antivax-attitudes

Reanalyses of data from Horne, Powell, Hummel & Holyoak (2015)
git clone https://git.eamoncaddigan.net/antivax-attitudes.git
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commit f89ae4e4a83aa70abd599016c6aedf770c0d382e
parent e906e43be9f64d6654248590b62f1c13515247da
Author: eamoncaddigan <eamon.caddigan@gmail.com>
Date:   Sun, 30 Aug 2015 21:02:00 -0400

Diagnosing the chains.

Diffstat:
Mantivax-attitudes.Rmd | 18++++++++++++++++++
1 file changed, 18 insertions(+), 0 deletions(-)

diff --git a/antivax-attitudes.Rmd b/antivax-attitudes.Rmd @@ -21,6 +21,7 @@ library(readxl) library(tidyr) suppressMessages(library(dplyr)) library(ggplot2) +library(runjags) # Generates warnings for the Ps who didn't do day 2 suppressWarnings(expData <- read_excel("Vacc_HPHH_publicDataset.xlsx", sheet = 2)) @@ -275,3 +276,19 @@ if (file.exists(saveName)) { } ``` +When model parameters are fit using Monte Carlo methods, it's important to inspect the results of the sampling procedure to make sure it's well-behaved. Here's an example of one parameter, the intercept for the mean of the cummulative normal. + +```{r, echo=FALSE} +{ +# So many hoops to make Kruschke's code quiet +sink("/dev/null") +suppressMessages(source("DBDA2E-utilities.R")) +sink() +} + +diagMCMC(codaObject = codaSamples, + parName = "b0", + saveName = NULL) +``` + +It's also important to check the predictions made by a model. If the model doesn't describe your data well, there's likely a problem. Here are response histograms for each question, +\ No newline at end of file