Include confidence interval. Ref #94

Author: usr110

R/gen_pa_rr.R

@@ -7,7 +7,7 @@
 #' @return data frame of relative risks per person per disease
 #' 
 #' @export
-gen_pa_rr <- function(mmets_pp){
+gen_pa_rr <- function(mmets_pp, conf_int = F){
   ### iterating over all all disease outcomes
   dose_columns <- match(paste0(SCEN_SHORT_NAME, '_mmet'),colnames(mmets_pp))
   doses_vector <- unlist(data.frame(mmets_pp[,dose_columns]))
@@ -30,12 +30,18 @@ gen_pa_rr <- function(mmets_pp){
 
     # Add quantile as the parameter
     return_vector <- drpa::dose_response(cause = pa_dn, outcome_type = outcome,
-                                         dose = doses_vector, quantile = quant)
+                                         dose = doses_vector, quantile = quant, confidence_intervals = conf_int)
     ##RJ take segments of returned vector corresponding to scenario
     for (i in 1:length(SCEN_SHORT_NAME)) {
       scen <- SCEN_SHORT_NAME[i]
       mmets_pp[[paste('RR_pa', scen, pa_n, sep = '_')]] <- return_vector$rr[(1 + (i - 1)*nrow(mmets_pp)):(i*nrow(mmets_pp))]
+      
+      if (conf_int){
+        mmets_pp[[paste('RR_pa', scen, pa_n, 'lb', sep = '_')]] <- return_vector$lb[(1 + (i - 1)*nrow(mmets_pp)):(i*nrow(mmets_pp))]
+        mmets_pp[[paste('RR_pa', scen, pa_n, 'ub', sep = '_')]] <- return_vector$ub[(1 + (i - 1)*nrow(mmets_pp)):(i*nrow(mmets_pp))]
+      }
     }
+    
   }
   mmets_pp 
 }