RDS.SS.estimates: Gile's SS Estimates

In RDS: Respondent-Driven Sampling

Gile's SS Estimates

Description

This function computes the sequential sampling (SS) estimates for a categorical variable or numeric variable.

Usage

RDS.SS.estimates(
  rds.data,
  outcome.variable,
  N = NULL,
  subset = NULL,
  number.ss.samples.per.iteration = 500,
  number.ss.iterations = 5,
  control = control.rds.estimates(),
  hajek = TRUE,
  to.factor = FALSE
)

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 or numeric variable to be analyzed.
N	An estimate of the number of members of the population being sampled. If NULL it is read as the population.size.mid attribute of the rds.data frame. If that is missing no finite population correction is applied and the estimate is the same as RDS-II.
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.
number.ss.samples.per.iteration	The number of samples to take in estimating the inclusion probabilities in each iteration of the sequential sampling algorithm. If NULL it is read as the eponymous attribute of rds.data. If that is missing it defaults to 5000.
number.ss.iterations	The number of iterations of the sequential sampling algorithm. If that is missing it defaults to 5.
control	A list of control parameters for algorithm tuning. Constructed using control.rds.estimates.
hajek	logical; Use the standard Hajek-type estimator of Gile (2011) or the standard Hortitz-Thompson. The default is TRUE.
to.factor	force variable to be a factor

Value

An object of class rds.weighted.estimate.

Author(s)

Krista J. Gile with help from Mark S. Handcock

References

Gile, Krista J. 2011 Improved Inference for Respondent-Driven Sampling Data with Application to HIV Prevalence Estimation, Journal of the American Statistical Association, 106, 135-146.

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>.

Gile, Krista J., Handcock, Mark S., 2015 Network Model-Assisted Inference from Respondent-Driven Sampling Data, Journal of the Royal Statistical Society, A. <doi:10.1111/rssa.12091>.

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.I.estimates, RDS.II.estimates

Examples

data(fauxmadrona)
RDS.SS.estimates(rds.data=fauxmadrona,outcome.variable="disease",N=1000)

RDS documentation built on Oct. 21, 2026, 1:06 a.m.