cheers

jonathon

Statistics: Posted by jonathon — Thu Jul 01, 2021 3:50 am

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"google::protobuf::internal::ThreadSafeArena::SpaceAllocated() const", referenced from:

google::protobuf::Arena::SpaceAllocated() const in jamovi.pb.cc.o

ld: symbol(s) not found for architecture x86_64

clang: error: linker command failed with exit code 1 (use -v to see invocation)

make: *** [jamovi-engine] Error 1

Statistics: Posted by sbtseiji — Thu Jun 24, 2021 11:51 pm

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I have been working on the Japanese localization of jamovi for a while, and now I can say that it is almost done. Currently, it works fine in the docker image. So, I want to test it on my Mac but I don't know how to compile a Mac binary of jamovi from git hub source. Could you give me some help?

Thank you,

Seiji

Statistics: Posted by sbtseiji — Thu Jun 24, 2021 8:10 am

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Thank you for your effort. Literature in statistics highlighted the importance and usefulness of bootstrapping in cases where the data is nonnormal or the sample is relatively small. We would like to see a bootstrap option in t-test, regression, correlation, and ANOVA similar to what we already have in Mediation and Moderation modules.

Your contribution is much appreciated!

Abdullah

Statistics: Posted by Abdullah — Thu Jun 17, 2021 5:54 am

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jonathon

Statistics: Posted by jonathon — Thu Jun 03, 2021 3:56 am

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Apparently, you don't accept that jamovi has an obligation to inform users when it calculates a measure in a way different to all other major stats packages. Other than jamovi, no other stats package uses n as the divisor for RMSE(?).

You ask: Is it (n - p - 1) a more useful measure?

1. It (n - p - 1) yields the unbiased estimator of the Var[e], (sigma^2), and so it has "nice", arguably superior statistical properties.

2. It is superior to the other measures of goodness of fit used in regression (such as, R^2 and the adjusted R^2) because "RMSE" is calibrated in the units of Y, the dependent variable. (By the way, the adjusted R^2 uses (n - p - 1) to estimate Var[e]. )

It seems I've failed to convince you and Ravi on the statistical issues.

--Davo

Statistics: Posted by DavoFromDapto — Thu Jun 03, 2021 2:34 am

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jonathon

Statistics: Posted by jonathon — Thu Jun 03, 2021 12:01 am

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you've made the case for doing it the "the wrong way", but there's also a case for doing it the "the right way".

one group of users may expect RMSE to match stata and sas, but another group of users will expect it to match wikipedia.

i'm not sure what the best way to handle this is ... but it's an issue with multiple facets. your suggestion to add a footnote explaining what other software does isn't unreasonable.

the best solution in most of these cases is have sas, stata, et al. add footnotes to their software ...

cheers

jonathon

Statistics: Posted by jonathon — Wed Jun 02, 2021 11:25 pm

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1. the formula for the RMSE -- jamovi uses n as the divisor, while every other major package uses (n - p - 1). Do you know of another package that uses n? Even though they use different labels for many features of their output, they all use the same formula.

2. jamovi and full disclosure. When jamovi departs from common statistical practice, it should disclose that to the user. Put differently, how is the user to know what jamovi has done? SAS and Stata label it the "RMSE" and so many users will conclude that when jamovi labels it "RMSE", it's computing the same thing as SAS and Stata. That confusion creates difficulties for replication.

I have a solution to the issue: Why not use n - p - 1 as the divisor, and then, if you object to calling that quantity the RMSE (like SAS and Stata label it), use the R label for it: "residual standard error."

My larger concern is that there may be other stats procedures where jamovi departs from standard statistical practice, and users are blissfully unaware of it. I think jamovi has an obligation to advise users about these departures.

Jamovi is a great idea; I like what you've done, and I know how much hard work it has taken. However, as a matter of strategy, consider making the jamovi default selections replicate the major / well established stats packages (IBM-SPSS, SAS, Stata ... ) in order to promote confidence in the numerical accuracy, and make it easier for users to transition to jamovi.

--Davo

Statistics: Posted by DavoFromDapto — Wed Jun 02, 2021 1:39 pm

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So what we do is literally take the Root of the Mean of the Squared Errors (aka RMSE), see https://github.com/jamovi/jmv/blob/04b9d4004bbbf87b727ab755c7c7adfaf0084c62/R/linreg.b.R#L598. I think the confusion is that the RMSE is used as an estimator of the residual standard error. And you are right that the RMSE is a biased estimator of the residual standard error, and that the way you calculate it is an unbiased estimator. However, I think it would be undesirable to use the name "RMSE" for the unbiased estimator of the residual standard error because it's not actually giving you the Root Mean Squared Error. This is also why lm in R calls it "residual standard error" and SPSS calls it "Std. Error of the Estimate" and not RMSE.

Cheers,

Ravi

Statistics: Posted by Ravi — Wed Jun 02, 2021 10:41 am

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jonathon

Statistics: Posted by jonathon — Wed Jun 02, 2021 9:28 am

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All the major stats software packages use (n - p - 1) to calculate RMSE in regression. This list includes: SPSS, Stata, SAS, lm() in R, Excel, and SHAZAM . Surely, this list makes the use of (n - p - 1) "the expected way"?

Which major stats software packages use n as the divisor for RMSE in regression?

If n is used in jamovi, perhaps there should be a note somewhere alerting users that it has a different approach in case of replication.

--Davo

Statistics: Posted by DavoFromDapto — Wed Jun 02, 2021 7:55 am

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lots of resources seem to use n as the denominator.

cheers

jonathon

Statistics: Posted by jonathon — Wed Jun 02, 2021 7:21 am

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