Big update to simulation code to use create_posterior_func directly r…

…ather than separate functions

Also should work with simulating stratifications and multiple biomarker groups

DOES NOT yet work with measurement offsets, as this doesn't work for multiple biomarker groups (yet)

R/plots_other.R

@@ -46,7 +46,12 @@ plot_antibody_data <- function(antibody_data,
     } else {
         antibody_data$biomarker_group <- 1
     }
-    p1 <- ggplot(antibody_data[antibody_data$individual %in% samps, ]) +
+
+    ## For birth dates
+    DOBs <- unique(antibody_data[antibody_data$individual %in% samps, c("individual","birth")])
+
+    p1 <- ggplot(antibody_data[antibody_data$individual %in% samps, ]) + 
+      geom_vline(data = DOBs, aes(xintercept = birth,linetype="Birth"),col = "purple") +
       geom_rect(data=measurement_ranges,aes(ymin=max_measurement,ymax=max_measurement+1),xmin=0,xmax=max_x,fill="grey70") +
       geom_rect(data=measurement_ranges,aes(ymin=min_measurement-1,ymax=min_measurement),xmin=0,xmax=max_x,fill="grey70") 
 
@@ -76,7 +81,7 @@ plot_antibody_data <- function(antibody_data,
 
       p1 <- p1 + geom_vline(data = melted_inf_hist[melted_inf_hist$individual %in% samps, ], 
                             aes(xintercept = variable,linetype="Known infection time"), 
-                            col = "orange",linetype="dashed")
+                            col = "orange")
     }
     breaks <- seq(floor(min(measurement_ranges$min_measurement)), ceiling(max(measurement_ranges$max_measurement)),by=2)
     p1 <- p1 +
@@ -95,7 +100,7 @@ plot_antibody_data <- function(antibody_data,
       coord_cartesian(xlim=time_range) +
       scale_y_continuous(expand=c(0,0),breaks=breaks) + 
       scale_color_viridis_d(name="Biomarker ID") + 
-      scale_linetype_manual(name="",values=c("Known infection time"="dashed"))
+      scale_linetype_manual(name="",values=c("Known infection time"="dashed","Birth"="solid"))
     return(p1)
 }
 

R/posteriors.R

@@ -356,8 +356,8 @@ create_posterior_func <- function(par_tab,
             antigenic_map_long <- array(dim=c(length(possible_biomarker_ids)^2,n_biomarker_groups,n_demographic_groups))
             antigenic_map_short <- array(dim=c(length(possible_biomarker_ids)^2,n_biomarker_groups,n_demographic_groups))
 
-            cr_longs <- as.matrix(theta[,which(par_names_theta_all=="cr_long")])
-            cr_shorts <- as.matrix(theta[,which(par_names_theta_all=="cr_short")])
+            cr_longs <- matrix(theta[,which(par_names_theta_all=="cr_long")],nrow=length(unique_groups))
+            cr_shorts <- matrix(theta[,which(par_names_theta_all=="cr_short")],nrow=length(unique_groups))
             for(group in unique_groups){
               for(biomarker_group in unique_biomarker_groups){
                 antigenic_map_long[,biomarker_group,group] <- create_cross_reactivity_vector(antigenic_map_melted[[biomarker_group]], cr_longs[group,biomarker_group])
@@ -467,9 +467,9 @@ create_posterior_func <- function(par_tab,
           antigenic_map_long <- array(dim=c(length(possible_biomarker_ids)^2,n_biomarker_groups,n_demographic_groups))
           antigenic_map_short <- array(dim=c(length(possible_biomarker_ids)^2,n_biomarker_groups,n_demographic_groups))
 
-          cr_longs <- as.matrix(theta[,which(par_names_theta_all=="cr_long")])
-          cr_shorts <- as.matrix(theta[,which(par_names_theta_all=="cr_short")])
-            
+          cr_longs <- matrix(theta[,which(par_names_theta_all=="cr_long")],nrow=length(unique_groups))
+          cr_shorts <- matrix(theta[,which(par_names_theta_all=="cr_short")],nrow=length(unique_groups))
+
           for(group in unique_groups){
             for(biomarker_group in unique_biomarker_groups){
               antigenic_map_long[,biomarker_group,group] <- create_cross_reactivity_vector(antigenic_map_melted[[biomarker_group]], cr_longs[group,biomarker_group])
@@ -561,8 +561,8 @@ create_posterior_func <- function(par_tab,
             antigenic_map_long <- array(dim=c(length(possible_biomarker_ids)^2,n_biomarker_groups,n_demographic_groups))
             antigenic_map_short <- array(dim=c(length(possible_biomarker_ids)^2,n_biomarker_groups,n_demographic_groups))
 
-            cr_longs <- as.matrix(theta[,which(par_names_theta_all=="cr_long")])
-            cr_shorts <- as.matrix(theta[,which(par_names_theta_all=="cr_short")])
+            cr_longs <- matrix(theta[,which(par_names_theta_all=="cr_long")],nrow=length(unique_groups))
+            cr_shorts <- matrix(theta[,which(par_names_theta_all=="cr_short")],nrow=length(unique_groups))
             for(group in unique_groups){
               for(biomarker_group in unique_biomarker_groups){
                   antigenic_map_long[,biomarker_group,group] <- create_cross_reactivity_vector(antigenic_map_melted[[biomarker_group]], cr_longs[group,biomarker_group])