Create partial predictive plots for a set of fitted models.

These helper functions exist to make it easier to explore and understand the impact of each of the covariates used in the conmat GAM model.

Usage

partial_effects_sum(model, ages, ...)

# S3 method for class 'contact_model'
partial_effects_sum(model, ages, ...)

Arguments

model: A fitted model, or list of fitted models
ages: vector of integer ages
...: dots for future extension. Currently not used.

Value

data frame with 3 columns plus n rows based on expand.grid combination of ages. The column gam_total_term is the sum over the coefficients for that age bracket.

Details

Partial predictive plots give a visual representation of the effect of each covariate on the model, or (equivalently) the effect of each setting on the total contact matrix. Positive values indicate more contacts in that region of the matrix compared to the null case, while negative values indicate less.

Scales are not comparable across settings, as each setting has it's own intercept term, which is not accounted for in partial effects.

Examples

# Summed up partial effects (y-hat) for a single setting
partials_summed_home <- partial_effects_sum(
    polymod_setting_models$home,
    ages = 1:99
  )

autoplot(partials_summed_home)

# summed up partial effects (y-hat) for all settings
partials_summed_setting <- partial_effects_sum(
    polymod_setting_models,
    ages = 1:99
  )
autoplot(partials_summed_setting)