Run the model to estimate the health impacts attributable to household air pollution for GCAM scenarios
calc_hap_impacts(
db_path = NULL,
query_path = "./inst/extdata",
db_name = NULL,
prj_name,
scen_name,
queries = "queries_rhap.xml",
final_db_year = 2100,
saveOutput = TRUE,
map = FALSE,
anim = TRUE,
HIA_var = "deaths",
normalized = FALSE,
by_gr = FALSE,
fit_result = NULL
)Path to the GCAM database
Path to the query file
Name of the GCAM database
Name of the rgcam project. This can be an existing project, or, if not, this will be the name
Vector names of the GCAM scenarios to be processed
Name of the GCAM query file. The file by default includes the queries required to run rfasst
Final year in the GCAM database (this allows to process databases with user-defined "stop periods")
Writes the emission files. By default=TRUE
Produce the maps. By default = FALSE
If set to TRUE, produces multi-year animations. By default=TRUE
Health metric to be predicted. c("deaths", "yll", "dalys"). By default = deaths
Transform the output to "normalized" values. By default = FALSE
Report within-country income-decile group shares of population, PM2.5+NOx exposure, and GDP as a CSV table plus a pie-chart snapshot at final_db_year (output/by_gr/). Descriptive only – it does not estimate group-level health impacts, since the regression model is fit on between-country variation and was never validated against within-country/sub-national variation. By default = FALSE
Optional pre-computed result from fit_model(HIA_var), e.g. from a previous call, to reuse instead of refitting the national regression from scratch. fit_model() doesn't depend on GCAM scenario data at all, so refitting it (including its Driscoll-Kraay vcov, which isn't cheap) on every calc_hap_impacts() call is pure waste when comparing many scenarios for the same HIA_var. Must have been fit with the same HIA_var as this call; a mismatch raises an error rather than silently using the wrong model. By default = NULL (fits internally).
Health impacts attributable to HAP for all the selected years, as a tibble of scenario, country, year, pred_var, pred_value, pred_value_normalized, reliability_ratio, and reliability. The last two flag how much of a country's predicted level rests on fit_model()'s bias-adder correction rather than on the regression's own covariates (reliability_ratio = abs(bias.adder)/naive prediction; reliability is a "high"/"medium"/"low" label over that ratio, thresholds 0.5 and 2) – "low" flags a country whose absolute-level prediction the model has little independent basis for, not necessarily a wrong number.