bt_test_mf — bt_test_mf • multifear

Basic function for running the Bayesian t-tests included in the main analyses

bt_test_mf(
  cs1,
  cs2,
  data,
  subj,
  group = NULL,
  na.rm = FALSE,
  paired = TRUE,
  rscale = "medium",
  phase = "acquisition",
  dv = "scr",
  exclusion = "full data",
  cut_off = "full data"
)

Arguments

cs1: The column name(s) of the conditioned responses for the first conditioned stimulus
cs2: The column name(s) of the conditioned responses for the second conditioned stimulus
data: A data frame containing all the relevant columns for the analyses
subj: The name of the column including the participant numbers. Unique numbers are expected
group: the name of the group, if included, default to NULL
na.rm: Whether NAs should be removed, default to FALSE
paired: Whether the t-test refers to dependent (i.e., paired) or to independent sample(s). Default to TRUE
rscale: r scale to be used in the prior of the alternative hypothesis, default to "medium".
phase: The conditioned phase that the analyses refer to. Accepted values are acquisition, acq, extinction, or ext
dv: name of the measured conditioned response. Default to "SCR"
exclusion: Name of the data reduction procedure used. Default to full data
cut_off: cut off Name of the cut_off applied. Default to full data

Value

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 model that was run p.value: irrelevant here effect.size: irrelevant here effect.size.ma: irrelevant here estimate: the estimate of the test run statistic: the t-value 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

Details

This is a wrapper function function around the BayesFactor::ttestBF(), running multiple Bayesian t-tests. Similar to the t_test_mf function, the function will run different t-tests based on the phase that the t-tests refer to. So, in case of the acquisition phase, there will be a t-test of differences and positive differences, whereas for the extinction phase a t-test for differences and negative differences.

Examples

# Load example data
data(example_data)

# Paired sample t-tests
bt_test_mf(cs1 = "CSP1", cs2 = "CSM1", subj = "id", data = example_data)
#> # A tibble: 2 × 18
#>   x     y     exclusion cut_off   model      controls method p.value effect.size
#>   <chr> <chr> <chr>     <chr>     <chr>      <lgl>    <chr>  <lgl>   <lgl>      
#> 1 cs    scr   full data full data Bayesian … NA       Bayes… NA      NA         
#> 2 cs    scr   full data full data Bayesian … NA       Bayes… NA      NA         
#> # … with 9 more variables: effect.size.lci <lgl>, effect.size.hci <lgl>,
#> #   effect.size.ma <lgl>, estimate <dbl>, statistic <lgl>, conf.low <lgl>,
#> #   conf.high <lgl>, framework <chr>, data_used <list>

# Independent  sample t-tests
bt_test_mf(cs1 = "CSP1", cs2 = "CSM1", subj = "id",  group = "group", data = example_data)
#> # A tibble: 2 × 18
#>   x     y     exclusion cut_off   model      controls method p.value effect.size
#>   <chr> <chr> <chr>     <chr>     <chr>      <lgl>    <chr>  <lgl>   <lgl>      
#> 1 cs    scr   full data full data Bayesian … NA       Bayes… NA      NA         
#> 2 cs    scr   full data full data Bayesian … NA       Bayes… NA      NA         
#> # … with 9 more variables: effect.size.lci <lgl>, effect.size.hci <lgl>,
#> #   effect.size.ma <lgl>, estimate <dbl>, statistic <lgl>, conf.low <lgl>,
#> #   conf.high <lgl>, framework <chr>, data_used <list>