EJ
About
- Username
- EJ
- Joined
- Visits
- 2,557
- Last Active
- Roles
- Member, Administrator, Moderator
Comments
-
Yes that makes perfect sense! (note that the BF says something about the evidence from the data -- if you want the probability of a hypothesis being true then you need to take the prior model plausibility into account). So the safest conclusion is t…
-
Hi Mirjam, This looks like an issue for our GitHub page. I'll create the issue for you. Cheers, E.J.
-
Are you using a binomial model, and do you want to use a beta prior? You can set the "a" and "b" parameters of the prior distribution. If you start from a uniform distribution (big if) then, after seeing 9/10 "successes"…
-
Hi Andrew, Right now, you can't. In the future we aim to expand the output , as we have recently done for linear regression (up to 0.8.5., we only reported BFs; as of 0.8.6, we also have posterior distributions). Richard may now how you can use the…
-
You should check yourself, there is no gold standard; it also depends on the numbers themselves -- you may not care whether the BF is one or two billion. I am not sure about the difference between "samples from the posterior" and "i…
-
Thanks for bringing this to our attention. We now have a team member working to upgrade this functionality. Cheers, E.J.
-
Yes, it is based on conting! It uses Reversible Jump MCMC, and under uniform prior probabilities for the models, the number of visits relates directly to the posterior probabilities (and this also given the BF). E.J.
-
Hi Andrew, There's a series of papers on planning experiments until a BF crosses a bound. If you look on my website you'll see a few with Felix Schoenbrodt. This is the latest one: https://psyarxiv.com/aqr79 Cheers, E.J.
-
For many regression/ANOVA analyses the integral only goes over one parameter, so full MCMC is not needed. I am not sure what the BayesFactor package does for repeated measures ANOVA though -- should be in the documentation. But yes, BayesFactor asse…
-
I'll pass this on to the person who developed the meta-analysis module. E.J.
-
Hi Jo, No, this has not been implemented yet. It would be straightforward to implement for the chi-square test, but for the ANOVA the problem is that the check may take a very long time. So we probably need to pick just a few points and plot the re…
-
I am not 100% sure. The "real/royal" Bayesian way is to change the data-generating process, for instance by assuming that the data are t-distributed instead of normally distributed, or assuming . Some classical robust methods throw away da…
-
Hi Mark, Thanks for your kind words, and thanks for reporting this issue. Could you perhaps send us your .jasp file, so we can reproduce the anomaly? It would be even better if you could post this on our GitHub page, as all the JASP team members th…
-
Basically you just copy-paste Lavaan code. We are working on a video and gif to demonstrate the process.
-
Jeffreys does mention this in his 1961 book, but I am not aware of a modern Bayes factor test. It is straightforward to do estimation using JAGS/Stan, but the test requires a bit more development. It is on our radar. E.J.
-
Hi eniseg2, Right now we only have "classical" SEM in JASP; we will of course include the Bayesian echo at some point (through blavaan, probably). Cheers, E.J.
-
Hi Kevin, JASP already provides effect sizes. To clarify, for the Bayesian methods JASP generally provides the following: (1) The Bayes factor, a single number that quantifies the evidence for the presence/absence of an effect; (2) A posterior dist…
-
For the record, the answer by Richard is here: https://twitter.com/richarddmorey/status/956574998898139138 Richard: "if X is the t-test prior scale, then X/sqrt(2) is the ANOVA scale, and X/2 is the corresponding regression scale." You: …
-
I would add the nuisance parameter (covariate) to the null model, and then interpret the outcome as you would do for the ANOVA. If you are also interested in the covariate itself, you can keep it out of the null model. If you send a screenshot of th…
-
That's great to hear -- I must admit I personally have used the BayesFactor package mostly indirectly, through JASP. Thanks for posting the solution. Cheers, E.J.
-
First and foremost, it is important to be aware that if IQ differs between the groups, adding it as a covariate does not "control" for it. ANCOVA was meant to be used when the covariate (IQ) is important, but does not differ between the gr…
-
Hi Michael, Correct -- the usual BF involves a null hypothesis and is therefore a test of presence/absence, not one of magnitude. Cheers, E.J.
-
We have a paper about effect size for ANOVA (minor revision); For the t-test, the standard output gives the posterior distribution for Cohen's delta (on the population level). The interpretation in terms of what is large won't differ (and depends on…
-
Good point. We hope to offer a generic solution for this issue in the future. For now, you can of course look at my own papers, and those of Dienes, Rouder, and Morey, for instance. A google search should also be effective. Cheers, E.J.
-
Hi Andrew, There is this paper: http://pcl.missouri.edu/node/133 We are currently revamping the Bayesian linear regression, and a new release (a few weeks away) will present more cool options. So stay tuned. Cheers, E.J.
-
Hi Michael, The updated priors are on the model parameters. But this happens implicitly, that is, the BF you obtain is the same one that you could also have obtained in case you had been able to properly update the prior distributions on the model p…
-
That would be your best bet. You could add my reply and ask whether he agrees.
-
Hi Kevin, You can run this past Richard Morey to be sure, but if you compare the scale for the ANOVA to that of the t-test you see that they differ by a factor of 1/2. So if you take the t-test scales and divide by 2 you should be good. Again, you …
-
Hi Michael, This is tricky. One thing you could do is compute the BF for the initial sample, and then for the complete sample; dividing them out yields the BF for the replication sample after updating the priors with the initial sample. This only w…
-
So when you look at BF01 and the "H0" is the null model, values higher than 1 are evidence for the null. So the BF01 = 43.15 is evidence for the null model that has no predictors. But the null model is not the best model -- this is the mod…