Comments (4)
Sounds like this could be different random number generators leading to different results as the number of models sampled differs in the two runs despite the same seed. See https://stackoverflow.com/questions/48626086/same-seed-different-os-different-random-numbers-in-r#:~:text=Linux%20gives%20a%20different%20random,results%20will%20agree%20or%20not) and one solution that suggests
set.seed(10, kind = "Mersenne-Twister", normal.kind = "Inversion"); rnorm(1)
to see if you get the same random numbers. Note the top model is the same in both (Null) and has the same marginal likelihood, so it may be a matter of which models are being sampled.
from bas.
Interestingly I get 312 unique models when running on MAC OSX R 4.3.2.
Do you get different results if you enumerate all models (use method="deterministic") for sampling.?
from bas.
Using method="deterministic" produces exactly the same results, so it must be an issue related with pseudo-random sequence.
from bas.
Well, this is what I got typing '> set.seed(10, kind = "Mersenne-Twister", normal.kind = "Inversion"); rnorm(10)' on Linux machine (Debian, R-4.3.2 from distro repos) and Windows machine (10 Pro, R-4.3.3):
[Linux]
[1] 0.01874617 -0.18425254 -1.37133055 -0.59916772 0.29454513 0.38979430
[7] -1.20807618 -0.36367602 -1.62667268 -0.25647839
[Windows]
[1] 0.01874617 -0.18425254 -1.37133055 -0.59916772 0.29454513 0.38979430
[7] -1.20807618 -0.36367602 -1.62667268 -0.25647839
As you can see sequences are identical, so maybe an issue is BAS-related?
from bas.
Related Issues (20)
- Remove intercept in bas.lm? HOT 1
- Multithreading Problem HOT 1
- Same Intercepts HOT 1
- dropping the null model from Jeffrey's prior conflicts with include.always HOT 1
- Using pivot = FALSE in bas.lm does not create a warning on M1mac in non-full rank case HOT 1
- Add Firth logistic regression to BAS
- coef throws warnings with bas.glm objects if prior is AIC or BIC HOT 1
- `bayesglm.fit` does not check arguments `x` or `y` for correct type before calling C HOT 1
- `bas.predict` error with a single covariate HOT 2
- segmentation fault with method "MCMC+BAS" if initial (best model) arg is not the null model HOT 1
- `bas.predict` error when n<p when using `estimator= BMA` and `se.fit =T` HOT 2
- Release BAS 1.6.6 HOT 1
- valgrind identifies uninitialized variables in `glm_fit.c` HOT 1
- Release BAS 1.7.0 HOT 1
- `bas.lm` and `bas.glm` ignoring prior model probabilities that are 0 HOT 1
- uninitialized values in `hyp1f1.c` HOT 1
- Release BAS 1.7.1 HOT 1
- issue with hypergeometric1F1 HOT 1
- error in hypergeometric1F1 HOT 1
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from bas.