Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- AGE GENDER AVAR BVAR OUTVAR
- 0 60 F 0.9 0 8260.0
- 1 56 F 5.4 1 15888.0
- 2 55 F 1.2 1 19734.4
- 3 52 F 1.7 1 15904.2
- 4 49 F 1.6 0 14848.0
- from bambi import Model
- model = Model(bdf)
- results = model.fit('OUTVAR ~ AGE + GENDER + AVAR + BVAR', samples=5000, chains=2)
- print(results[1000:].summary())
- Auto-assigning NUTS sampler...
- Initializing NUTS using advi...
- Average Loss = 1,476.2: 21%|██████████████▊ | 10601/50000 [00:01<00:07, 5542.87it/s]
- Convergence achieved at 10700
- Interrupted at 10,699 [21%]: Average Loss = 1,520.6
- Multiprocess sampling (2 chains in 2 jobs)
- NUTS: [OUTVAR_sd, BVAR, AVAR, AGE, GENDER, Intercept]
- Sampling 2 chains: 100%|██████████████████████████████████████████████████████████████████████████| 11000/11000 [11:40<00:00, 15.71draws/s]
- There were 154 divergences after tuning. Increase `target_accept` or reparameterize.
- The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.
- There were 241 divergences after tuning. Increase `target_accept` or reparameterize.
- The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.
- The number of effective samples is smaller than 10% for some parameters.
- mean sd hpd0.95_lower hpd0.95_upper effective_n gelman_rubin
- AGE 6.936836 81.741918 -158.176468 165.050530 589 1.005245
- AVAR -78.356403 410.942267 -896.068374 718.650196 2414 1.000314
- BVAR 2639.993063 2262.101985 -1528.841953 7297.760056 553 1.000544
- GENDER[T.M] -615.092080 1659.905226 -3855.789966 2756.704710 1392 1.003126
- Intercept 11739.843222 3591.680335 5239.872556 19053.145349 179 1.007447
- OUTVAR_sd 8936.351700 283.640474 8402.347757 9318.495791 6027 1.000028
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement