knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(splithalfr)
This vignette demonstrates the methods of splitting data that are supported by the splithalfr
. Each splitting method is illustrated by calling by_split
with the right arguments, printing to the terminal what data is in each of the two parts produced by a split. For a comprehensive review of each splitting method, see Pronk et al. (2021).
We'll use this example dataset with eight trials of one participant, each trial having a condition and rt variable.
ds <- data.frame( participant = rep(1, 8), condition = rep(c("a", "b"), each = 4), rt = 100 * 1 : 8 )
First-second splitting assigns trials of the first half of rows to one part and trials of the second half of rows to the other (Green et al., 2016; Webb, Shavelson, & Haertel, 1996; Williams & Kaufmann, 2012). For this splitting method, set method
to first_second
.
dummy = by_split( ds, ds$participant, method = "first_second", function(ds) { print(ds); }, ncores = 1, verbose = F )
Odd-even splitting assigns trials with an odd row number to one part and trials with an even row number to the other (Green et al., 2016; Webb, Shavelson, & Haertel, 1996; Williams & Kaufmann, 2012). For this splitting method, set method
to odd_even
.
dummy = by_split( ds, ds$participant, method = "odd_even", function(ds) { print(ds); }, ncores = 1, verbose = F )
Permutated splitting is also known as random splitting (Kopp, Lange, & Steinke, 2021), bootstrapped splitting (Parsons, Kruijt, & Fox, 2019) and random sample of split halves (Williams & Kaufmann, 2012). It assigns trials to each part via random sampling without replacement. This splitting method is the default, but you can make it explicit by setting method
to random
. In practice, random splits are averaged over many replications, but for illustration we're only printing one.
dummy = by_split( ds, ds$participant, method = "random", replications = 1, function(ds) { print(ds); }, ncores = 1, verbose = F )
Monte Carlo splitting assigns trials to each part by sampling with replacement (Williams & Kaufmann, 2012). For constructing parts that are of any length, use the split_p
argument and set replace
to TRUE
. The example below constructs two parts of the same length as the original dataset by setting split_p
to 1.
dummy = by_split( ds, ds$participant, method = "random", replace = TRUE, split_p = 1, replications = 1, function(ds) { print(ds); }, ncores = 1, verbose = F )
If a split is stratified by a variable, then trials are separately assigned to each part for each level of that variable (Green et al., 2016). For example, if splits are stratified by ds$condition
, the trials with condition a and b are split separately. Stratification can be used in combination with any of the methods above. For illustration we combine it with first-second splitting
dummy = by_split( ds, ds$participant, method = "first_second", stratification = ds$condition, function(ds) { print(ds); }, ncores = 1, verbose = F )
In a subsampled split, a subset of the trials is randomly sampled without replacement and then split (see the supplementary materials of Hedge, Powell, & Sumner, 2018). Sub-sampling only works well with splitting methods that uses random sampling (permutated and Monte Carlo). Since the sub-sampling procedure already randomizes the trials selected for splitting, splitting methods that assign trials to part based on their row number, such as first-second and odd-even, should give results that are similar to permutated splitting. Any stratifications are applied both to the sub-sampling and splitting.
dummy = by_split( ds, ds$participant, method = "random", stratification = ds$condition, subsample_p = 0.5, function(ds) { print(ds); }, ncores = 1, verbose = F )
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