Comments (10)
rmcelreath
commented on August 16, 2024
Yes until I update the model code, you need to edit it so it uses the new array syntax. More details: https://mc-stan.org/cmdstanr/articles/deprecations.html
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emstruong
commented on August 16, 2024
Do you take pull requests? I'd like to try my hand at fixing it... Would you like the fix to check what version of Stan people are using?
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rmcelreath
commented on August 16, 2024
I do sometimes yes. I actually have a draft fix in my local branch. I will see if I can find time to review it and push up later today.
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rmcelreath
commented on August 16, 2024
Okay I just committed and push it up to Experimental. It runs for me, but still with one warning that I'd like to fix. 0c15281
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emstruong
commented on August 16, 2024
Okay I just committed and push it up to Experimental. It runs for me, but still with one warning that I'd like to fix. 0c15281
Thanks!
For anyone else that's interested, this particular commit can be obtained using
remotes::install_github("rmcelreath/rethinking@0c15281e9452e335fc73eba368f39cafde3ef131")Also, I'm not sure if this was intentional, but when I re-ran the code, it seemed to require rstan this time around even though ulam_options$use_cmdstan <- TRUE.
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rmcelreath
commented on August 16, 2024
Re rstan, I couldn't repeat that issue. I don't have rstan installed, and it seemed to work. Any more details that might help me figure out the issue?
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emstruong
commented on August 16, 2024
Re rstan, I couldn't repeat that issue. I don't have rstan installed, and it seemed to work. Any more details that might help me figure out the issue?
Looking at it more carefully, I guess it's not a serious issue -- see the very bottom of this reprex regarding the error.
library(reprex)
library(rethinking)
#> Loading required package: cmdstanr
#> This is cmdstanr version 0.6.1
#> - CmdStanR documentation and vignettes: mc-stan.org/cmdstanr
#> - CmdStan path: /home/mtruong/.cmdstan/cmdstan-2.33.1
#> - CmdStan version: 2.33.1
#> Loading required package: posterior
#> This is posterior version 1.5.0
#>
#> Attaching package: 'posterior'
#> The following objects are masked from 'package:stats':
#>
#> mad, sd, var
#> The following objects are masked from 'package:base':
#>
#> %in%, match
#> Loading required package: parallel
#> rethinking (Version 2.41)
#>
#> Attaching package: 'rethinking'
#> The following object is masked from 'package:stats':
#>
#> rstudent
# library(rstan)
data("Lynx_Hare")
data("Lynx_Hare_model")
dat_list <- list(N=nrow(Lynx_Hare),
pelts= Lynx_Hare[,2:3])
# ulam_options$use_cmdstan <- TRUE
paste("My ulam_options is", ulam_options$use_cmdstan)
#> [1] "My ulam_options is TRUE"
m16.5 <- rethinking::stan(
model_code = Lynx_Hare_model,
data = dat_list,
chains = 3,
cores = 3,
control = list(adapt_delta = 0.95)
)
#> Warning in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 39, column 22: integrate_ode_rk45
#> is deprecated and will be removed in Stan 3.0. Use ode_rk45 instead.
#> The new interface is slightly different, see:
#> https://mc-stan.org/users/documentation/case-studies/convert_odes.html
#> Running MCMC with 3 parallel chains...
#>
#> Chain 1 Iteration: 1 / 1000 [ 0%] (Warmup)
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: lognormal_lpdf: Scale parameter is inf, but must be positive finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: lognormal_lpdf: Scale parameter is inf, but must be positive finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: lognormal_lpdf: Location parameter is -nan, but must be finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: integrate_ode_rk45: Failed to integrate to next output time (2) in less than max_num_steps steps (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 39, column 2 to line 42, column 65)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: lognormal_lpdf: Location parameter is -nan, but must be finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: lognormal_lpdf: Location parameter is -nan, but must be finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: integrate_ode_rk45: initial state[1] is inf, but must be finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 39, column 2 to line 42, column 65)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: lognormal_lpdf: Scale parameter is inf, but must be positive finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 2 Iteration: 1 / 1000 [ 0%] (Warmup)
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: lognormal_lpdf: Scale parameter is inf, but must be positive finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: lognormal_lpdf: Scale parameter is inf, but must be positive finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: integrate_ode_rk45: Failed to integrate to next output time (2) in less than max_num_steps steps (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 39, column 2 to line 42, column 65)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: integrate_ode_rk45: Failed to integrate to next output time (2) in less than max_num_steps steps (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 39, column 2 to line 42, column 65)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: integrate_ode_rk45: Failed to integrate to next output time (5) in less than max_num_steps steps (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 39, column 2 to line 42, column 65)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: integrate_ode_rk45: initial state[1] is inf, but must be finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 39, column 2 to line 42, column 65)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: integrate_ode_rk45: Failed to integrate to next output time (2) in less than max_num_steps steps (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 39, column 2 to line 42, column 65)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2
#> Chain 3 Rejecting initial value:
#> Chain 3 Error evaluating the log probability at the initial value.
#> Chain 3 Exception: lognormal_lpdf: Location parameter is -nan, but must be finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 3 Exception: lognormal_lpdf: Location parameter is -nan, but must be finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 3 Iteration: 1 / 1000 [ 0%] (Warmup)
#> Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 3 Exception: lognormal_lpdf: Location parameter is -nan, but must be finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 3
#> Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 3 Exception: lognormal_lpdf: Location parameter is -nan, but must be finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 3
#> Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 3 Exception: integrate_ode_rk45: Failed to integrate to next output time (2) in less than max_num_steps steps (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 39, column 2 to line 42, column 65)
#> Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 3
#> Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 3 Exception: integrate_ode_rk45: Failed to integrate to next output time (2) in less than max_num_steps steps (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 39, column 2 to line 42, column 65)
#> Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 3
#> Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 3 Exception: lognormal_lpdf: Location parameter is -nan, but must be finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 55, column 6 to column 63)
#> Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 3
#> Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 3 Exception: integrate_ode_rk45: initial state[2] is inf, but must be finite! (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 39, column 2 to line 42, column 65)
#> Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 3
#> Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 3 Exception: integrate_ode_rk45: Failed to integrate to next output time (2) in less than max_num_steps steps (in '/tmp/RtmpxyKElk/model-6a844fa15eb9.stan', line 39, column 2 to line 42, column 65)
#> Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 3
#> Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup)
#> Chain 2 Iteration: 100 / 1000 [ 10%] (Warmup)
#> Chain 3 Iteration: 100 / 1000 [ 10%] (Warmup)
#> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup)
#> Chain 3 Iteration: 200 / 1000 [ 20%] (Warmup)
#> Chain 2 Iteration: 200 / 1000 [ 20%] (Warmup)
#> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup)
#> Chain 3 Iteration: 300 / 1000 [ 30%] (Warmup)
#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup)
#> Chain 2 Iteration: 300 / 1000 [ 30%] (Warmup)
#> Chain 3 Iteration: 400 / 1000 [ 40%] (Warmup)
#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup)
#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling)
#> Chain 2 Iteration: 400 / 1000 [ 40%] (Warmup)
#> Chain 3 Iteration: 500 / 1000 [ 50%] (Warmup)
#> Chain 3 Iteration: 501 / 1000 [ 50%] (Sampling)
#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling)
#> Chain 3 Iteration: 600 / 1000 [ 60%] (Sampling)
#> Chain 2 Iteration: 500 / 1000 [ 50%] (Warmup)
#> Chain 2 Iteration: 501 / 1000 [ 50%] (Sampling)
#> Chain 3 Iteration: 700 / 1000 [ 70%] (Sampling)
#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling)
#> Chain 2 Iteration: 600 / 1000 [ 60%] (Sampling)
#> Chain 3 Iteration: 800 / 1000 [ 80%] (Sampling)
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#> Chain 3 Iteration: 900 / 1000 [ 90%] (Sampling)
#> Chain 2 Iteration: 700 / 1000 [ 70%] (Sampling)
#> Chain 3 Iteration: 1000 / 1000 [100%] (Sampling)
#> Chain 3 finished in 4.6 seconds.
#> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling)
#> Chain 2 Iteration: 800 / 1000 [ 80%] (Sampling)
#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling)
#> Chain 1 finished in 5.5 seconds.
#> Chain 2 Iteration: 900 / 1000 [ 90%] (Sampling)
#> Chain 2 Iteration: 1000 / 1000 [100%] (Sampling)
#> Chain 2 finished in 6.3 seconds.
#>
#> All 3 chains finished successfully.
#> Mean chain execution time: 5.5 seconds.
#> Total execution time: 6.5 seconds.
#> Error in loadNamespace(x): there is no package called 'rstan'Created on 2023-11-27 with reprex v2.0.2
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emstruong
commented on August 16, 2024
Also, here's an updated string for the Boxes_model
Boxed_model <-"data{
int N;
array[N] int y;
array[N] int majority_first;
}
parameters{
simplex[5] p;
}
model{
vector[5] phi;
// prior
p ~ dirichlet( rep_vector(4,5) );
// probability of data
for ( i in 1:N ) {
if ( y[i]==2 ) phi[1]=1; else phi[1]=0; // majority
if ( y[i]==3 ) phi[2]=1; else phi[2]=0; // minority
if ( y[i]==1 ) phi[3]=1; else phi[3]=0; // maverick
phi[4]=1.0/3.0; // random
if ( majority_first[i]==1 ) // follow first
if ( y[i]==2 ) phi[5]=1; else phi[5]=0;
else
if ( y[i]==3 ) phi[5]=1; else phi[5]=0;
// compute log( p_s * Pr(y_i|s )
for ( j in 1:5 ) phi[j] = log(p[j]) + log(phi[j]);
// compute average log-probability of y_i
target += log_sum_exp( phi );
}
}"I'm curious -- should we prefer RStan or CmdStanR? The Stan website and the rethinking package seem to imply that we should be using CmdStanR, yet when I make a new Stan file through R-Studio, it seems to require the RStan package...
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rmcelreath
commented on August 16, 2024
Thanks for the model code. I guess that one is deprecated too now.
CmdStanR is the better choice. RStan is many patches behind in the binaries, because the CRAN policies make it hard to update. CmdStanR in contrast doesn't even need to updated when the core cmdstan library is updated. That's why I rewrote rethinking so that RStan is not required.
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emstruong
commented on August 16, 2024
Thanks for the model code. I guess that one is deprecated too now.
CmdStanR is the better choice. RStan is many patches behind in the binaries, because the CRAN policies make it hard to update. CmdStanR in contrast doesn't even need to updated when the core cmdstan library is updated. That's why I rewrote rethinking so that RStan is not required.
I see... Well I think that about solves my issue, so I'll go ahead and close it. Thank you for the help
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