Fit and report Bayes Factors for associations with education, fields and occupations
Source:R/get_association_models.R
get_association_models.RdThis function computes the Bayes Factors for associations between a grouping
variable (e.g., Group, Cluster) and the variables education,
field, and occupation in a data frame. It uses the
BayesFactor::contingencyTableBF() function to compute the Bayes Factor
for each variable and returns a tidy data frame with the results.
Arguments
- df
A data frame containing the variables
education,field, andoccupation, along with the grouping variable.- groups
A grouping variable (e.g.
GrouporCluster) without quotes.- type
A character string specifying the type of contingency table model to use. Default is
"indepMulti", which indicates an independent multinomial model. See?BayesFactor::contingencyTableBFfor more details and options.
Value
A data frame summarising the Bayes Factors for associations
between the grouping variable and the variables education, field, and
occupation, with the variable name in a Variable column, the
contingency table in a table column, and the log Bayes Factor in a
log_bf10 column. The variable names are capitalised for better readability.
Examples
df_merged <- merge_clusters(
df_raw = study_data,
df_red = scale_reduce_vars(study_data),
clustering = cluster_selected_vars(study_data)
)
get_association_models(df_merged, group)
#> # A tibble: 3 × 4
#> # Rowwise: Variable
#> Variable data table log_bf10
#> <chr> <list> <list> <dbl>
#> 1 Education <tibble [96 × 2]> <tibble [6 × 3]> -4.88
#> 2 Field <tibble [96 × 2]> <tibble [11 × 3]> -5.41
#> 3 Occupation <tibble [96 × 2]> <tibble [9 × 3]> -4.37
get_association_models(df_merged, cluster)
#> # A tibble: 3 × 4
#> # Rowwise: Variable
#> Variable data table log_bf10
#> <chr> <list> <list> <dbl>
#> 1 Education <tibble [96 × 2]> <tibble [6 × 4]> -7.44
#> 2 Field <tibble [96 × 2]> <tibble [11 × 4]> -6.06
#> 3 Occupation <tibble [96 × 2]> <tibble [9 × 4]> -3.88
get_association_models(df_merged, subcluster)
#> # A tibble: 3 × 4
#> # Rowwise: Variable
#> Variable data table log_bf10
#> <chr> <list> <list> <dbl>
#> 1 Education <tibble [96 × 2]> <tibble [6 × 5]> -9.84
#> 2 Field <tibble [96 × 2]> <tibble [11 × 5]> -7.7
#> 3 Occupation <tibble [96 × 2]> <tibble [9 × 5]> -7.06
get_association_models(df_merged, group)$table
#> [[1]]
#> # A tibble: 6 × 3
#> value Control Aphantasic
#> <fct> <int> <int>
#> 1 Other 5 4
#> 2 Upper secondary 1 0
#> 3 Post-secondary 9 5
#> 4 Bachelor 17 17
#> 5 Master 17 16
#> 6 Doctorate 2 3
#>
#> [[2]]
#> # A tibble: 11 × 3
#> value Control Aphantasic
#> <fct> <int> <int>
#> 1 Generic programmes 4 4
#> 2 Education 1 1
#> 3 Arts, humanities 9 12
#> 4 Social sciences, journalism, information 11 4
#> 5 Business, Administration, Law 10 8
#> 6 Natural sciences, mathematics, statistics 6 4
#> 7 Information, communication technologies 4 4
#> 8 Engineering, manufacturing, construction 3 3
#> 9 Agriculture, forestry, fisheries, veterinary 1 1
#> 10 Health and Welfare 2 3
#> 11 Services 0 1
#>
#> [[3]]
#> # A tibble: 9 × 3
#> value Control Aphantasic
#> <fct> <int> <int>
#> 1 No answer 1 1
#> 2 Unemployed 1 1
#> 3 Student 20 12
#> 4 Science and Engineering 2 4
#> 5 Health 2 6
#> 6 Teaching 4 3
#> 7 Business, Administration 9 10
#> 8 Information, Communications 8 6
#> 9 Social, Cultural, Legal 4 2
#>