An S4 class to store measurements (count data, sampling fractions), prior support and posterior parameters
# S4 method for Counts
get_counts(object)# S4 method for Counts
get_fractions(object)
# S4 method for Counts
set_counts(object) <- value
# S4 method for Counts
set_fractions(object) <- value
# S4 method for Counts
compute_posterior(
object,
n_start,
n_end,
replacement = FALSE,
b = 1e-10,
alg = "dup"
)
# S4 method for Counts
get_posterior_param(object, low = 0.025, up = 0.975, ...)
# S4 method for Counts
plot_posterior(object, low = 0.025, up = 0.975, xlab, step, ...)
counts vector from a Counts object
fractions vector from a Counts object
an object of class Counts
an object of class Counts
an object of class Counts
an object of class Counts
no return value, called for side effects
object of class Counts
numeric vector of sampling fractions
start of prior support range
end of prior support range
was sampling performed with replacement? Default to FALSE
prior rate parameter of the gamma distribution used to compute the posterior with Clough. Default to 1e-10
algorithm to be used to compute posterior. One of ... . Default to "dup"
1 - right tail posterior probability
left tail posterior probability
additional parameters to be passed to curve
x-axis label. Default to 'n' (no label)
integer defining the increment for x-axis labels (distance between two consecutive tick marks)
get_counts(Counts): Returns counts from a Counts object
get_fractions(Counts): Returns fractions from a Counts object
set_counts(Counts) <- value: Replaces counts of a Counts object with the provided values
set_fractions(Counts) <- value: Replaces fractions of a Counts object with the provided values
compute_posterior(Counts): Compute the posterior probability distribution of the population size
get_posterior_param(Counts): Extract statistical parameters (e.g. credible intervals)
from a posterior probability distribution
plot_posterior(Counts): Plot posterior probability distribution and posterior parameters
countsinteger vector of counts (required)
fractionsnumeric vector of sampling fractions (required)
n_startstart of prior support range. If omitted and total counts greater than zero,
computed as 0.5 * mle, where mle is the maximum likelihood estimate of the population size
n_endend of prior support range. If omitted and total counts greater than zero,
computed as 2 * mle, where mle is the maximum likelihood estimate of the population size
f_productproduct of (1-fractions)
mlemaximum likelihood estimate of the population size (ratio between total counts and total sampling fraction)
norm_constantnormalization constant
posteriornumeric vector of posterior probabilities over the prior support
map_pmaximum of posterior probability
map_indexindex of prior support corresponding to the maximum a posteriori
mapmaximum a posteriori of population size
q_lowlower bound of the credible interval
q_low_pprobability of the lower bound of the credible interval
q_low_indexindex of the prior support corresponding to q_low
q_low_cum_pcumulative posterior probability from n_start to q_low (left tail)
q_upupper bound of the credible interval
q_up_pprobability of the upper bound of the credible interval
q_up_indexindex of the prior support corresponding to q_high
q_up_cum_pcumulative posterior probability from q_high to n_end (right tail)
gammalogical, TRUE if posterior computed using a Gamma approximation
Federico Comoglio
Comoglio F, Fracchia L and Rinaldi M (2013) Bayesian inference from count data using discrete uniform priors. PLoS ONE 8(10): e74388
compute_posterior, get_posterior_param
# constructor:
# create an object of class 'Counts'
new_counts(counts = c(30, 35), fractions = c(0.075, 0.1))
# same, using new
new("Counts", counts = c(30, 35), fractions = c(0.075, 0.1))
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