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RDS (version 0.9-10)

Respondent-Driven Sampling

Description

Provides functionality for carrying out estimation with data collected using Respondent-Driven Sampling. This includes Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential Sampling estimator. The package is part of the "RDS Analyst" suite of packages for the analysis of respondent-driven sampling data. See Gile and Handcock (2010) , Gile and Handcock (2015) and Gile, Beaudry, Handcock and Ott (2018) .

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Version

Install

install.packages('RDS')

Monthly Downloads

522

Version

0.9-10

License

LGPL-2.1

Maintainer

Mark S Handcock

Last Published

September 6th, 2024

Functions in RDS (0.9-10)

A Simulated RDS Data Set with extreme seed dependency

export.rds.interval.estimate

Convert the output of print.rds.interval.estimate from a character data.frame to a numeric matrix

A Simulated RDS Data Set

get.number.of.recruits

Calculates the number of (direct) recuits for each respondent.

Returns the network size of each subject (i.e. their degree).

count.transitions

Counts the number or recruiter->recruitee transitions between different levels of the grouping variable.

Get recruiter id

Calculates the root seed id for each node of the recruitement tree.

assert.valid.rds.data.frame

Does various checks and throws errors if x is not a valid rds.data.frame

convergence.plot

Convergence Plots

cumulative.estimate

Calculates estimates at each successive wave of the sampling process

get.population.size

Returns the population size associated with the data.

differential.activity.estimates

Differential Activity between groups

Get the subject id

Get Horvitz-Thompson estimator assuming inclusion probability proportional to the inverse of network.var (i.e. degree).

Imputes missing degree values

A Simulated RDS Data Set with no seed dependency

impute.visibility

Estimates each person's personal visibility based on their self-reported degree and the number of their (direct) recruits. It uses the time the person was recruited as a factor in determining the number of recruits they produce.

Calculates the depth of the recruitment tree (i.e. the recruitment wave) at each node.

A Simulated RDS Data Set

control.rds.estimates

Auxiliary for Controlling RDS.bootstrap.intervals

get.recruitment.time

Returns the recruitment time for each subject

homophily configuration graph weights

[<-.rds.data.frame

get.stationary.distribution

Markov chain statistionary distribution

Gets the recruiter id associated with the seeds

gile.ss.weights

Weights using Giles SS estimator

hcg.replicate.weights

HCG parametric bootstrap replicate weights

has.recruitment.time

RDS data.frame has recruitment time information

print.rds.contin.bootstrap

Displays an rds.contin.bootstrap

print.rds.data.frame

Displays an rds.data.frame

[.rds.data.frame

plot.rds.data.frame

Diagnostic plots for the RDS recruitment process

impute.visibility_mle

Estimates each person's personal visibility based on their self-reported degree and the number of their (direct) recruits. It uses the time the person was recruited as a factor in determining the number of recruits they produce.

is.rds.interval.estimate.list

Is an instance of rds.interval.estimate.list This is a (typically time ordered) sequence of RDS estimates of a comparable quantity

print.rds.interval.estimate

Prints an rds.interval.estimate object

homophily.estimates

This function computes an estimate of the population homophily and the recruitment homophily based on a categorical variable.

print.summary.svyglm.RDS

Summarizing Generalized Linear Model Fits with Odds Ratios

Create RDS samples with given characteristics

show.rds.data.frame

Displays an rds.data.frame

Writes out the RDS tree in RDSAT format

Import data from the 'RDSAT' format as an rds.data.frame

summary.svyglm.RDS

Summarizing Generalized Linear Model Fits with Odds Ratios for Survey Data

print.differential.activity.estimate

Prints an differential.activity.estimate object

Writes out the RDS tree in NetDraw format

print.pvalue.table

Displays a pvalue.table

Volz-Heckathorn (RDS-II) weights

reingold.tilford.plot

Plots the recruitment network using the Reingold Tilford algorithm.

writes an rds.data.frame recruitment tree as a GraphViz file

Import data saved using write.rdsobj

is.rds.data.frame

Is an instance of rds.data.frame

Export an rds.data.frame to file

rid.from.coupons

Determines the recruiter.id from recruitment coupon information

set.control.class

Set the class of the control list

is.rds.interval.estimate

Is an instance of rds.interval.estimate

rds.interval.estimate

An object of class rds.interval.estimate

transition.counts.to.Markov.mle

calculates the mle. i.e. the row proportions of the transition matrix

Extract or replace the *ult*imate (last) element of a vector or a list, or an element counting from the end.

RDS: Respondent-Driven Sampling

RDS.compare.proportions

Compares the rates of two variables against one another.

converts to character with minimal loss of precision for numeric variables

control.list.accessor

Named element accessor for ergm control lists

as.rds.data.frame

Coerces a data.frame object into an rds.data.frame object.

RDS.compare.two.proportions

Compares the rates of two variables against one another.

RDS.HCG.estimates

Homophily Configuration Graph Estimates

compute.weights

Compute estimates of the sampling weights of the respondent's observations based on various estimators

bootstrap.contingency.test

Performs a bootstrap test of independance between two categorical variables

bottleneck.plot

Bottleneck Plot

RDS.bootstrap.intervals

RDS Bootstrap Interval Estimates

Compute a test of trend in prevalences based on a likelihood-ratio statistic

RDS.SS.estimates

Gile's SS Estimates

LRT.value.trend

Compute a test of trend in prevalences based on a likelihood-ratio statistic

RDS.II.estimates

RDS-II Estimates

RDS.I.estimates

Compute RDS-I Estimates

bootstrap.incidence

Calculates incidence and bootstrap confidence intervals for immunoassay data collected with RDS