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.
# S3 method for sspse
summary(object, support = 1000, HPD.level = 0.95, method = "bgk", ...)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.
an object of class "sspse", usually, a result of a call
to posteriorsize.
the number of equally-spaced points to use for the support of the estimated posterior density function.
numeric; probability level of the highest probability density interval determined from the estimated posterior.
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.
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.
The model fitting function posteriorsize,
summary.
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)
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