bootstrap.contingency.test: Performs a bootstrap test of independance between two...

In RDS: Respondent-Driven Sampling

Performs a bootstrap test of independance between two categorical variables

Description

Performs a bootstrap test of independance between two categorical variables

Usage

bootstrap.contingency.test(
  rds.data,
  row.var,
  col.var,
  number.of.bootstrap.samples = 1000,
  weight.type = c("HCG", "RDS-II", "Arithmetic Mean"),
  table.only = FALSE,
  verbose = TRUE,
  ...
)

Arguments

rds.data	an rds.data.frame
row.var	the name of the first categorical variable
col.var	the name of the second categorical variable
number.of.bootstrap.samples	The number of simulated boootstrap populations
weight.type	The type of weighting to use for the contningency table. Only large sample methods are allowed.
table.only	only returns the weighted table, without bootstrap.
verbose	level of output
...	Additional parameters for compute_weights

Details

This function first estimates a Homophily Configuration Graph model for the underlying network under the assumption that the two variables are independant and that the population size is large. It then draws bootstrap RDS samples from this population distribution and calculates the chi.squared statistic on the weighted contingency table. Weights are calculated using the HCG estimator assuming a large population size.

Examples

data(faux)
bootstrap.contingency.test(rds.data=faux, row.var="X", col.var="Y",
  number.of.bootstrap.samples=50, verbose=FALSE)

RDS documentation built on Aug. 20, 2023, 9:06 a.m.