AIscreenR (version 0.1.1)

sample_references: Random sample references

Description

sample_referencessamples n rows from the dataset with titles and abstracts either with or without replacement. This function is supposed to support the construct of a test dataset, as suggested by Vembye et al. (2024).

Usage

sample_references(
  data,
  n,
  with_replacement = FALSE,
  prob_vec = rep(1/n, nrow(data))
)

Value

A dataset with n rows.

Arguments

data

Dataset containing the titles and abstracts wanted to be screened.

n

A non-negative integer giving the number of rows to choose.

with_replacement

Logical indicating if sampling should be done with of without replacement. Default is FALSE.

prob_vec

'A vector of probability weights for obtaining the elements of the vector being sampled.' Default is a vector of 1/n.

References

Vembye, M. H., Christensen, J., Mølgaard, A. B., & Schytt, F. L. W. (2024) GPT API Models Can Function as Highly Reliable Second Screeners of Titles and Abstracts in Systematic Reviews: A Proof of Concept and Common Guidelines https://osf.io/preprints/osf/yrhzm

Examples

excl_test_dat <- filges2015_dat[1:200,] |> sample_references(100)