inputType = 'matrix', otherwise if the input is
a named vector of posterior probabilities, specify
inputType = 'postProb'.
step.size argument is how much Bayes factor support is needed to accept a more complex model.
By default, this value is 1, so any more complex model that has a better Bayes factor than the previous model
will be accepted. Increasing the step size greatly reduces the Type I error at the cost of inflating Type II
error. So, with increasing step.size, you will infer fewer shifts.
a list of 3 items: the number of shifts for the best model, the number of shifts for the second best model, and the Bayes factor support for the best model over the second best.
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data(mcmc.whales) # remove 10% burnin mcmc.whales <- mcmc.whales[floor(0.1 * nrow(mcmc.whales)):nrow(mcmc.whales), ] # from a square matrix of Bayes factor values (inputType = 'matrix') bfmat <- computeBayesFactors(mcmc.whales, expectedNumberOfShifts = 1, burnin = 0) stepBF(bfmat, step.size = 1, inputType = 'matrix') # or from a vector of posterior probabilities (inputType = 'postProb') postProb <- table(mcmc.whales$N_shifts) / nrow(mcmc.whales) stepBF(postProb, step.size = 1, inputType = 'postProb')
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