Description Usage Arguments Value Author(s) References See Also Examples
This function computes the sequential sampling (SS) estimates for a categorical variable or numeric variable.
1 2 3  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, empir.lik = TRUE)

rds.data 
An 
outcome.variable 
A string giving the name of the variable in the

N 
An estimate of the number of members of the population being
sampled. If 
subset 
An optional criterion to subset 
number.ss.samples.per.iteration 
The number of samples to take in
estimating the inclusion probabilites in each iteration of the sequential
sampling algorithm. If 
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 
hajek 
logical; Use the standard Hajektype estimator of Gile (2011) or the standard HortitzThompson. The default is TRUE. 
empir.lik 
If true, and outcome.variable is numeric, standard errors based on empirical likelihood will be given. 
If outcome.variable
is numeric then the Gile SS estimate of the mean is returned, otherwise a vector of proportion estimates is returned.
If the empir.lik
is true, an object of class rds.interval.estimate
is returned. This is a list with components
estimate
: The numerical point estimate of proportion
of the trait.variable
.
interval
: A matrix with six
columns and one row per category of trait.variable
:
point estimate
: The HT estimate of the population mean.
95% Lower Bound
: Lower 95% confidence bound.
95%
Upper Bound
: Upper 95% confidence bound.
Design Effect
: The
design effect of the RDS.
s.e.
: Standard error.
n
:
Count of the number of sample values with that value of the trait.
Otherwise, an object of class rds.SS.estimate
is returned.
Krista J. Gile with help from Mark S. Handcock
Gile, Krista J. 2011 Improved Inference for RespondentDriven Sampling Data with Application to HIV Prevalence Estimation, Journal of the American Statistical Association, 106, 135146.
Gile, Krista J., Handcock, Mark S., 2010 Respondentdriven Sampling: An Assessment of Current Methodology, Sociological Methodology, 40, 285327.
Gile, Krista J., Handcock, Mark S., 2011 Network ModelAssisted Inference from RespondentDriven Sampling Data, ArXiv Preprint.
Salganik, M., Heckathorn, D. D., 2004. Sampling and estimation in hidden populations using respondentdriven sampling. Sociological Methodology 34, 193239.
Volz, E., Heckathorn, D., 2008. Probability based estimation theory for Respondent Driven Sampling. The Journal of Official Statistics 24 (1), 7997.
RDS.I.estimates
, RDS.II.estimates
1 2  data(fauxmadrona)
RDS.SS.estimates(rds.data=fauxmadrona,outcome.variable="disease",N=1000)

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