control.rds.estimates: Auxiliary for Controlling RDS.bootstrap.intervals

Description Usage Arguments Details Value See Also

View source: R/control.rds.estimates.R


Auxiliary function as user interface for fine-tuning RDS.bootstrap.intervals algorithm, which computes interval estimates for via bootstrapping.


control.rds.estimates(confidence.level = 0.95, SS.infinity = 0.01,
  lowprevalence = c(8, 14), discrete.cutoff = 0.8, useC = TRUE,
  number.of.bootstrap.samples = NULL, seed = NULL)



The confidence level for the confidence intervals. The default is 0.95 for 95%.


The sample proportion, n/N, below which the computation of the SS weights should simplify to that of the RDS-II weights.


Standard confidence interval procedures can be inaccurate when the outcome expected count is close to zero. This sets conditions where alternatives to the standard are used for the ci.type="hmg" option. See Details for its use.


The minimum proportion of the values of the outcome variable that need to be unique before the variable is judged to be continuous.


Use a C-level implementation of Gile's bootstrap (rather than the R level). The implementations should be computational equivalent (except for speed).


The number of bootstrap samples to take in estimating the uncertainty of the estimator. If NULL it defaults to the number necessary to compute the standard error to accuracy 0.001.


Seed value (integer) for the random number generator. See set.seed


This function is only used within a call to the RDS.bootstrap.intervals function.

Some of the arguments are not yet fully implemented. It will evolve slower to incorporate more arguments as the package develops.

Standard confidence interval procedures can be inaccurate when the outcome expected count is close to zero. In these cases the combined Agresti-Coull and the bootstrap-t interval of Mantalos and Zografos (2008) can be used. The lowprevalence argument is a two vector parameter setting the conditions under which the approximation is used. The first is the penalty term on the differential activity. If the observed number of the rare group minus the product of the first parameter and the differential activity is lower than the second parameter, the low prevalence approximation is used.


A list with arguments as components.

See Also


RDS documentation built on Dec. 2, 2017, 1:08 a.m.