Closed
Description
Describe the bug
When I use Pathfinder to get inits I get "mismatch in number dimensions declared" as error for some models.
To Reproduce
Reprex:
library(tidyverse)
library(janitor)
#>
#> Attaching package: 'janitor'
#> The following objects are masked from 'package:stats':
#>
#> chisq.test, fisher.testlibrary(nlme)
#>
#> Attaching package: 'nlme'
#> The following object is masked from 'package:dplyr':
#>
#> collapselibrary(cmdstanr)
#> This is cmdstanr version 0.8.1
#> - CmdStanR documentation and vignettes: mc-stan.org/cmdstanr
#> - CmdStan path: C:/Users/STBE21/.cmdstan/cmdstan-2.35.0
#> - CmdStan version: 2.35.0library(brms)
#> Loading required package: Rcpp
#> Loading 'brms' package (version 2.21.3). Useful instructions
#> can be found by typing help('brms'). A more detailed introduction
#> to the package is available through vignette('brms_overview').
#>
#> Attaching package: 'brms'
#> The following object is masked from 'package:stats':
#>
#> arBodyWeight %>%
as_tibble() %>%
clean_names() %>%
mutate(time2=time %>% factor %>% as.numeric()) ->
body_weight
brm(data = body_weight, formula = weight ~ diet + ar(p = 1, time = time2, gr=rat)+(1|rat), backend="cmdstanr", cores =6, chains=6, empty = T) ->
brm_empty
cmdstanr::write_stan_file(brm_empty$model) %>%
cmdstan_model(cpp_options = list(stan_threads = TRUE)) ->
model_cmdstan
model_cmdstan$pathfinder(data = make_standata(data = body_weight,
formula = weight ~ diet + ar(p = 1, time = time2, gr=rat)+(1|rat)),
init = 2, history_size = 100,
num_threads = 6,
num_paths = 6) ->
pathfinder_inits
#> Path [1] :Initial log joint density = -2354013.421594
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite function evaluation.
#> Path [4] :Initial log joint density = -2837875.019409
#> Path [2] :Initial log joint density = -13657453.097900
#> Path [3] :Initial log joint density = -5782846.815396
#> Path [6] :Initial log joint density = -21086717.897784
#> Path [5] :Initial log joint density = -117122627.277960
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite function evaluation.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite function evaluation.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite function evaluation.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite function evaluation.
#> Error evaluating model log probability: Non-finite gradient.
#> Error evaluating model log probability: Non-finite gradient.
#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 88 -5.855e+02 4.938e-04 9.595e-02 1.000e+00 1.000e+00 2201 -3.697e+03 -3.697e+03
#> Path [4] :Best Iter: [40] ELBO (-662.093438) evaluations: (2201)
#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 99 -5.855e+02 2.429e-02 1.419e+01 1.000e+00 1.000e+00 2476 -7.844e+02 -7.844e+02
#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 115 -5.854e+02 3.519e-04 7.368e-02 7.838e-01 7.838e-01 2876 -1.467e+03 -1.467e+03
#> Path [5] :Best Iter: [60] ELBO (-662.139363) evaluations: (2876)
#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 99 -1.484e+03 4.062e-02 1.668e+02 3.698e-01 3.698e-02 2476 -1.613e+03 -1.613e+03
#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 99 -5.854e+02 4.431e-04 1.919e-01 4.818e-01 4.818e-01 2476 -9.339e+02 -9.339e+02
#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 -5.854e+02 5.683e-04 8.569e-02 1.000e+00 1.000e+00 2501 -8.646e+02 -8.646e+02
#> Path [1] :Best Iter: [49] ELBO (-661.727387) evaluations: (2501)
#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 99 -5.864e+02 4.325e-04 6.753e+00 1.000e+00 1.000e+00 2476 -6.850e+02 -6.850e+02
#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 99 -5.855e+02 5.448e-03 2.277e+01 1.000e+00 1.000e+00 2476 -6.847e+02 -6.847e+02
#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 123 -5.854e+02 4.205e-04 1.956e-01 4.433e-01 4.433e-01 3076 -1.200e+03 -1.200e+03
#> Path [2] :Best Iter: [101] ELBO (-681.050138) evaluations: (3076)
#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 153 -5.853e+02 8.429e-04 2.050e-01 1.000e+00 1.000e+00 3826 -4.474e+03 -4.474e+03
#> Path [6] :Best Iter: [127] ELBO (-679.735085) evaluations: (3826)
#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 199 -1.463e+03 4.024e-02 2.453e+03 1.880e-01 1.880e-01 4976 -1.618e+03 -1.618e+03
#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 299 -1.415e+03 2.415e-01 1.322e+05 1.698e-01 3.418e-01 7476 -1.606e+03 -1.606e+03
#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 399 -1.314e+03 3.157e-01 1.600e+06 6.980e-01 6.980e-01 9976 -1.785e+03 -1.785e+03
#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 492 -6.218e+02 1.854e-05 3.144e+04 8.008e-01 8.008e-01 12301 -3.149e+04 -3.149e+04
#> Path [3] :Best Iter: [383] ELBO (-1566.436377) evaluations: (12301)
#> Total log probability function evaluations:32631
#> Pareto k value (1.4) is greater than 0.7. Importance resampling was not able to improve the approximation, which may indicate that the approximation itself is poor.
#> Finished in 0.3 seconds.
model_cmdstan$sample(data = make_standata(data = body_weight,
formula = weight ~ diet + ar(p = 1, time = time2, gr=rat)+(1|rat)),
init = pathfinder_inits,
threads_per_chain = 1,
parallel_chains = 6,
chains = 6) ->
samples
#> Running MCMC with 6 parallel chains, with 1 thread(s) per chain...
#> Chain 1 Unrecoverable error evaluating the log probability at the initial value.
#> Chain 1 Exception: mismatch in number dimensions declared and found in context; processing stage=parameter initialization; variable name=z_1; dims declared=(1,16); dims found=(16) (in 'C:/Users/STBE21/AppData/Local/Temp/Rtmp08oU96/model-b0542cd44c4b.stan', line 38, column 2 to column 29)
#> Chain 2 Unrecoverable error evaluating the log probability at the initial value.
#> Chain 2 Exception: mismatch in number dimensions declared and found in context; processing stage=parameter initialization; variable name=z_1; dims declared=(1,16); dims found=(16) (in 'C:/Users/STBE21/AppData/Local/Temp/Rtmp08oU96/model-b0542cd44c4b.stan', line 38, column 2 to column 29)
#> Chain 3 Unrecoverable error evaluating the log probability at the initial value.
#> Chain 3 Exception: mismatch in number dimensions declared and found in context; processing stage=parameter initialization; variable name=z_1; dims declared=(1,16); dims found=(16) (in 'C:/Users/STBE21/AppData/Local/Temp/Rtmp08oU96/model-b0542cd44c4b.stan', line 38, column 2 to column 29)
#> Chain 4 Unrecoverable error evaluating the log probability at the initial value.
#> Chain 4 Exception: mismatch in number dimensions declared and found in context; processing stage=parameter initialization; variable name=z_1; dims declared=(1,16); dims found=(16) (in 'C:/Users/STBE21/AppData/Local/Temp/Rtmp08oU96/model-b0542cd44c4b.stan', line 38, column 2 to column 29)
#> Chain 5 Unrecoverable error evaluating the log probability at the initial value.
#> Chain 5 Exception: mismatch in number dimensions declared and found in context; processing stage=parameter initialization; variable name=z_1; dims declared=(1,16); dims found=(16) (in 'C:/Users/STBE21/AppData/Local/Temp/Rtmp08oU96/model-b0542cd44c4b.stan', line 38, column 2 to column 29)
#> Chain 6 Unrecoverable error evaluating the log probability at the initial value.
#> Chain 6 Exception: mismatch in number dimensions declared and found in context; processing stage=parameter initialization; variable name=z_1; dims declared=(1,16); dims found=(16) (in 'C:/Users/STBE21/AppData/Local/Temp/Rtmp08oU96/model-b0542cd44c4b.stan', line 38, column 2 to column 29)
#> Warning: Chain 1 finished unexpectedly!
#> Warning: Chain 2 finished unexpectedly!
#> Warning: Chain 3 finished unexpectedly!
#> Warning: Chain 4 finished unexpectedly!
#> Warning: Chain 5 finished unexpectedly!
#> Warning: Chain 6 finished unexpectedly!
#> Warning: All chains finished unexpectedly! Use the $output(chain_id) method for more information.
#> Warning: Use read_cmdstan_csv() to read the results of the failed chains.
#> Warning: No chains finished successfully. Unable to retrieve the fit.Created on 2024-06-07 with reprex v2.1.0
Operating system
Windows 10.
CmdStanR version number
packageVersion("cmdstanr")
[1] ‘0.8.1’