RDS: RDS.HCG.estimates – R documentation

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RDS

RDS.HCG.estimates

Homophily Configuration Graph Estimates

Description

This function computes the Homophily Configuration Graph type estimates for a categorical variable.

Usage

RDS.HCG.estimates(
  rds.data,
  outcome.variable,
  N = NULL,
  subset = NULL,
  small.fraction = FALSE,
  empir.lik = TRUE,
  to.factor = FALSE,
  cont.breaks = 3
)

Arguments

`rds.data`	An `rds.data.frame` with recruitment time set.
`outcome.variable`	A string giving the name of the variable in the `rds.data` that contains a categorical variable to be analyzed.
`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.
`small.fraction`	Should a small sample fraction be assumed
`empir.lik`	Should confidence intervals be estimated using empirical likelihood.
`to.factor`	force variable to be a factor
`cont.breaks`	If variable is numeric, how many discretization points should be used in the calculation of the weights.

Value

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.HCG.estimate object is returned.

Author(s)

Ian E. Fellows

Examples

data(fauxtime)
RDS.HCG.estimates(rds.data=fauxtime,outcome.variable='var1')

RDS

Respondent-Driven Sampling

v0.9-3

LGPL-2.1

Authors

Mark S. Handcock [aut, cre], Krista J. Gile [aut], Ian E. Fellows [aut], W. Whipple Neely [aut]

Initial release

2021-03-11