antivax-attitudes

commit 0fa77a29683e1d68163bc8d9cee6410dcd026611
parent 30c5f346199548d8aed681b853fcb2e72cd91484
Author: eamoncaddigan <eamon.caddigan@gmail.com>
Date:   Tue, 15 Sep 2015 21:58:31 -0400

Preintervention samples.

Diffstat:
M
antivax-bootstrap.Rmd
 | 
29
+++++++++++++++++++++++------

1 file changed, 23 insertions(+), 6 deletions(-)
diff --git a/antivax-bootstrap.Rmd b/antivax-bootstrap.Rmd
@@ -21,10 +21,6 @@ library(tidyr)
 library(dplyr)
 library(ggplot2)
 library(gridExtra)
-library(rjags)
-library(runjags)
-source("DBDA2E-utilities.R")
-source("ggPostPlot.R")
 
 # Clean and process the data --------------------------------------------------
 
@@ -80,4 +76,26 @@ Some of my friends offered insightful comments, and one [pointed out](https://tw
 
 ![Posteror of final score differences](https://pbs.twimg.com/media/COE2e8bUkAEeu8b.png:large)
 
-Still, interpreting differences in parameter values isn't always straightforward, so I thought it'd be fun to try a different approach. Instead of modeling the (process that generated the) data, we can use bootstrapping to estimate population parameters using the sample. [Bootstrapping is cool and simple](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)), and it's one of those great techniques that people would've been using all along had computers been around in the early days of statistics. 
-\ No newline at end of file
+Still, interpreting differences in parameter values isn't always straightforward, so I thought it'd be fun to try a different approach. Instead of modeling the (process that generated the) data, we can use bootstrapping to estimate population parameters using the sample. [Bootstrapping is cool and simple](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)), and it's one of those great techniques that people would've been using all along had computers been around in the early days of statistics.
+
+```{r }
+# Bootstrap to find the probability that each response will be given to pre-test
+# questions.
+numBootstraps <- 1e5
+numObservations <- nrow(questionnaireData)
+numResponseValues <- length(unique(questionnaireData$response))
+
+preinterventionData <- matrix(data = 0,
+                              nrow = numBootstraps, 
+                              ncol = numResponseValues)
+for (ii in seq_len(numBootstraps)) {
+  bootSamples <- sample(questionnaireData$response, 
+                        numObservations, 
+                        replace = TRUE)
+  bootTable <- table(bootSamples)
+  preinterventionData[ii, as.integer(names(bootTable))] <- bootTable
+}
+preinterventionData <- preinterventionData / numObservations
+
+```
+