Counts-class {dupiR}

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

an object of class Counts

an object of class Counts

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

See Also

compute_posterior, get_posterior_param

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))