Draw RDS bootstrap resamples using the algorithm in Salganik 2006 (see Details below)

This algorithm picks a respondent from the survey to be a seed uniformly at random. it then generates a bootstrap draw by simulating the markov process forward for n steps, where n is the size of the draw required.

If you wish the bootstrap dataset to end up with variables from the original dataset other than the traits and degree, then you must specify this when you construct dd using the 'estimate.degree.distns function.

Usage

rds.mc.boot.draws(chains, mm, dd, num.reps)

Arguments

chains: A list with the chains constructed from the survey using make.chain
mm: The mixing model
dd: The degree distributions
num.reps: The number of bootstrap resamples we want

Value

A list of length num.reps; each entry in the list has one bootstrap-resampled dataset

Details

See:

Salganik, Matthew J. "Variance estimation, design effects, and sample size calculations for respondent-driven sampling." Journal of Urban Health 83.1 (2006): 98-112.