Daniel Oberski
@daob.nl
Data science prof @ Utrecht. Incoming scientific director, Dutch national infrastructure for social sciences ODISSEI
Latent variables, Structural Equation Models, Measurement error
https://daob.nl | https://hds.sites.uu.nl | https://odissei-data.nl
Latent variables, Structural Equation Models, Measurement error
https://daob.nl | https://hds.sites.uu.nl | https://odissei-data.nl
Not in regular lists, which would be a closer equivalent to the given R code IMO.
May 18, 2025 at 8:18 PM
Not in regular lists, which would be a closer equivalent to the given R code IMO.
Interesting. How it thinks R should work *is* how Python works, and a lot of the coding benchmarks are very focused on python. Maybe that is the explanation?
May 18, 2025 at 8:07 PM
Interesting. How it thinks R should work *is* how Python works, and a lot of the coding benchmarks are very focused on python. Maybe that is the explanation?
Sending an email with this content to semnet (or wanting to) is all part of the circle of life Eiko. Generations of researchers have found out: semnet simply cannot change its nature, like in that scorpion story.
April 30, 2025 at 8:43 AM
Sending an email with this content to semnet (or wanting to) is all part of the circle of life Eiko. Generations of researchers have found out: semnet simply cannot change its nature, like in that scorpion story.
I feel an instrumental variable hype coming on!
February 13, 2025 at 7:28 AM
I feel an instrumental variable hype coming on!
Good to see you're still keeping up with socials during this time of reflection :p
January 14, 2025 at 8:50 AM
Good to see you're still keeping up with socials during this time of reflection :p
Anderson collected a lot more data than Fisher actually used for his example application in the LDA paper. What we did was feed some of that other data to many models at OpenML as well as the original LDA. I don't remember looking at overconfidence, but I'm sure you're right.
November 2, 2024 at 10:20 PM
Anderson collected a lot more data than Fisher actually used for his example application in the LDA paper. What we did was feed some of that other data to many models at OpenML as well as the original LDA. I don't remember looking at overconfidence, but I'm sure you're right.
I know you're joking but we actually did this. Linear discriminant analysis was still the best :D
November 2, 2024 at 10:14 PM
I know you're joking but we actually did this. Linear discriminant analysis was still the best :D
There is no 1-1 conversion between probit and logit coefficients. Probit coefs are easy to interpret if you standardize the LRV ("STDY"): the coefficient is the number of sd's increase for a one unit increase of the predictor. Easy to convert to probabilities by pulling the prediction through pnorm.
August 29, 2024 at 8:46 PM
There is no 1-1 conversion between probit and logit coefficients. Probit coefs are easy to interpret if you standardize the LRV ("STDY"): the coefficient is the number of sd's increase for a one unit increase of the predictor. Easy to convert to probabilities by pulling the prediction through pnorm.
I.e. the sense is likely "more interpretable" rather than "obvious".
July 11, 2024 at 2:58 PM
I.e. the sense is likely "more interpretable" rather than "obvious".
I think they don't mean "intuitive" in the sense of "obvious" but in the sense that it gives some extra information: From V and the pattern of zeroes you can tell which linear dependencies are taken care of. E.g. if there's both age and year of birth in X, those will show up in one column of V.
July 11, 2024 at 2:56 PM
I think they don't mean "intuitive" in the sense of "obvious" but in the sense that it gives some extra information: From V and the pattern of zeroes you can tell which linear dependencies are taken care of. E.g. if there's both age and year of birth in X, those will show up in one column of V.
Thanks, this is *exactly* what I have been looking for!
July 10, 2024 at 12:08 PM
Thanks, this is *exactly* what I have been looking for!
LISS has a basket option which automatically merges the time points/datasets you need. Is this what you mean?
(also the "time-invariant" background variables tend to be simple copies, except when they are actually repeated; so a single copy is often enough, depending on the time period)
(also the "time-invariant" background variables tend to be simple copies, except when they are actually repeated; so a single copy is often enough, depending on the time period)
July 2, 2024 at 2:19 PM
LISS has a basket option which automatically merges the time points/datasets you need. Is this what you mean?
(also the "time-invariant" background variables tend to be simple copies, except when they are actually repeated; so a single copy is often enough, depending on the time period)
(also the "time-invariant" background variables tend to be simple copies, except when they are actually repeated; so a single copy is often enough, depending on the time period)
I'm sure he would be delighted that he was able to bring down the number of his opponents to 1 in 5 in only 363 years!
(sorry for the wonky colors btw)
(sorry for the wonky colors btw)
June 25, 2024 at 9:53 AM
I'm sure he would be delighted that he was able to bring down the number of his opponents to 1 in 5 in only 363 years!
(sorry for the wonky colors btw)
(sorry for the wonky colors btw)
Quite. Hopefully it's all just measurement error from being confused by the question??
June 25, 2024 at 9:37 AM
Quite. Hopefully it's all just measurement error from being confused by the question??