Skip to contents

Get started

Source: vignettes/aphantasiaEmotions.Rmd
aphantasiaEmotions.Rmd

This page is a short, practical introduction to aphantasiaEmotions: how to load it, what the data looks like, and how to run one basic model. It is not the place to learn about the study’s findings; for that, see the Extended Online Report, starting with how this study found its shape. This page is about using the package, for anyone who wants to reproduce or build on the analyses directly in R.

The data

The package ships with one built-in dataset, all_data: the pooled sample from five studies, combined and cleaned for this project’s analyses.

dplyr::glimpse(all_data)
#> Rows: 1,478
#> Columns: 16
#> $ study        <fct> burns, burns, burns, burns, burns, burns, burns, burns, b…
#> $ lang         <fct> en, en, en, en, en, en, en, en, en, en, en, en, en, en, e…
#> $ id           <fct> subj_burns_1, subj_burns_2, subj_burns_3, subj_burns_4, s…
#> $ sex          <fct> female, female, female, female, male, female, female, fem…
#> $ gender       <fct> female, female, female, female, male, female, female, fem…
#> $ age          <dbl> 62, 39, 45, 57, 40, 86, 59, 50, 25, 44, 49, 57, 45, 69, 4…
#> $ vviq         <dbl> 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 1…
#> $ tas          <dbl> 57, 33, 94, 30, 62, 48, 58, 67, 51, 37, 35, 39, 44, 74, 8…
#> $ tas_identify <dbl> 23, 7, 33, 7, 17, 22, 19, 30, 22, 11, 7, 11, 20, 31, 27, …
#> $ tas_describe <dbl> 16, 5, 25, 8, 19, 14, 16, 20, 13, 8, 8, 10, 8, 22, 21, 18…
#> $ tas_external <dbl> 18, 21, 36, 15, 26, 12, 23, 17, 16, 18, 20, 18, 16, 21, 3…
#> $ tas_group    <fct> typical_tas, typical_tas, alexithymia, typical_tas, alexi…
#> $ vviq_group_4 <fct> aphantasia, aphantasia, aphantasia, aphantasia, aphantasi…
#> $ vviq_group_3 <fct> aphantasia, aphantasia, aphantasia, aphantasia, aphantasi…
#> $ vviq_group_2 <fct> aphantasia, aphantasia, aphantasia, aphantasia, aphantasi…
#> $ items        <list> [<tbl_df[1 x 36]>], [<tbl_df[1 x 36]>], [<tbl_df[1 x 36]…

A few columns worth knowing about from the start:

  • study identifies which of the five source datasets a row comes from ("burns", "monzel", "ruby", "mas", "kvamme") — see the sample description page for what each one is.
  • vviq is the raw Vividness of Visual Imagery Questionnaire total score (16-80, where 16 is a complete absence of voluntary visual imagery).
  • tas is the raw Toronto Alexithymia Scale (TAS-20) total score.
  • vviq_group_4 and vviq_group_2 are categorical groupings of vviq at different levels of granularity, used throughout this project’s more traditional, categorical analyses.

Fitting a model

Most of this project’s statistical work uses fit_brms_model(), a thin wrapper around brms::brm() with this project’s own conventions for chains, iterations, and priors already set as sensible defaults. Here is the simplest possible example, a plain linear model of alexithymia as a function of imagery vividness:

simple_model <- fit_brms_model(
  formula = tas ~ vviq,
  data = all_data,
  prior = brms::prior(normal(0, 20), class = "b")
)

summary(simple_model)

This example is not run on this page (fitting a real Bayesian model takes real time), but it will work as written if you run it yourself. See ?fit_brms_model for the full set of arguments, including how to control the number of chains and iterations.

Where to go next