Counts-class: An S4 class to store measurements (count data, sampling...
In dupiR: Bayesian Inference from Count Data using Discrete Uniform Priors

Counts-class

R Documentation

An S4 class to store measurements (count data, sampling fractions), prior support and posterior parameters

Description

An S4 class to store measurements (count data, sampling fractions), prior support and posterior parameters

Usage

## S4 method for signature 'Counts'
get_counts(object)

## S4 method for signature 'Counts'
get_fractions(object)

## S4 replacement method for signature 'Counts'
set_counts(object) <- value

## S4 replacement method for signature 'Counts'
set_fractions(object) <- value

## S4 method for signature 'Counts'
compute_posterior(
  object,
  n_start,
  n_end,
  replacement = FALSE,
  b = 1e-10,
  alg = "dup"
)

## S4 method for signature 'Counts'
get_posterior_param(object, low = 0.025, up = 0.975, ...)

## S4 method for signature 'Counts'
plot_posterior(object, low = 0.025, up = 0.975, xlab, step, ...)

Arguments

`object`	object of class `Counts`
`value`	numeric vector of sampling fractions
`n_start`	start of prior support range
`n_end`	end of prior support range
`replacement`	was sampling performed with replacement? Default to FALSE
`b`	prior rate parameter of the gamma distribution used to compute the posterior with Clough. Default to 1e-10
`alg`	algorithm to be used to compute posterior. One of ... . Default to "dup"
`low`	1 - right tail posterior probability
`up`	left tail posterior probability
`...`	additional parameters to be passed to curve
`xlab`	x-axis label. Default to 'n' (no label)
`step`	integer defining the increment for x-axis labels (distance between two consecutive tick marks)

Value

counts vector from a Counts object

fractions vector from a Counts object

an object of class Counts

no return value, called for side effects

Methods (by generic)

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

Slots

counts: integer vector of counts (required)
fractions: numeric vector of sampling fractions (required)
n_start: start 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_end: end 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_product: product of (1-fractions)
mle: maximum likelihood estimate of the population size (ratio between total counts and total sampling fraction)
norm_constant: normalization constant
posterior: numeric vector of posterior probabilities over the prior support
map_p: maximum of posterior probability
map_index: index of prior support corresponding to the maximum a posteriori
map: maximum a posteriori of population size
q_low: lower bound of the credible interval
q_low_p: probability of the lower bound of the credible interval
q_low_index: index of the prior support corresponding to q_low
q_low_cum_p: cumulative posterior probability from n_start to q_low (left tail)
q_up: upper bound of the credible interval
q_up_p: probability of the upper bound of the credible interval
q_up_index: index of the prior support corresponding to q_high
q_up_cum_p: cumulative posterior probability from q_high to n_end (right tail)
gamma: logical, TRUE if posterior computed using a Gamma approximation

Note

The posterior slot contains either the PMF or a logical value used to compute posterior parameters with a Gamma approximation (see reference for details)

Lower and upper bounds of the credibile interval are computed at a default confidence level of 95

For more details on the normalization constant, see Corollary 1 in reference

Author(s)

Federico Comoglio

References

Comoglio F, Fracchia L and Rinaldi M (2013) Bayesian inference from count data using discrete uniform priors. PLoS ONE 8(10): e74388

Examples

# 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))

dupiR documentation built on May 29, 2024, 1:21 a.m.