BEST (version 0.5.3)

postPriorOverlap: Overlap between posterior and prior probability distributions.

Description

Calculates and displays the overlap between a posterior distribution (as a vector of samples, typically from an MCMC process) and a prior distribution (as a vector of samples or as a function). Unidentifiable parameters will have high overlap: Gimenez et al (2009) suggest that overlap greater than 35% indicates weak identifiability.

Usage

postPriorOverlap(paramSampleVec, prior, ..., yaxt="n", ylab="",
           xlab="Parameter", main="", cex.lab=1.5, cex=1.4,
           xlim=range(paramSampleVec), breaks=NULL)

Arguments

paramSampleVec

a vector of samples drawn from the target distribution.

prior

either a vector of samples drawn from the prior distribution or the name for the density function of the distribution; standard R functions for this have a d- prefix, eg. dbeta. Arguments required by the function must be specified by their (abbreviated) names in the … argument; see the examples.

...

named parameters to be passed to prior when it is a function.

yaxt

a character which specifies the y axis type; the default, "n", suppresses plotting.

ylab

text to use as the label of the y axis.

xlab

text to use as the label of the x axis.

cex.lab

the magnification to be used for x and y labels relative to the current setting of cex

cex

a numerical value giving the amount by which plotting text and symbols should be magnified relative to the default

xlim

a vector of length 2 giving the limits for the x axis.

main

text to use as the main title of the plot

breaks

controls the histogram break points or the number of bars; see hist.

Value

Returns the overlap, the area lying under the lower of the two density curves.

References

Gimenez, Morgan and Brooks (2009) Weak identifiability in models for mark-recapture-recovery data. pp.1055-1068 in Thomson, Cooch and Conroy (eds) Modeling demographic processes in marked populations Springer

Examples

# NOT RUN {
# Generate some data
tst <- rbeta(1e6, 5, 7)

# check overlap with a Beta(0.2, 0.2) prior:
postPriorOverlap(tst, dbeta, shape1=0.2, shape2=0.2)

# check overlap with a Uniform(0, 1) prior:
postPriorOverlap(tst, runif(1e6))

# }