splithalf.multiverse: Multiverse of data processing decisions on internal...

In sdparsons/splithalf: Calculate Task Split Half Reliability Estimates

Multiverse of data processing decisions on internal consistency reliability estimates.

Description

This function enables the user to run a multiverse of data processing options and extract the resulting (internal consistency) reliability estimates generated by splithalf. The user specifies a set of data processing decisions and passes this to the function, along with a splithalf object. The output can then be explored and plotted as desired.

Usage

splithalf.multiverse(input, specifications)

Arguments

input	splithalf object or list of splithalf objects
specifications	list of data processing specifications

Details

The (unofficial) function version name is "This function will let you get honey from a hornets nest"

Value

Returns a multiverse object containing the reliability estimates and dataframes from all data processing specifications provided

Examples

## Not run: 
## see online documentation for examples
https://github.com/sdparsons/splithalf
## also see https://psyarxiv.com/y6tcz

## example simulated data
n_participants = 60 ## sample size
n_trials = 80
n_blocks = 2
sim_data <- data.frame(participant_number = rep(1:n_participants,
                       each = n_blocks * n_trials),
                       trial_number = rep(1:n_trials,
                       times = n_blocks * n_participants),
                       block_name = rep(c("A","B"),
                       each = n_trials,
                       length.out = n_participants * n_trials * n_blocks),
                       trial_type = rep(c("congruent","incongruent"),
                       length.out = n_participants * n_trials * n_blocks),
                       RT = rnorm(n_participants * n_trials * n_blocks,
                       500,
                       200),
                       ACC = 1)

## specify several data processing decisions
specifications <- list(RT_min = c(0, 100, 200),
                       RT_max = c(1000, 2000),
                       averaging_method = c("mean", "median"))
## run splithalf, and save the output
difference <- splithalf(data = sim_data,
                        outcome = "RT",
                        score = "difference",
                        conditionlist = c("A"),
                        halftype = "random",
                        permutations = 5000,
                        var.RT = "RT",
                        var.condition = "block_name",
                        var.participant = "participant_number",
                        var.compare = "trial_type",
                        var.ACC = "ACC",
                        compare1 = "congruent",
                        compare2 = "incongruent",
                        average = "mean")

## run splithalf.multiverse to perform the multiverse of data processing
## and reliability estimation
multiverse <- splithalf.multiverse(input = difference,
                                   specifications = specifications)

## can be plot with:
multiverse.plot(multiverse = multiverse,
                title = "README multiverse")

## End(Not run)

sdparsons/splithalf documentation built on Aug. 12, 2022, 9:31 a.m.