Basic function for conducting multiverse analyses of conditioning data
multiverse_cs(
cs1,
cs2,
data,
subj,
group = NULL,
cs_paired = NULL,
include_bayes = TRUE,
include_mixed = FALSE,
phase = "acquisition",
cutoff = c(0, 1, 2, 3),
print_output = TRUE,
correction = FALSE,
meta.effect = "d_to_eta2"
)The column name(s) of the conditioned responses for the first conditioned stimulus
The column name(s) of the conditioned responses for the second conditioned stimulus
A data frame containing all the relevant columns for the analyses
The name of the column including the participant numbers. Unique numbers are expected
the name of the group, if included, default to NULL
A character vector with the trials that were paired. Default is set to NULL, suggesting that there was full reinforcement
Whether the Bayesian analyses should be run. Default to TRUE
Whether the mixed model results should be run. Default to FALSE
The conditioned phase that the analyses refer to. Accepted values are acquisition, acq, extinction, or ext
A numeric vector of the cutoff criteria applied. Default to 0, 0.05, .1
Whether to print the output or not. Default set to TRUE
whether the Greenhouse-Geisser correction should be applied or not. Default to FALSE
How the meta-analytic effect should be computed.
A tibble with the following column names:
x: the name of the independent variable (e.g., cs)
y: the name of the dependent variable as this defined in the dv argument
exclusion: see exclusion argument
model: the model that was run (e.g., t-test)
controls: ignore this column for this test
method: the method used
p.value: the reported p-value
effect.size: the reported effect size
estimate: the estimate of the test run
statistic: the value of the test
conf.low: the lower confidence interval for the estimate
conf.high: the higher confidence interval for the estimate
framework: were the data analysed within a NHST or Bayesian framework?
data_used: a list with the data used for the specific test
In case of higher order interaction, only the highest order effect is returned.
In case the CSs include only 1 observation per participant, or of unequal numbers of CS trials, the function will return the warning ""Skipping ANOVA due to the number of trials for the cs1 and/or cs2."".
In principle the multiverse_cs function runs the universe_cs function multiple times, so whatever holds for the universe_cs -- e.g., in terms of warnings, holds for here as well.