jamovi

Jonathon,
You pose the dilemma: "one group of users may expect RMSE to match stata and sas, but another group of users will expect it to match wikipedia." To resolve this dilemma I'd benchmark against Stata and Sas [and SPSS, R, Excel, ...] and use (n - p - 1), rather than against wikipedia ...

Ravi and Jonathon,
Perhaps it may be helpful to disentangle two related issues.

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 ...

Jonathon,
Thanks for your quick response.

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 ...

The web-version has an error in the calculation of the root mean squared error (RMSE) as a measure of fit in the linear regression procedure. Presumably, this same error is also in the current Download version?

The usual formula is RMSE = sqrt( Sigma e_i ^2 / (n - p - 1) )

I can replicate the ...

I'm new to Jamovi, and so I'll respond only to the statistics/data cleaning part of your questions.

No you don't need to do a log transformation before a PCA.

There are several rationales for transforming variables. One is highly skewed data. The normal distribution is symmetric. One good and ...