RDS.I.estimates: Compute RDS-I Estimates

In RDS: Respondent-Driven Sampling

Compute RDS-I Estimates

Description

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

Usage

RDS.I.estimates(
  rds.data,
  outcome.variable,
  subset = NULL,
  smoothed = FALSE,
  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.
subset	An optional criterion to subset rds.data by. It is an R expression which, when evaluated, subset the data. In plain English, it can be something like subset = seed > 0 to exclude seeds. It can also be the name of a logical vector of the same length of the outcome variable where TRUE means include it in the analysis. If NULL then no subsetting is done.
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.
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

An object of class rds.weighted.estimate.

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. <doi:10.1111/j.1467-9531.2010.01223.x>

Gile, Krista J., Beaudry, Isabelle S. and Handcock, Mark S., 2018 Methods for Inference from Respondent-Driven Sampling Data, Annual Review of Statistics and Its Application <doi:10.1146/annurev-statistics-031017-100704>.

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

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 Oct. 21, 2026, 1:06 a.m.