RDS.I.estimates: Compute RDS-I Estimates

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/rds-I.R

Description

This function computes the RDS-I type estimates for a categorical variable. It is also referred to as the Salganik-Heckathorn estimator.

Usage

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RDS.I.estimates(rds.data, outcome.variable, N = NULL, subset = NULL,
  smoothed = FALSE, empir.lik = TRUE, to.factor = FALSE,
  cont.breaks = 3)

Arguments

rds.data

An rds.data.frame that indicates recruitment patterns by a pair of attributes named “id” and “recruiter.id”.

outcome.variable

A string giving the name of the variable in the rds.data that contains a categorical variable to be analyzed.

N

Population size to be used to calculate the empirical likelihood interval. If NULL, this value is taken to be the population.size.mid attribute of the data and if that is not set, no finite population correction is used.

subset

An expression defining a subset of rds.data.

smoothed

Logical, if TRUE then the “data smoothed” version of RDS-I is used, where it is assumed that the observed Markov process is reversible.

empir.lik

Should confidence intervals be estimated using empirical likelihood.

to.factor

force variable to be a factor

cont.breaks

The number of categories used for the RDS-I adjustment when the variate is continuous.

Value

If the empir.lik is true, an object of class rds.interval.estimate is returned. This is a list with components

Otherwise an object of class rds.I.estimate object is returned.

Author(s)

Mark S. Handcock and W. Whipple Neely

References

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

Neely, W. W., 2009. Bayesian methods for data from respondent driven sampling. Dissertation in-progress, Department of Statistics, University of Wisconsin, Madison.

Salganik, M., Heckathorn, D. D., 2004. Sampling and estimation in hidden populations using respondent-driven sampling. Sociological Methodology 34, 193-239.

Volz, E., Heckathorn, D., 2008. Probability based estimation theory for Respondent Driven Sampling. The Journal of Official Statistics 24 (1), 79-97.

See Also

RDS.II.estimates, RDS.SS.estimates

Examples

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data(faux)
RDS.I.estimates(rds.data=faux,outcome.variable='X')
RDS.I.estimates(rds.data=faux,outcome.variable='X',smoothed=TRUE)

RDS documentation built on Dec. 2, 2017, 1:08 a.m.

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