This function computes the sequential sampling (SS) estimates for a categorical variable or numeric variable.
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,
to.factor = FALSE
)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.
An rds.data.frame that indicates recruitment patterns
by a pair of attributes named ``id'' and ``recruiter.id''.
A string giving the name of the variable in the
rds.data that contains a categorical or numeric variable to be
analyzed.
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 it defaults to 1000.
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.
The number of samples to take in
estimating the inclusion probabilites 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.
The number of iterations of the sequential sampling algorithm. If that is missing it defaults to 5.
A list of control parameters for algorithm
tuning. Constructed using
control.rds.estimates.
logical; Use the standard Hajek-type estimator of Gile (2011) or the standard Hortitz-Thompson. The default is TRUE.
If true, and outcome.variable is numeric, standard errors based on empirical likelihood will be given.
force variable to be a factor
Krista J. Gile with help from Mark S. Handcock
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.
RDS.I.estimates, RDS.II.estimates
data(fauxmadrona)
RDS.SS.estimates(rds.data=fauxmadrona,outcome.variable="disease",N=1000)
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