Description Usage Arguments Methods Value Author(s) References See Also Examples
Compute the posterior probability distribution of the population size using a discrete uniform prior and a binomial likelihood (DUP method). When applicable, an approximation using a Gamma prior and a Poisson likelihood is used instead (GP method, see Clough et al).
1 | computePosterior(object, n1, n2, replacement = FALSE, b, alg = "DUP")
|
object |
An object of class 'Counts' |
n1 |
Left endpoint of the prior support interval (Optional). If not provided and total counts are not zero, computed using the maximum likelihood estimate (mle) of the population size as 0.5 * mle |
n2 |
Right endpoint of the prior support interval (Optional). If not provided and total counts are not zero, computed using the maximum likelihood estimate (mle) of the population size as 2 * mle |
replacement |
Whether sampling has been performed with replacement. Default to FALSE. |
b |
Prior rate parameter of the gamma distribution used to compute the posterior with Clough. Default is 1e-10. |
alg |
Algorithm to be used to perform computations. Default to DUP. |
signature(object = "Counts")
an object of class Counts
.
Returns an object of class Counts
.
Federico Comoglio, federico.comoglio@bsse.ethz.ch
Comoglio F, Fracchia L and Rinaldi M (2013) Bayesian inference from count data using discrete uniform priors. PLOS ONE, to appear
Clough HE et al. (2005) Quantifying Uncertainty Associated with Microbial Count Data: A Bayesian Approach. Biometrics 61: 610-616
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | K <- newCounts( counts = c(20,30), fractions = c(0.075, 0.10))
#using default parameters (DUP, sampling without replacement and default prior support)
K.dup <- computePosterior(K)
#using custom prior support (DUP)
K.cust <- computePosterior(K, n1 = 0, n2 = 1e3)
#using a Gamma prior (GP method)
K.gp <- computePosterior(K, alg = 'GP')
#plot the results (compare DUP with GP)
plotPosterior(K.dup, type = 'l', lwd = 3, col = 'blue3', low = 0.025, up = 0.975)
lines(K.gp@posterior, lwd = 3, col = 'red3')
#for sampling with replacement:
computePosterior(K, replacement = TRUE)
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