Description Usage Arguments Details Value

View source: R/rds_bootstrap.r

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.

1 | ```
rds.mc.boot.draws(chains, mm, dd, num.reps)
``` |

`chains` |
a list with the chains constructed from the survey
using |

`mm` |
the mixing model |

`dd` |
the degree distributions |

`num.reps` |
the number of bootstrap resamples we want |

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.

TODO be sure to comment the broken-out trait variables (ie these could all be different from the originals)

a list of length `num.reps`

; each entry in
the list has one bootstrap-resampled dataset

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