summary.sspse: Summarizing Population Size Estimation Model Fits

In sspse: Estimating Hidden Population Size using Respondent Driven Sampling Data

Summarizing Population Size Estimation Model Fits

Description

This is the summary method for class "sspse" objects. These objects encapsulate an estimate of the posterior distribution of the population size based on data collected by Respondent Driven Sampling. The approach approximates the RDS via the Sequential Sampling model of Gile (2008). As such, it is referred to as the Sequential Sampling - Population Size Estimate (SS-PSE). It uses the order of selection of the sample to provide information on the distribution of network sizes over the population members. summary method for class "sspse". posterior distribution of the population size based on data collected by Respondent Driven Sampling. The approach approximates the RDS via the Sequential Sampling model of Gile (2008). As such, it is referred to as the Sequential Sampling - Population Size Estimate (SS-PSE). It uses the order of selection of the sample to provide information on the distribution of network sizes over the population members.

Usage

## S3 method for class 'sspse'
summary(object, support = 1000, HPD.level = 0.95, method = "bgk", ...)

Arguments

object	an object of class "sspse", usually, a result of a call to posteriorsize.
support	the number of equally-spaced points to use for the support of the estimated posterior density function.
HPD.level	numeric; probability level of the highest probability density interval determined from the estimated posterior.
method	character; The method to use for density estimation (default Gaussian Kernel; "bgk"). "Bayes" uses a Bayesian density estimator which has good properties.
...	further arguments passed to or from other methods.

Details

print.summary.sspse tries to be smart about formatting the coefficients, standard errors, etc. and additionally gives ‘significance stars’ if signif.stars is TRUE.

Aliased coefficients are omitted in the returned object but restored by the print method.

Correlations are printed to two decimal places (or symbolically): to see the actual correlations print summary(object)$correlation directly.

Value

The function summary.sspse computes and returns a two row matrix of summary statistics of the prior and estimated posterior distributions. The rows correspond to the Prior and the Posterior, respectively. The rows names are Mean, Median, Mode, 25%, 75%, and 90%. These correspond to the distributional mean, median, mode, lower quartile, upper quartile and 90% quantile, respectively.

See Also

The model fitting function posteriorsize, summary.

Examples

data(fauxmadrona)
# Here interval=1 so that it will run faster. It should be higher in a 
# real application.
fit <- posteriorsize(fauxmadrona, median.prior.size=1000,
                                 burnin=20, interval=1, samplesize=100)
summary(fit)

sspse documentation built on Aug. 8, 2023, 9:07 a.m.