brr: Generate replicates of a dataset using Balanced Repeated...

In tmatta/lsasim: Functions to Facilitate the Simulation of Large Scale Assessment Data

Generate replicates of a dataset using Balanced Repeated Replication

Description

Generate replicates of a dataset using Balanced Repeated Replication

Usage

brr(
  data,
  k = 0,
  pseudo_strata = ceiling(nrow(data)/2),
  reps = NULL,
  max_reps = 80,
  weight_cols = "none",
  id_col = 1,
  drop = TRUE
)

Arguments

data	dataset
k	deflating weight factor. 0 \leq k \leq 1.
pseudo_strata	number of pseudo-strata
reps	number of replicates
max_reps	maximum number of replicates (only functional if 'reps = NULL')
weight_cols	vector of weight columns
id_col	number of column in dataset containing subject IDs. Set 0 to use the row names as ID
drop	if 'TRUE', the observation that will not be part of the subsample is dropped from the dataset. Otherwise, it stays in the dataset but a new weight column is created to differentiate the selected observations

Value

a list containing all the BRR replicates of 'data'

Note

PISA uses the BRR Fay method with k = 0.5.

References

OECD (2015). Pisa Data Analysis Manual. Adams, R., & Wu, M. (2002). PISA 2000 Technical Report. Paris: Organisation for Economic Co-operation and Development (OECD). Rust, K. F., & Rao, J. N. K. (1996). Variance estimation for complex surveys using replication techniques. Statistical methods in medical research, 5(3), 283-310.

See Also

jackknife

tmatta/lsasim documentation built on Aug. 25, 2023, 5:50 p.m.