EJ
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- EJ
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Comments
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Instructions on how you can become a tester: https://jasp-stats.org/manual-for-external-testers/
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Definitely before Christmas, and definitely with the conditional process models! We are nearing the end of the testing phase now... :-) EJ
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It's pretty close! I believe there are some minor deviations if I recall correctly (maybe headings in italics or something like that). EJ
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Hi Niar, You can get JASP help files by clicking the blue circle with an "i" on it -- in the GUI for SEM. However, for SEM it is relatively concise, and I've copied it here: Perform structural equation modeling (SEM) using lavaan (Rosseel,…
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Oh, and about the post-hoc tests: it works best if you push this feature request on our GitHub page (for details see https://jasp-stats.org/2018/03/29/request-feature-report-bug-jasp/) because this is what most of the team sees. EJ
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Yes this is the official channel :-) Sphericity is still on the to-do list. Equality of standard deviations is implicitly assumed by LML also. We have concrete ideas on how to implement this, but have not gotten round to it. See also https://www.fro…
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Hi Chantal, The difference is not pronounced, right? You would draw the same conclusion -- there is weak evidence that the groups are the same and hence the variable does not contribute. The post-hoc tests are just t-tests, so they will give results…
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We use Lavaan syntax. There is help available online (YouTube?), there is the help file (blue circle with the "i") and you can check out the examples in the JASP Data Library. Cheers, E.J.
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imo, it starts by a model for the data-generating process (Normal? Poisson?). Then the model parameter of interest is modeled as a linear combination of predictors, possibly on thea suitable scale. Any nonlinear effects on this scale can possibly be…
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I'll ask one of our experts! EJ
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Autocorrelation decreases standard error only when left unaccounted for. If you properly account for the autocorrelation, the standard error increases (because effectively you have fewer observations). They may pull in opposite directions but probab…
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The option "correlation matrix" is there explicitly in the Bayesian formulation (what were you doing with the frequentist tools anyway? ;-)). In the frequentist version, you can tick the various plot options to construct the desired matrix…
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BTW you can compute the semi-partial r from the standard r's, as outlined in here for instance: https://personal.utdallas.edu/~herve/Abdi-PartialRegressionCoefficient2007-pretty.pdf
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It would be a good feature request for our GitHub page! (for details see https://jasp-stats.org/2018/03/29/request-feature-report-bug-jasp/) EJ
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OK it does not work for me right now either E.J.
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Is this still the case??
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BTW, I can confirm the site is down. We're on it. EJ
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I can check with our provider. *Usually* these are temporary glitches. Also, note that this issue will receive the attention it deserves when it is posted on our GitHub page, so the entire team sees it (for details see https://jasp-stats.org/2018/03…
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Last post on this for a while, promise. check out https://robjhyndman.com/hyndsight/arima-trends/index.html. This seems a reasonable idea. EJ
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sorry for the deluge of shorts posts --- I keep discovering new issues. The Mann-Kendall test assumes that the observations are independent, which I don't think is the case (there is autocorrelation, even apart from any trend). So right now I am thi…
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This looks like the standard test: https://help.healthycities.org/hc/en-us/articles/233420187-Mann-Kendall-test-for-trend-overview It feels similar to doing a linear regression on the ranks, but perhaps it is different E.J.
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Oh that KPSS test is actually not what you need, because it tests whether the series is stationary around a trend
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Also, the JASP Time Series module offers a test of trend stationarity. From the help file: "Kwiatkowski-Phillips-Schmidt-Shin: Computes a KPSS test where the null assumes that the time-series is level or trend stationary. The p-values are inter…
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Well the team was recently expanded with Mahdi Kamalabad (https://www.uu.nl/staff/MShafieeKamalabad), and he was hoping to do just this. I will bring this issue to his attention EJ
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A more informative nonparametric test could be to transform the sizes into ranks. You then do the linear regression on the ranks (for instance). Transforming to ranks would probably be a good way to start. Note that I am making this up as I go along…
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I am not sure what nonparametric test would be best. But I think the simplest nonparametric test would be to code your data as "larger" or "smaller" than the previous, and then do a binomial test! EJ
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Hi Per, We are aware of this issue. What is the problem with GitHub? (this is really very much a GitHub issue) EJ
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Hi Mila, I think this topic is understudied. Pragmatically, I might suggest either doubling or halving the default setting... Cheers, E.J.
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Hi Paolo85, Feature requests can be issued through our GitHub page (for details see https://jasp-stats.org/2018/03/29/request-feature-report-bug-jasp/) where it would be best to be as specific as possible -- JASP calls 475 R packages :-) Cheers, E.J.
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Dear JamieB, Hmm I don't think this is possible now, but it would be an interesting feature request for our GitHub page (for details see https://jasp-stats.org/2018/03/29/request-feature-report-bug-jasp/) Cheers, E.J.