Mattan S. Ben-Shachar
@mattansb.msbstats.info
Statistics lecturer | Freelance statistical consultant & research analyst | #rstats dev @easystats.github.io
home.msbstats.info
(He/Him)
home.msbstats.info
(He/Him)
MORE DATA + MORA ALGORITHM = MORE MONEY
November 19, 2025 at 4:53 PM
MORE DATA + MORA ALGORITHM = MORE MONEY
What a little gemstone from {tidytext} to order axes *within* facets!
#rstats
juliasilge.github.io/tidytext/ref...
#rstats
juliasilge.github.io/tidytext/ref...
November 19, 2025 at 10:36 AM
What a little gemstone from {tidytext} to order axes *within* facets!
#rstats
juliasilge.github.io/tidytext/ref...
#rstats
juliasilge.github.io/tidytext/ref...
Seems not inconsequential IMO. It's weird though, because it's pulling very strongly in opposite directions - seems to result in a strange platykurtic shape? Compare to a flat prior (3rd image)
November 18, 2025 at 9:58 PM
Seems not inconsequential IMO. It's weird though, because it's pulling very strongly in opposite directions - seems to result in a strange platykurtic shape? Compare to a flat prior (3rd image)
The default prior for the intercept in both {rstanarm} and {brms} are very wide.
Counterintuitively - being on the logit scale, this is actually translates to a **strong** prior that p(y=1) is near 1 or near 0.
Always check your priors!
#rstats
Counterintuitively - being on the logit scale, this is actually translates to a **strong** prior that p(y=1) is near 1 or near 0.
Always check your priors!
#rstats
November 18, 2025 at 2:01 PM
The default prior for the intercept in both {rstanarm} and {brms} are very wide.
Counterintuitively - being on the logit scale, this is actually translates to a **strong** prior that p(y=1) is near 1 or near 0.
Always check your priors!
#rstats
Counterintuitively - being on the logit scale, this is actually translates to a **strong** prior that p(y=1) is near 1 or near 0.
Always check your priors!
#rstats
oh yes, very nice!
November 16, 2025 at 1:36 PM
oh yes, very nice!
No, sorry I totally pulled those numbers out of thin air.
It's these:
It's these:
November 8, 2025 at 11:45 AM
No, sorry I totally pulled those numbers out of thin air.
It's these:
It's these:
How to fit a model without estimating any parameters?
Use a formula with
y ~ 0 + offset(...)
This works with any formula interface, and with #tidymodels this can be passed to add_model().
#rstats
Use a formula with
y ~ 0 + offset(...)
This works with any formula interface, and with #tidymodels this can be passed to add_model().
#rstats
November 5, 2025 at 10:56 AM
How to fit a model without estimating any parameters?
Use a formula with
y ~ 0 + offset(...)
This works with any formula interface, and with #tidymodels this can be passed to add_model().
#rstats
Use a formula with
y ~ 0 + offset(...)
This works with any formula interface, and with #tidymodels this can be passed to add_model().
#rstats
October 16, 2025 at 3:16 PM
Garden variety topographical maps of spectral perturbations!
(I think I stole some of this ggplot code from @mattcraddock.com?)
(I think I stole some of this ggplot code from @mattcraddock.com?)
October 5, 2025 at 8:53 PM
Garden variety topographical maps of spectral perturbations!
(I think I stole some of this ggplot code from @mattcraddock.com?)
(I think I stole some of this ggplot code from @mattcraddock.com?)
A radial histogram???
October 5, 2025 at 8:53 PM
A radial histogram???
Illustration of phase coherence!
October 5, 2025 at 8:53 PM
Illustration of phase coherence!
Hear me out, what if we...
October 2, 2025 at 6:44 PM
Hear me out, what if we...
One paragraph in and you can tell 🤦♂️
September 29, 2025 at 2:14 PM
One paragraph in and you can tell 🤦♂️
September 27, 2025 at 3:58 PM
Ha no surprises here!
September 20, 2025 at 3:43 AM
Ha no surprises here!
September 17, 2025 at 2:40 PM
September 17, 2025 at 2:33 PM