brr {lsasim} | R Documentation |
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. |
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: Organization 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