Behind on Bills

Behind on bills is a supplementary module in Minos. It is based on the xphsdba variable from Understanding Society, which represents an individuals ability to pay their bills on time. The individual can then respond to say that they are up to date with all bills (1), behind with some bills (2), or behind with all bills (3).

#discrete_barplot(obs, 'loneliness')
order <- c(1, 2, 3)
discrete_barplot(obs, order)

Transition Model

To predict the next state of behind_on_bills we use a Random Forest Ordinal model from the ranger package in R.

Formula:

\[\begin{split}behind\_on\_bills \sim behind\_on\_bills\_last + age + sex + ethnicity + region + education\_state\\ + hh\_income + job\_sec\end{split}\]

plot_rfo_importance(model)

Fig. 45 plot of chunk bob_model_summary

Validation

handover_ordinal(raw.dat, base.dat, v)

pivoted <- combine_and_pivot_long(df1 = cv,
                                       df1.name = 'simulated',
                                       df2 = raw,
                                       df2.name = 'raw',
                                       var = v)

cv_ordinal_plots(pivoted.df = pivoted,
                 var = v,
                 save = FALSE)

## `summarise()` has grouped output by 'time', 'scenario'. You can override using
## the `.groups` argument.

Results

Random Forest Ordinal models from the ranger package cannot provide a summary like some other models can, so instead we will look at plots of observed vs predicted values as well as the importance of each variable in the resulting model.

cumulative_link_plot(obs, preds)

## `geom_smooth()` using formula = 'y ~ x'