Skip to content
Snippets Groups Projects
Commit 076d9e01 authored by Jeffrey Stevens's avatar Jeffrey Stevens
Browse files

Post-workshop revisions

- Added Bayesian tSpectacles
- Added assumptions same as frequentist
- Added JASP priors plot
parent d95523ec
No related branches found
No related tags found
No related merge requests found
...@@ -372,6 +372,7 @@ We have very strong evidence for the model with no interaction. ...@@ -372,6 +372,7 @@ We have very strong evidence for the model with no interaction.
* *Psychonomic Bulletin & Review* special issue on [Bayesian methods for advancing psychological science](https://link.springer.com/journal/13423/25/1/page/1) * *Psychonomic Bulletin & Review* special issue on [Bayesian methods for advancing psychological science](https://link.springer.com/journal/13423/25/1/page/1)
* [`BayesFactor`](https://richarddmorey.github.io/BayesFactor/) package * [`BayesFactor`](https://richarddmorey.github.io/BayesFactor/) package
* [JASP](https://jasp-stats.org/) statistical software * [JASP](https://jasp-stats.org/) statistical software
* [Bayesian Spectacles](https://www.bayesianspectacles.org/) blog
* [Understanding Bayes](https://alexanderetz.com/understanding-bayes/) blog * [Understanding Bayes](https://alexanderetz.com/understanding-bayes/) blog
*** ***
......
No preview for this file type
...@@ -255,8 +255,17 @@ $\frac{P(D|H_0)}{P(D|H_1)}$ ...@@ -255,8 +255,17 @@ $\frac{P(D|H_0)}{P(D|H_1)}$
## Potential issues ## Potential issues
\pause \pause
* Results can be sensitive to priors * Rely on same assumptions as frequentist statistics
\pause \pause
* Results can be sensitive to priors
\centering \includegraphics[height=1.5in]{../figures/jasp_priors_plot.png}
## Potential issues
* Rely on same assumptions as frequentist statistics
* Results can be sensitive to priors
* Still use cutoffs * Still use cutoffs
\pause \pause
* Not accepted in the field * Not accepted in the field
...@@ -387,6 +396,7 @@ $BF_{interaction} = \frac{16}{14} = 1.14$ ...@@ -387,6 +396,7 @@ $BF_{interaction} = \frac{16}{14} = 1.14$
* *Psychonomic Bulletin & Review* special issue on [Bayesian methods for advancing psychological science](https://link.springer.com/journal/13423/25/1/page/1) * *Psychonomic Bulletin & Review* special issue on [Bayesian methods for advancing psychological science](https://link.springer.com/journal/13423/25/1/page/1)
* [`BayesFactor`](https://richarddmorey.github.io/BayesFactor/) package * [`BayesFactor`](https://richarddmorey.github.io/BayesFactor/) package
* [JASP](https://jasp-stats.org/) statistical software * [JASP](https://jasp-stats.org/) statistical software
* [Bayesian Spectacles](https://www.bayesianspectacles.org/) blog
* [Understanding Bayes](https://alexanderetz.com/understanding-bayes/) blog * [Understanding Bayes](https://alexanderetz.com/understanding-bayes/) blog
* Anderson, D. R. (2008). [Model Based Inference in the Life Sciences]( http://www.springer.com/life+sci/ecology/book/978-0-387-74073-7). New York: Springer. * Anderson, D. R. (2008). [Model Based Inference in the Life Sciences]( http://www.springer.com/life+sci/ecology/book/978-0-387-74073-7). New York: Springer.
* Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). [Bayesian Data Analysis](http://www.stat.columbia.edu/~gelman/book/) (Third Edition). Boca Raton, FL: CRC Press. * Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). [Bayesian Data Analysis](http://www.stat.columbia.edu/~gelman/book/) (Third Edition). Boca Raton, FL: CRC Press.
......
No preview for this file type
figures/jasp_priors_plot.png

234 KiB

...@@ -151,21 +151,17 @@ ggplot(datafile, aes(x = rt, y = choice)) + ...@@ -151,21 +151,17 @@ ggplot(datafile, aes(x = rt, y = choice)) +
geom_point() + # plot individual points in grey geom_point() + # plot individual points in grey
geom_smooth(method = "lm") + # plot regression line and CI geom_smooth(method = "lm") + # plot regression line and CI
labs(x = "Reaction time", y = "Choice proportion") labs(x = "Reaction time", y = "Choice proportion")
ggplot(datafile, aes(x = pre, y = choice)) + ggplot(datafile, aes(x = age, y = choice)) +
geom_point() + # plot individual points in grey geom_point() + # plot individual points in grey
geom_smooth(method = "lm") + # plot regression line and CI geom_smooth(method = "lm") + # plot regression line and CI
labs(x = "Pre ratings", y = "Choice proportion") labs(x = "Age", y = "Choice proportion")
ggplot(datafile, aes(x = post, y = choice)) +
geom_point() + # plot individual points in grey
geom_smooth(method = "lm") + # plot regression line and CI
labs(x = "Post ratings", y = "Choice proportion")
## Frequentist ## Frequentist
summary(lm(choice ~ rt + pre + post, data = datafile)) # conduct frequentist linear regression summary(lm(choice ~ rt + age, data = datafile)) # conduct frequentist linear regression
## Bayesian ## Bayesian
regressionBF(choice ~ rt + pre + post, data = datafile) # conduct Bayesian linear regression regressionBF(choice ~ rt + age, data = datafile) # conduct Bayesian linear regression
head(regressionBF(choice ~ rt + pre + post, data = datafile)) # order results by BF head(regressionBF(choice ~ rt + age, data = datafile)) # order results by BF
......
0% Loading or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment