update logistic regression example fig

R/logistic_ex.R

@@ -31,7 +31,7 @@ toplo <- toprd |>
     prd = prds$fit,
     hival = prds$fit + 1.96 * prds$se.fit,
     loval = prds$fit - 1.96 * prds$se.fit, 
-    Turbidity = factor( Turbidity, labels = c('95th %tile','mean'), levels = unique(Turbidity) )
+    Turbidity = factor( Turbidity, labels = c('Elevated','Normal'), levels = unique(Turbidity) )
   ) 
 
 # get lines to show connection between hypothetical target and certainty of meeting threshold
@@ -45,24 +45,28 @@ tolns <- trgs |>
     prd = lnprds$fit,
     hival = lnprds$fit + 1.96 * lnprds$se.fit,
     loval = lnprds$fit - 1.96 * lnprds$se.fit, 
-    Turbidity = factor( Turbidity, labels = c('95th %tile','mean'), levels = unique(Turbidity) )
+    Turbidity = factor( Turbidity, labels = c('Elevated','Normal'), levels = unique(Turbidity) )
   )
 
 # plot
-p <- ggplot(toplo, aes(x = value, y = prd, fill = Turbidity, color = Turbidity, group = Turbidity)) +
-  geom_ribbon(aes(ymin = loval, ymax = hival), alpha = 0.2) +
-  geom_line() +
-  geom_segment(data = tolns, aes(x = value, xend = value, y = 0, yend = hival), linetype = 'dashed', inherit.aes = F) +
-  geom_segment(data = tolns, aes(x = 0, xend = value, y = loval, yend = loval), linetype = 'dashed') +  
-  geom_segment(data = tolns, aes(x = 0, xend = value, y = hival, yend = hival), linetype = 'dashed') +
+p <- ggplot(toplo, aes(x = value, y = prd, group = Turbidity)) +
+  geom_ribbon(aes(ymin = loval, ymax = hival, fill = Turbidity), alpha = 0.2) +
+  geom_line(aes(color = Turbidity)) +
+  geom_segment(data = tolns, aes(color = Turbidity, x = value, xend = value, y = 0, yend = prd), linetype = 'dashed', inherit.aes = F) +
+  geom_segment(data = tolns, aes(color = Turbidity, x = 0, xend = value, y = prd, yend = prd), linetype = 'dashed') +  
+  # geom_segment(data = tolns, aes(x = 0, xend = value, y = hival, yend = hival), linetype = 'dashed') +
+  scale_color_manual(values = c('tomato1', 'dodgerblue')) +
+  scale_fill_manual(values = c('tomato1', 'dodgerblue')) +
+  coord_cartesian(xlim = c(0, 200)) +
   theme_minimal() +
   theme(legend.position = 'top') +
   labs(
-    x = 'Monthly TN load', 
-    y = 'Probability of attaining 9.3 µg/L',
-    caption = 'Hypothetical example of how to use model to set TN load targets'
-  ) 
+    x = 'Monthly nitrogen load', 
+    fill = 'Climate condition', 
+    color = 'Climate condition',
+    y = 'Probability of meeting water quality goal'
+  )
 
-png(here::here('figs/logisticex.png'), width = 6, height = 4, units = 'in', res = 300)
+png(here::here('figs/logisticex.png'), width = 5, height = 4, units = 'in', res = 300)
 print(p)
 dev.off()