RDS.II.estimates | R Documentation |
This function computes the RDS-II estimates for a categorical variable or the RDS-II estimate for a numeric variable.
RDS.II.estimates(
rds.data,
outcome.variable,
N = NULL,
subset = NULL,
empir.lik = TRUE,
to.factor = FALSE
)
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 optional criterion to subset |
empir.lik |
If true, and outcome.variable is numeric, standard errors based on empirical likelihood will be given. |
to.factor |
force variable to be a factor |
If outcome.variable
is numeric then the RDS-II 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.II.estimate
is returned.
Mark S. Handcock and W. Whipple Neely
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>.
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.SS.estimates
data(faux)
RDS.II.estimates(rds.data=faux,outcome.variable='X')
RDS.II.estimates(rds.data=faux,outcome.variable='X',subset= Y!="blue")
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