antivax-attitudes

commit 9d6f834859cd7c3fa6ebf4057372609c5d71f2ee
parent f2ec3e6d5188be0057b4a8928f6f8dfaa6e2727e
Author: eamoncaddigan <eamon.caddigan@gmail.com>
Date:   Sat, 29 Aug 2015 22:58:37 -0400

Seperated post. calculation from plotting

Diffstat:
M
ggPostPlot.R
 | 
35
+++++++++++++++++++++++------------

1 file changed, 23 insertions(+), 12 deletions(-)
diff --git a/ggPostPlot.R b/ggPostPlot.R
@@ -3,8 +3,9 @@
 library(ggplot2)
 library(dplyr)
 
-ggPosteriorPredictive <- function(modelData, codaObject, 
-                                  x1Level, x2Level = NA, x3Level = NA) {
+# Find the responses and the posterior predictions for the given factor levels
+calcPosteriorPredictive <- function(modelData, codaObject,
+                                    x1Level, x2Level = NA, x3Level = NA) {
   mcmcMat <- as.matrix(codaObject)
   
   # Separate the observed counts for the given levels
@@ -32,7 +33,7 @@ ggPosteriorPredictive <- function(modelData, codaObject,
     }
   }
   sigma <- mcmcMat[, paste0("sigma[", x1Level, "]")]
-
+  
   # Find the HDI and mean of the posterior probabilities of each of the y levels
   # (Stolen from Kruschke)
   chainLength <- nrow(mcmcMat)
@@ -46,25 +47,35 @@ ggPosteriorPredictive <- function(modelData, codaObject,
   outHdi <- apply(outProb, 2, HDIofMCMC)
   outMean <- apply(outProb, 2, mean, na.rm=TRUE)
   
-  # Create the plot
+  # Create a data.frame with the posterior data
   plotData <- cellData %>%
     count(response) %>% 
-    left_join(data_frame(response = 1:max(y)), ., by = "response")
-  plotData <- plotData %>%
+    left_join(data_frame(response = 1:max(y)), ., by = "response") %>%
     mutate(n = ifelse(is.na(n), 0, n),
            response_proportion = n / sum(n),
            posterior_mean = outMean,
            posterior_hdi_low = outHdi[1,],
            posterior_hdi_high = outHdi[2,])
+  return(plotData)
+}
+
+
+# Plot the responses and posterior predictions for the given factor levels
+ggPosteriorPredictive <- function(modelData, codaObject, 
+                                  x1Level, x2Level = NA, x3Level = NA) {
+  plotData <- calcPosteriorPredictive(modelData, codaObject,
+                                      x1Level, x2Level, x3Level)
+  numLevels = length(unique(plotData[["response"]]))
+
   p <- ggplot(plotData, aes(x = response, y = response_proportion)) + 
-    geom_bar(stat = "identity", binwidth=1, color="white") + 
+    geom_bar(stat = "identity", binwidth=1, color="white", fill="skyblue") + 
     geom_point(aes(y = posterior_mean), 
-               size=3, color="red") + 
+               size=5, color="red") + 
     geom_errorbar(aes(ymin = posterior_hdi_low, ymax = posterior_hdi_high), 
-                  size=2, color="red", width=0) + 
-    scale_x_continuous(breaks = 1:6) + # XXX hardcoded 6
+                  size=3, color="red", width=0) + 
+    scale_x_continuous(breaks = 1:numLevels) +
     scale_y_continuous(limits = c(0, 1)) +
-    xlab("Response") + 
-    ylab("Proportion of Responses")
+    xlab("Response Level") + 
+    ylab("Proportion")
   return(p)
 }