Create a table summarising the models for each variable — get_full_model

This macro-function runs various computations to model the variables in the Variable column of a long format data that have their values in a value column with a grouping variable (e.g., Group, Cluster) and an Age variable. It wraps the get_mean_sd(), get_bf_inclusion(), and get_contrast_bf() functions to compute the mean and standard deviation for each group, the Bayes Factor for Inclusion of the grouping variable and the Age covariate, and the contrasts between the levels of the grouping variable, respectively. The results are then formatted in a clean table with one row per variable and columns for the mean, standard deviation, Bayes Factor for Inclusion, and contrasts.

Usage

get_full_model_table(df_long, ...)

Arguments

df_long: A data frame in long format containing the variables to be analysed with a Variable column, a value column, a grouping variable (e.g., Group, Cluster), and an Age covariate. This is for example the output of get_longer(study_data).
...: A grouping variable (e.g. Group or Cluster) without quotes.

Value

A data frame summarising the models for each variable.

Examples

df_merged <- merge_clusters(
  df_raw     = study_data,
  df_red     = scale_reduce_vars(study_data),
  clustering = cluster_selected_vars(study_data)
)
df_long_example <-
 df_merged |>
 get_longer() |>
 dplyr::filter(Variable %in% c("VVIQ"))

cluster_models <- get_full_model_table(df_long_example, Cluster)
print(cluster_models)
#> # A tibble: 3 × 11
#>   Variable `A (Aphant.)` `B (Mixed)`  `C (Control)` Cluster   Age
#>   <fct>    <chr>         <chr>        <chr>           <dbl> <dbl>
#> 1 VVIQ     18.06 (4.1)   36.5 (17.56) 62.18 (8.66)     54.9 -1.11
#> 2 VVIQ     18.06 (4.1)   36.5 (17.56) 62.18 (8.66)     54.9 -1.11
#> 3 VVIQ     18.06 (4.1)   36.5 (17.56) 62.18 (8.66)     54.9 -1.11
#> # ℹ 5 more variables: `Cluster $\\times$ Age` <dbl>, Comparison <chr>,
#> #   `Difference ($\\Delta$)` <dbl>, `95% CrI` <chr>, `$log(BF_{10})$` <dbl>