print.summary.sspse {sspse}

Summarizing Population Size Estimation Model Fits

Description

This is the print method for the summary class 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.

Usage

## S3 method for class 'summary.sspse'
print(
  x,
  digits = max(3, getOption("digits") - 3),
  correlation = FALSE,
  covariance = FALSE,
  signif.stars = getOption("show.signif.stars"),
  eps.Pvalue = 1e-04,
  ...
)

Arguments

x

an object of class "summary.sspse", usually, a result of a call to summary.sspse.

digits

the number of significant digits to use when printing.

correlation

logical; if TRUE, the correlation matrix of the estimated parameters is returned and printed.

covariance

logical; if TRUE, the covariance matrix of the estimated parameters is returned and printed.

signif.stars

logical. If TRUE, ‘significance stars’ are printed for each coefficient.

eps.Pvalue

number; indicates the smallest p-value. printCoefmat.

...

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.

References

Gile, Krista J. (2008) Inference from Partially-Observed Network Data, Ph.D. Thesis, Department of Statistics, University of Washington.

Gile, Krista J. and Handcock, Mark S. (2010) Respondent-Driven Sampling: An Assessment of Current Methodology, Sociological Methodology 40, 285-327.

Gile, Krista J. and Handcock, Mark S. (2014) sspse: Estimating Hidden Population Size using Respondent Driven Sampling Data R package, Los Angeles, CA. Version 0.5, https://hpmrg.org/sspse/.

Handcock MS (2003). degreenet: Models for Skewed Count Distributions Relevant to Networks. Statnet Project, Seattle, WA. Version 1.2, https://statnet.org/.

Handcock, Mark S., Gile, Krista J. and Mar, Corinne M. (2014) Estimating Hidden Population Size using Respondent-Driven Sampling Data, Electronic Journal of Statistics, 8, 1, 1491-1521

Handcock, Mark S., Gile, Krista J. and Mar, Corinne M. (2015) Estimating the Size of Populations at High Risk for HIV using Respondent-Driven Sampling Data, Biometrics.

See Also

The model fitting function posteriorsize, summary.

Function coef will extract the matrix of coefficients with standard errors, t-statistics and p-values.

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)
fit