Description Usage Arguments Value Author(s) References See Also Examples
This function computes the RDSI type estimates for a categorical variable. It is also referred to as the SalganikHeckathorn estimator.
1 2 3 
rds.data 
An 
outcome.variable 
A string giving the name of the variable in the

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 RDSI 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 RDSI adjustment when the variate is continuous. 
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.I.estimate
object is returned.
Mark S. Handcock and W. Whipple Neely
Gile, Krista J., Handcock, Mark S., 2010, Respondentdriven Sampling: An Assessment of Current Methodology. Sociological Methodology 40, 285327.
Neely, W. W., 2009. Bayesian methods for data from respondent driven sampling. Dissertation inprogress, Department of Statistics, University of Wisconsin, Madison.
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.II.estimates
, RDS.SS.estimates
1 2 3  data(faux)
RDS.I.estimates(rds.data=faux,outcome.variable='X')
RDS.I.estimates(rds.data=faux,outcome.variable='X',smoothed=TRUE)

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