Generate replicates of a dataset using Balanced Repeated Replication

Provides functions to simulate data from large-scale educational
assessments, including background questionnaire data and cognitive item
responses that adhere to a multiple-matrix sampled design. The theoretical
foundation can be found on
Matta, T.H., Rutkowski, L., Rutkowski, D. et al. (2018)
<doi:10.1186/s40536-018-0068-8>.

Waldir Leoncio

lsasim

Functions to Facilitate the Simulation of Large Scale Assessment
Data

Tyler Matta

Leslie Rutkowski

David Rutkowski

Yuan-Ling Linda Liaw

Kondwani Kajera Mughogho

Sinan Yavuz

Paul Bailey

brr function

<dl><dt>data</dt>
<dd>dataset</dd>
<dt>k</dt>
<dd>deflating weight factor. \(0 \leq k \leq 1\).</dd>
<dt>pseudo_strata</dt>
<dd>number of pseudo-strata</dd>
<dt>reps</dt>
<dd>number of replicates</dd>
<dt>max_reps</dt>
<dd>maximum number of replicates (only functional if <code>reps = NULL</code>)</dd>
<dt>weight_cols</dt>
<dd>vector of weight columns</dd>
<dt>id_col</dt>
<dd>number of column in dataset containing subject IDs. Set 0 to use the row names as ID</dd>
<dt>drop</dt>
<dd>if <code>TRUE</code>, 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</dd></dl>

Arguments

Generate replicates of a dataset using Balanced Repeated Replication — brr

<dl>

<dt>data</dt>
<dd>dataset</dd>


<dt>k</dt>
<dd>deflating weight factor. \(0 \leq k \leq 1\).</dd>


<dt>pseudo_strata</dt>
<dd>number of pseudo-strata</dd>


<dt>reps</dt>
<dd>number of replicates</dd>


<dt>max_reps</dt>
<dd>maximum number of replicates (only functional if <code>reps = NULL</code>)</dd>


<dt>weight_cols</dt>
<dd>vector of weight columns</dd>


<dt>id_col</dt>
<dd>number of column in dataset containing subject IDs. Set 0 to use the row names as ID</dd>


<dt>drop</dt>
<dd>if <code>TRUE</code>, 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</dd>

</dl>

brr: Generate replicates of a dataset using Balanced Repeated Replication

Description

Usage

Value

Arguments

References

See Also