Bayesian model rstan using comiler

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

=============================================================
/*    Author :    Daniel tulps liu  */
=============================================================


[ 1 ]   require package

1. library(rstan)
   
2. library(ggplot2)
   
3. 
   
4. source("10.5_CasualEffectsUsingIV.R") # where data was cleaned
   
5. 
   
6. ## Rename variables of interest
   
7. 
   
8. pretest <- prelet
   
9. 
   
10. ## 2 stage least squares (sesame_one_pred_a.stan)
    
11. ## lm (watched ~ encouraged)
    
12. 
    
13. dataList.1 <- list(N=length(watched), watched=watched,encouraged=encouraged)
    
14. sesame_one_pred_2a.sf1 <- stan(file='sesame_one_pred_a.stan', data=dataList.1,
    
15.                                iter=1000, chains=4)
    
16. print(sesame_one_pred_2a.sf1)
    
17. beta.post <- extract(sesame_one_pred_2a.sf1, "beta")$beta
    
18. beta.mean2a <- colMeans(beta.post)
    
19. 
    
20. watched.hat <- beta.mean2a[1] + beta.mean2a[2] * encouraged
    
21. 
    
22. ## (sesame_one_pred_2b.stan)
    
23. ## lm (y ~ watched.hat)
    
24. 
    
25. dataList.2 <- list(N=length(y), watched=y,encouraged=watched.hat)
    
26. sesame_one_pred_2b.sf1 <- stan(file='sesame_one_pred_a.stan', data=dataList.2,
    
27.                                iter=1000, chains=4)
    
28. print(sesame_one_pred_2b.sf1)
    
29. 
    
30. ## Adjusting for covariates in a IV framework (sesame_multi_preds_3a.stan)
    
31. ## lm (watched ~ encouraged + pretest + as.factor(site) + setting)
    
32. 
    
33. dataList.3 <- list(N=length(watched), watched=watched,encouraged=encouraged,pretest=pretest, site=site,setting=setting)
    
34. sesame_multi_pred_3a.sf1 <- stan(file='sesame_multi_preds_3a.stan',
    
35.                                  data=dataList.3,
    
36.                                  iter=1000, chains=4)
    
37. print(sesame_multi_pred_3a.sf1)
    
38. 
    
39. beta.post <- extract(sesame_multi_pred_3a.sf1, "beta")$beta
    
40. beta.mean3a <- colMeans(beta.post)
    
41. 
    
42. watched.hat <- beta.mean3a[1] + beta.mean3a[2] * encouraged + beta.mean3a[3] * pretest + beta.mean3a[4] * (site==2) + beta.mean3a[5] * (site==3) + beta.mean3a[6] * (site==4) + beta.mean3a[7] * (site==5) + beta.mean3a[8] * setting
    
43. 
    
44. ## (sesame_multi_preds_3b.stan)
    
45. ## lm (y ~ watched.hat + pretest + as.factor(site) + setting)
    
46. dataList.4 <- list(N=length(watched.hat), watched=y,encouraged=watched.hat,pretest=pretest, site=site,setting=setting)
    
47. sesame_multi_pred_3b.sf1 <- stan(file='sesame_multi_preds_3b.stan',
    
48.                                  data=dataList.4,
    
49.                                  iter=1000, chains=4)
    
50. print(sesame_multi_pred_3b.sf1)
    
51. 
    
52. ## Se for IV estimates (FIXME)
    
53. 
    
54. ## Performing 2sls automatically
    
55. 
    
56. # regression without pre-treatment variables
    
57. 
    
58. # regression controlling for pre-treatment variables

复制代码

> library(rstan)
> library(ggplot2)
> library(foreign)
>
> sesame <- read.dta("sesame.dta")
> attach(sesame)
>
> ## Rename variables of interest
> watched <- regular
> encouraged <- encour
> y <- postlet
>
> ## Instrumental variables estimate (sesame_one_pred_a.stan)
> ## lm (watched ~ encouraged)
>
> dataList.1 <- list(N=length(watched), watched=watched,encouraged=encouraged)
> sesame_one_pred_a.sf1 <- stan(file='sesame_one_pred_a.stan', data=dataList.1,
+                               iter=1000, chains=4)

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.001 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 10 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 1: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 1: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 1: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 1: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 1: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 1: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 1: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 1: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 1: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 1: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 1:
Chain 1:  Elapsed Time: 0.065 seconds (Warm-up)
Chain 1:                0.051 seconds (Sampling)
Chain 1:                0.116 seconds (Total)
Chain 1:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 2: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 2: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 2: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 2: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 2: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 2: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 2: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 2: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 2: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 2: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 2: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 2:
Chain 2:  Elapsed Time: 0.049 seconds (Warm-up)
Chain 2:                0.05 seconds (Sampling)
Chain 2:                0.099 seconds (Total)
Chain 2:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 3: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 3: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 3: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 3: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 3: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 3: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 3: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 3: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 3: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 3: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 3: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 3:
Chain 3:  Elapsed Time: 0.044 seconds (Warm-up)
Chain 3:                0.043 seconds (Sampling)
Chain 3:                0.087 seconds (Total)
Chain 3:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 4: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 4: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 4: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 4: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 4: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 4: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 4: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 4: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 4: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 4: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 4: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 4:
Chain 4:  Elapsed Time: 0.048 seconds (Warm-up)
Chain 4:                0.042 seconds (Sampling)
Chain 4:                0.09 seconds (Total)
Chain 4:
Warning message:
In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
  'C:/rtools40/usr/mingw_/bin/g++' not found
> print(sesame_one_pred_a.sf1)
Inference for Stan model: sesame_one_pred_a.
4 chains, each with iter=1000; warmup=500; thin=1;
post-warmup draws per chain=500, total post-warmup draws=2000.

          mean se_mean   sd   2.5%    25%    50%    75%  97.5% n_eff Rhat
beta[1]   0.55    0.00 0.04   0.47   0.52   0.55   0.57   0.62   782    1
beta[2]   0.36    0.00 0.05   0.27   0.33   0.36   0.39   0.45   819    1
sigma     0.38    0.00 0.02   0.35   0.37   0.38   0.40   0.42  1300    1
lp__    110.19    0.04 1.20 106.95 109.65 110.48 111.08 111.59   909    1

Samples were drawn using NUTS(diag_e) at Sun Dec 06 09:18:11 2020.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
>
> beta.post <- extract(sesame_one_pred_a.sf1, "beta")$beta
> beta.mean1 <- colMeans(beta.post)
>
> ## (sesame_one_pred_b.stan)
> ## lm (y ~ encouraged)
>
> dataList.2 <- list(N=length(y), watched=y,encouraged=encouraged)

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：Bayesian Bayes model Using STAN

> sesame_one_pred_b.sf1 <- stan(file='sesame_one_pred_a.stan', data=dataList.2,
+                               iter=1000, chains=4)

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 1: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 1: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 1: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 1: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 1: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 1: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 1: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 1: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 1: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 1: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 1:
Chain 1:  Elapsed Time: 0.081 seconds (Warm-up)
Chain 1:                0.047 seconds (Sampling)
Chain 1:                0.128 seconds (Total)
Chain 1:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 2: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 2: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 2: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 2: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 2: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 2: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 2: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 2: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 2: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 2: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 2: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 2:
Chain 2:  Elapsed Time: 0.071 seconds (Warm-up)
Chain 2:                0.048 seconds (Sampling)
Chain 2:                0.119 seconds (Total)
Chain 2:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 3: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 3: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 3: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 3: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 3: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 3: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 3: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 3: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 3: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 3: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 3: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 3:
Chain 3:  Elapsed Time: 0.073 seconds (Warm-up)
Chain 3:                0.037 seconds (Sampling)
Chain 3:                0.11 seconds (Total)
Chain 3:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 4: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 4: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 4: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 4: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 4: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 4: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 4: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 4: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 4: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 4: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 4: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 4:
Chain 4:  Elapsed Time: 0.07 seconds (Warm-up)
Chain 4:                0.034 seconds (Sampling)
Chain 4:                0.104 seconds (Total)
Chain 4:
Warning message:
In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
  'C:/rtools40/usr/mingw_/bin/g++' not found
> print(sesame_one_pred_b.sf1)
Inference for Stan model: sesame_one_pred_a.
4 chains, each with iter=1000; warmup=500; thin=1;
post-warmup draws per chain=500, total post-warmup draws=2000.

           mean se_mean   sd    2.5%     25%     50%     75%   97.5% n_eff Rhat
beta[1]   24.89    0.05 1.38   22.19   23.98   24.91   25.76   27.71   897    1
beta[2]    2.92    0.06 1.77   -0.55    1.78    2.95    4.12    6.32   967    1
sigma     13.41    0.02 0.64   12.22   12.96   13.37   13.83   14.76  1111    1
lp__    -739.55    0.04 1.24 -742.69 -740.15 -739.21 -738.64 -738.14   840    1

Samples were drawn using NUTS(diag_e) at Sun Dec 06 09:18:16 2020.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
>
> beta.post <- extract(sesame_one_pred_b.sf1, "beta")$beta
> beta.mean2 <- colMeans(beta.post)
>
>
> iv.est.1 <- beta.mean2[2] / beta.mean1[2]
> print(iv.est.1)
[1] 8.077231
> library(rstan)
> library(ggplot2)
>
> source("10.5_CasualEffectsUsingIV.R") # where data was cleaned
The following objects are masked from sesame (pos = 3):

    _Isite_2, _Isite_3, _Isite_4, _Isite_5, age, agecat, encour, id, peabody, postbody,
    postclasf, postform, postlet, postnumb, postrelat, prebody, preclasf, preform,
    prelet, prenumb, prerelat, regular, rownames, setting, sex, site, viewcat, viewenc


SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 1: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 1: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 1: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 1: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 1: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 1: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 1: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 1: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 1: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 1: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 1:
Chain 1:  Elapsed Time: 0.06 seconds (Warm-up)
Chain 1:                0.06 seconds (Sampling)
Chain 1:                0.12 seconds (Total)
Chain 1:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 2: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 2: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 2: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 2: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 2: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 2: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 2: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 2: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 2: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 2: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 2: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 2:
Chain 2:  Elapsed Time: 0.057 seconds (Warm-up)
Chain 2:                0.048 seconds (Sampling)
Chain 2:                0.105 seconds (Total)
Chain 2:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 3: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 3: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 3: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 3: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 3: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 3: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 3: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 3: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 3: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 3: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 3: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 3:
Chain 3:  Elapsed Time: 0.057 seconds (Warm-up)
Chain 3:                0.057 seconds (Sampling)
Chain 3:                0.114 seconds (Total)
Chain 3:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 4: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 4: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 4: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 4: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 4: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 4: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 4: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 4: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 4: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 4: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 4: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 4:
Chain 4:  Elapsed Time: 0.058 seconds (Warm-up)
Chain 4:                0.051 seconds (Sampling)
Chain 4:                0.109 seconds (Total)
Chain 4:
Inference for Stan model: sesame_one_pred_a.
4 chains, each with iter=1000; warmup=500; thin=1;
post-warmup draws per chain=500, total post-warmup draws=2000.

          mean se_mean   sd   2.5%    25%    50%    75%  97.5% n_eff Rhat
beta[1]   0.54    0.00 0.04   0.47   0.52   0.54   0.57   0.63   931    1
beta[2]   0.36    0.00 0.05   0.27   0.33   0.36   0.40   0.46   854    1
sigma     0.38    0.00 0.02   0.35   0.37   0.38   0.39   0.42  1440    1
lp__    110.21    0.04 1.18 107.38 109.61 110.54 111.10 111.59   754    1

Samples were drawn using NUTS(diag_e) at Sun Dec 06 09:18:48 2020.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 1: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 1: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 1: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 1: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 1: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 1: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 1: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 1: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 1: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 1: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 1:
Chain 1:  Elapsed Time: 0.129 seconds (Warm-up)
Chain 1:                0.068 seconds (Sampling)
Chain 1:                0.197 seconds (Total)
Chain 1:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 2: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 2: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 2: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 2: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 2: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 2: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 2: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 2: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 2: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 2: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 2: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 2:
Chain 2:  Elapsed Time: 0.114 seconds (Warm-up)
Chain 2:                0.065 seconds (Sampling)
Chain 2:                0.179 seconds (Total)
Chain 2:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 3: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 3: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 3: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 3: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 3: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 3: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 3: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 3: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 3: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 3: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 3: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 3:
Chain 3:  Elapsed Time: 0.137 seconds (Warm-up)
Chain 3:                0.07 seconds (Sampling)
Chain 3:                0.207 seconds (Total)
Chain 3:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 4: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 4: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 4: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 4: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 4: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 4: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 4: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 4: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 4: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 4: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 4: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 4:
Chain 4:  Elapsed Time: 0.123 seconds (Warm-up)
Chain 4:                0.056 seconds (Sampling)
Chain 4:                0.179 seconds (Total)
Chain 4:
Inference for Stan model: sesame_one_pred_a.
4 chains, each with iter=1000; warmup=500; thin=1;
post-warmup draws per chain=500, total post-warmup draws=2000.

           mean se_mean   sd    2.5%     25%     50%     75%   97.5% n_eff Rhat
beta[1]   24.90    0.06 1.40   22.24   23.97   24.87   25.81   27.69   643    1
beta[2]    2.92    0.07 1.78   -0.57    1.70    2.94    4.17    6.28   692    1
sigma     13.39    0.02 0.61   12.31   12.96   13.35   13.79   14.66  1203    1
lp__    -739.48    0.04 1.15 -742.23 -740.00 -739.21 -738.63 -738.13   851    1

Samples were drawn using NUTS(diag_e) at Sun Dec 06 09:18:53 2020.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
[1] 8.002048

> pretest <- prelet
>
> ## 2 stage least squares (sesame_one_pred_a.stan)
> ## lm (watched ~ encouraged)
>
> dataList.1 <- list(N=length(watched), watched=watched,encouraged=encouraged)
> sesame_one_pred_2a.sf1 <- stan(file='sesame_one_pred_a.stan', data=dataList.1,
+                                iter=1000, chains=4)

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 1: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 1: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 1: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 1: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 1: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 1: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 1: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 1: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 1: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 1: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 1:
Chain 1:  Elapsed Time: 0.039 seconds (Warm-up)
Chain 1:                0.042 seconds (Sampling)
Chain 1:                0.081 seconds (Total)
Chain 1:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 2: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 2: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 2: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 2: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 2: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 2: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 2: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 2: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 2: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 2: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 2: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 2:
Chain 2:  Elapsed Time: 0.039 seconds (Warm-up)
Chain 2:                0.037 seconds (Sampling)
Chain 2:                0.076 seconds (Total)
Chain 2:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 3: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 3: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 3: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 3: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 3: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 3: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 3: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 3: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 3: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 3: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 3: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 3:
Chain 3:  Elapsed Time: 0.042 seconds (Warm-up)
Chain 3:                0.038 seconds (Sampling)
Chain 3:                0.08 seconds (Total)
Chain 3:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 4: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 4: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 4: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 4: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 4: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 4: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 4: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 4: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 4: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 4: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 4: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 4:
Chain 4:  Elapsed Time: 0.047 seconds (Warm-up)
Chain 4:                0.036 seconds (Sampling)
Chain 4:                0.083 seconds (Total)
Chain 4:
Warning message:
In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
  'C:/rtools40/usr/mingw_/bin/g++' not found
> print(sesame_one_pred_2a.sf1)
Inference for Stan model: sesame_one_pred_a.
4 chains, each with iter=1000; warmup=500; thin=1;
post-warmup draws per chain=500, total post-warmup draws=2000.

          mean se_mean   sd   2.5%    25%    50%    75%  97.5% n_eff Rhat
beta[1]   0.55    0.00 0.04   0.47   0.52   0.55   0.57   0.63   947    1
beta[2]   0.36    0.00 0.05   0.26   0.33   0.36   0.40   0.46   981    1
sigma     0.38    0.00 0.02   0.35   0.37   0.38   0.39   0.42  1380    1
lp__    110.14    0.05 1.29 106.88 109.56 110.51 111.08 111.58   786    1

Samples were drawn using NUTS(diag_e) at Sun Dec 06 09:18:58 2020.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).

> beta.post <- extract(sesame_one_pred_2a.sf1, "beta")$beta
> beta.mean2a <- colMeans(beta.post)
>
> watched.hat <- beta.mean2a[1] + beta.mean2a[2] * encouraged
>
> ## (sesame_one_pred_2b.stan)
> ## lm (y ~ watched.hat)
>
> dataList.2 <- list(N=length(y), watched=y,encouraged=watched.hat)
> sesame_one_pred_2b.sf1 <- stan(file='sesame_one_pred_a.stan', data=dataList.2,
+                                iter=1000, chains=4)

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 1: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 1: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 1: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 1: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 1: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 1: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 1: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 1: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 1: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 1: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 1:
Chain 1:  Elapsed Time: 0.133 seconds (Warm-up)
Chain 1:                0.086 seconds (Sampling)
Chain 1:                0.219 seconds (Total)
Chain 1:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 2: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 2: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 2: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 2: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 2: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 2: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 2: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 2: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 2: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 2: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 2: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 2:
Chain 2:  Elapsed Time: 0.15 seconds (Warm-up)
Chain 2:                0.092 seconds (Sampling)
Chain 2:                0.242 seconds (Total)
Chain 2:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 3: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 3: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 3: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 3: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 3: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 3: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 3: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 3: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 3: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 3: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 3: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 3:
Chain 3:  Elapsed Time: 0.14 seconds (Warm-up)
Chain 3:                0.088 seconds (Sampling)
Chain 3:                0.228 seconds (Total)
Chain 3:

SAMPLING FOR MODEL 'sesame_one_pred_a' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 4: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 4: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 4: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 4: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 4: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 4: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 4: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 4: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 4: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 4: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 4: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 4:
Chain 4:  Elapsed Time: 0.164 seconds (Warm-up)
Chain 4:                0.088 seconds (Sampling)
Chain 4:                0.252 seconds (Total)
Chain 4:

> print(sesame_one_pred_2b.sf1)
Inference for Stan model: sesame_one_pred_a.
4 chains, each with iter=1000; warmup=500; thin=1;
post-warmup draws per chain=500, total post-warmup draws=2000.

           mean se_mean   sd    2.5%     25%     50%     75%   97.5% n_eff Rhat
beta[1]   20.66    0.16 3.98   12.53   17.96   20.80   23.37   28.33   600 1.01
beta[2]    7.89    0.20 4.99   -2.41    4.55    7.68   11.26   18.15   607 1.01
sigma     13.42    0.02 0.62   12.28   12.99   13.40   13.84   14.68   823 1.00
lp__    -739.56    0.05 1.25 -742.81 -740.13 -739.22 -738.66 -738.15   608 1.00

Samples were drawn using NUTS(diag_e) at Sun Dec 06 09:19:03 2020.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
>
> ## Adjusting for covariates in a IV framework (sesame_multi_preds_3a.stan)
> ## lm (watched ~ encouraged + pretest + as.factor(site) + setting)
>
> dataList.3 <- list(N=length(watched), watched=watched,encouraged=encouraged,pretest=pretest, site=site,setting=setting)
> sesame_multi_pred_3a.sf1 <- stan(file='sesame_multi_preds_3a.stan',
+                                  data=dataList.3,
+                                  iter=1000, chains=4)

SAMPLING FOR MODEL 'sesame_multi_preds_3a' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 1: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 1: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 1: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 1: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 1: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 1: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 1: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 1: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 1: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 1: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 1:
Chain 1:  Elapsed Time: 1.361 seconds (Warm-up)
Chain 1:                0.607 seconds (Sampling)
Chain 1:                1.968 seconds (Total)
Chain 1:

SAMPLING FOR MODEL 'sesame_multi_preds_3a' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 2: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 2: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 2: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 2: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 2: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 2: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 2: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 2: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 2: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 2: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 2: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 2:
Chain 2:  Elapsed Time: 1.354 seconds (Warm-up)
Chain 2:                0.667 seconds (Sampling)
Chain 2:                2.021 seconds (Total)
Chain 2:

SAMPLING FOR MODEL 'sesame_multi_preds_3a' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 3: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 3: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 3: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 3: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 3: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 3: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 3: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 3: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 3: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 3: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 3: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 3:
Chain 3:  Elapsed Time: 1.287 seconds (Warm-up)
Chain 3:                0.573 seconds (Sampling)
Chain 3:                1.86 seconds (Total)
Chain 3:

SAMPLING FOR MODEL 'sesame_multi_preds_3a' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 4: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 4: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 4: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 4: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 4: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 4: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 4: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 4: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 4: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 4: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 4: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 4:
Chain 4:  Elapsed Time: 1.506 seconds (Warm-up)
Chain 4:                0.622 seconds (Sampling)
Chain 4:                2.128 seconds (Total)
Chain 4:
Warning message:
In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
  'C:/rtools40/usr/mingw_/bin/g++' not found
> print(sesame_multi_pred_3a.sf1)
Inference for Stan model: sesame_multi_preds_3a.
4 chains, each with iter=1000; warmup=500; thin=1;
post-warmup draws per chain=500, total post-warmup draws=2000.

          mean se_mean   sd   2.5%    25%    50%    75%  97.5% n_eff Rhat
beta[1]   0.66    0.00 0.11   0.45   0.58   0.66   0.73   0.88  1010    1
beta[2]   0.34    0.00 0.05   0.24   0.31   0.34   0.38   0.45  1507    1
beta[3]   0.01    0.00 0.00   0.00   0.00   0.00   0.01   0.01  2915    1
beta[4]   0.03    0.00 0.07  -0.11  -0.02   0.03   0.08   0.17  1147    1
beta[5]  -0.11    0.00 0.07  -0.24  -0.16  -0.12  -0.07   0.02  1133    1
beta[6]  -0.34    0.00 0.07  -0.49  -0.39  -0.34  -0.29  -0.21  1250    1
beta[7]  -0.29    0.00 0.11  -0.51  -0.36  -0.29  -0.22  -0.09  1313    1
beta[8]  -0.05    0.00 0.05  -0.15  -0.09  -0.06  -0.02   0.05  1336    1
sigma     0.36    0.00 0.02   0.32   0.34   0.35   0.37   0.39  1682    1
lp__    127.78    0.07 2.16 122.77 126.48 128.09 129.41 131.02   980    1

Samples were drawn using NUTS(diag_e) at Sun Dec 06 09:19:50 2020.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
>
> beta.post <- extract(sesame_multi_pred_3a.sf1, "beta")$beta
> beta.mean3a <- colMeans(beta.post)
>
> watched.hat <- beta.mean3a[1] + beta.mean3a[2] * encouraged + beta.mean3a[3] * pretest + beta.mean3a[4] * (site==2) + beta.mean3a[5] * (site==3) + beta.mean3a[6] * (site==4) + beta.mean3a[7] * (site==5) + beta.mean3a[8] * setting
>
> ## (sesame_multi_preds_3b.stan)
> ## lm (y ~ watched.hat + pretest + as.factor(site) + setting)
> dataList.4 <- list(N=length(watched.hat), watched=y,encouraged=watched.hat,pretest=pretest, site=site,setting=setting)
> sesame_multi_pred_3b.sf1 <- stan(file='sesame_multi_preds_3b.stan',
+                                  data=dataList.4,
+                                  iter=1000, chains=4)

SAMPLING FOR MODEL 'y_x' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 3: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 3: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 3: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 3: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 3: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 3: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 3: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 3: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 3: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 3: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 3: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 3:
Chain 3:  Elapsed Time: 0.075 seconds (Warm-up)
Chain 3:                0.197 seconds (Sampling)
Chain 3:                0.272 seconds (Total)
Chain 3:

SAMPLING FOR MODEL 'y_x' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 4: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 4: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 4: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 4: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 4: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 4: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 4: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 4: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 4: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 4: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 4: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 4:
Chain 4:  Elapsed Time: 0.146 seconds (Warm-up)
Chain 4:                0.347 seconds (Sampling)
Chain 4:                0.493 seconds (Total)
Chain 4:

SAMPLING FOR MODEL 'y_x' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 1: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 1: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 1: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 1: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 1: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 1: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 1: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 1: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 1: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 1: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 1:
Chain 1:  Elapsed Time: 0.015 seconds (Warm-up)
Chain 1:                0.01 seconds (Sampling)
Chain 1:                0.025 seconds (Total)
Chain 1:

SAMPLING FOR MODEL 'y_x' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration:   1 / 1000 [  0%]  (Warmup)
Chain 2: Iteration: 100 / 1000 [ 10%]  (Warmup)
Chain 2: Iteration: 200 / 1000 [ 20%]  (Warmup)
Chain 2: Iteration: 300 / 1000 [ 30%]  (Warmup)
Chain 2: Iteration: 400 / 1000 [ 40%]  (Warmup)
Chain 2: Iteration: 500 / 1000 [ 50%]  (Warmup)
Chain 2: Iteration: 501 / 1000 [ 50%]  (Sampling)
Chain 2: Iteration: 600 / 1000 [ 60%]  (Sampling)
Chain 2: Iteration: 700 / 1000 [ 70%]  (Sampling)
Chain 2: Iteration: 800 / 1000 [ 80%]  (Sampling)
Chain 2: Iteration: 900 / 1000 [ 90%]  (Sampling)
Chain 2: Iteration: 1000 / 1000 [100%]  (Sampling)
Chain 2:
Chain 2:  Elapsed Time: 0.018 seconds (Warm-up)
Chain 2:                0.014 seconds (Sampling)
Chain 2:                0.032 seconds (Total)
Chain 2:

Exports from E:/devpackage/projpred/src/glmfun.cpp:
   List glm_elnet_c(arma::mat x, Function pseudo_obs, arma::vec lambda, double alpha, bool intercept, arma::vec penalty, double thresh, int qa_updates_max, int pmax, bool pmax_strict, arma::vec beta, double beta0, arma::vec w0, int as_updates_max = 50)
   List glm_ridge_c(arma::mat x, Function pseudo_obs, double lambda, bool intercept, arma::vec penalty, arma::vec beta_init, arma::vec w_init, double thresh, int qa_updates_max, int ls_iter_max = 100, bool debug = false)
   List glm_forward_c(arma::mat x, Function pseudo_obs, double lambda, bool intercept, arma::vec penalty, double thresh, int qa_updates_max, int pmax, arma::vec w0, int ls_iter_max = 50)

E:/devpackage/projpred/src/RcppExports.cpp updated.
E:/devpackage/projpred/R/RcppExports.R updated.
Re-compiling projpred
-  installing *source* package 'projpred' ... (896ms)
   ** using staged installation
   ** libs
   "C:/rtools40/mingw64/bin/"g++ -std=gnu++11  -I"D:/softApp/R/include" -DNDEBUG  -I'D:/softApp/R/library/Rcpp/include' -I'D:/softApp/R/library/RcppArmadillo/include'        -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign -c RcppExports.cpp -o RcppExports.o
   "C:/rtools40/mingw64/bin/"g++ -std=gnu++11  -I"D:/softApp/R/include" -DNDEBUG  -I'D:/softApp/R/library/Rcpp/include' -I'D:/softApp/R/library/RcppArmadillo/include'        -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign -c glmfun.cpp -o glmfun.o
   C:/rtools40/mingw64/bin/g++ -std=gnu++11 -shared -s -static-libgcc -o projpred.dll tmp.def RcppExports.o glmfun.o -LD:/softApp/R/bin/x64 -lRlapack -LD:/softApp/R/bin/x64 -lRblas -lgfortran -lm -lquadmath -LD:/softApp/R/bin/x64 -lR
   installing to C:/Users/ADMINI~1/AppData/Local/Temp/RtmpUJncZE/devtools_install_33bc15895793/00LOCK-projpred/00new/projpred/libs/x64
-  DONE (projpred)
This is projpred version 2.0.2.
Writing NAMESPACE
Writing NAMESPACE
Writing reexports.Rd
-- Running 22 example files ------------------------------------------------------------ projpred --
Loading projpred
This is projpred version 2.0.2.

Loading projpred
This is projpred version 2.0.2.
Testing projpred
√ |  OK F W S | Context
√ |   8       | as.matrix.projection [7.4 s]                                                        
√ |  20       | cv-indices [0.2 s]                                                                  
√ | 215       | datafit [4.3 s]                                                                     
√ |  53       | formula [0.6 s]                                                                     
√ | 301       | elnet [5.4 s]                                                                       
√ |  19       | ridge [0.9 s]                                                                       
√ |  45       | miscellaneous [146.7 s]                                                            
x |  52 1     | proj_linpred [70.6 s]                                                               
----------------------------------------------------------------------------------------------------
Failure (test_proj_pred.R:67:5): proj_linpred: newdata is specified correctly
`proj_linpred(proj_solution_terms_list)` threw an error with unexpected message.
Expected match: "argument \"newdata\" is missing, with no default"
Actual message: "缺少参数\"newdata\",也没有缺省值"
Backtrace:
1. testthat::expect_error(...) test_proj_pred.R:67:4
6. projpred::proj_linpred(proj_solution_terms_list)
7. projpred::proj_helper(...) E:/devpackage/projpred/R/methods.R:206:2
----------------------------------------------------------------------------------------------------
x |  68 1     | proj_predict [32.3 s]                                                               
----------------------------------------------------------------------------------------------------
Failure (test_proj_pred.R:326:5): proj_predict: newdata is specified correctly
`proj_predict(proj_solution_terms_list)` threw an error with unexpected message.
Expected match: "argument \"newdata\" is missing, with no default"
Actual message: "缺少参数\"newdata\",也没有缺省值"
Backtrace:
1. testthat::expect_error(...) test_proj_pred.R:326:4
6. projpred::proj_predict(proj_solution_terms_list)
7. projpred::proj_helper(...) E:/devpackage/projpred/R/methods.R:252:2
----------------------------------------------------------------------------------------------------
√ | 121       | project [34.2 s]                                                                    
√ |  13       | refmodel [0.5 s]                                                                    
√ |   4       | syntax [10.9 s]                                                                     
x | 100 1     | varsel [6.6 s]                                                                     
----------------------------------------------------------------------------------------------------
Failure (test_varsel.R:196:5): Having something else than stan_glm as the fit throws an error
`varsel(rnorm(5), verbose = FALSE)` threw an error with unexpected message.
Expected match: "no applicable method"
Actual message: "\"family\"没有适用于\"c('double', 'numeric')\"目标对象的方法"
Backtrace:
  1. testthat::expect_error(varsel(rnorm(5), verbose = FALSE), regexp = "no applicable method") test_varsel.R:196:4
  7. projpred::varsel.default(rnorm(5), verbose = FALSE) E:/devpackage/projpred/R/varsel.R:89:4
  9. projpred::get_refmodel.default(object) E:/devpackage/projpred/R/refmodel.R:189:2
12. stats::family(object) E:/devpackage/projpred/R/extend_family.R:248:2
----------------------------------------------------------------------------------------------------
x | 135 1     | cv_varsel [103.5 s]                                                                 
----------------------------------------------------------------------------------------------------
Failure (test_varsel.R:400:7): Having something else than stan_glm as the fit throws an error
`cv_varsel(rnorm(5), verbose = FALSE)` threw an error with unexpected message.
Expected match: "no applicable method"
Actual message: "\"family\"没有适用于\"c('double', 'numeric')\"目标对象的方法"
Backtrace:
  1. testthat::expect_error(...) test_varsel.R:400:6
  7. projpred::cv_varsel.default(rnorm(5), verbose = FALSE) E:/devpackage/projpred/R/cv_varsel.R:81:2
  9. projpred::get_refmodel.default(object) E:/devpackage/projpred/R/refmodel.R:189:2
12. stats::family(object) E:/devpackage/projpred/R/refmodel.R:218:4
----------------------------------------------------------------------------------------------------
√ |  42       | summary [4.5 s]                                                                     
√ |   4       | plots [0.1 s]                                                                       
√ |  17       | suggest_size [2.1 s]                                                               

== Results =========================================================================================
Duration: 431.0 s

[ FAIL 4 | WARN 0 | SKIP 0 | PASS 1217 ]
>

Click the Refresh button to see progress of the chains
starting worker pid=248 on localhost:11785 at 10:36:45.397
starting worker pid=9116 on localhost:11785 at 10:36:45.576
Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo
Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo

CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.

SAMPLING FOR MODEL 'CCCMGARCH' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: WARNING: No variance estimation is
Chain 1:          performed for num_warmup < 20
Chain 1:
Chain 1: Iteration: 1 / 10 [ 10%]  (Warmup)
Chain 1: Iteration: 2 / 10 [ 20%]  (Warmup)
Chain 1: Iteration: 3 / 10 [ 30%]  (Warmup)
Chain 1: Iteration: 4 / 10 [ 40%]  (Warmup)

CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.

SAMPLING FOR MODEL 'CCCMGARCH' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: WARNING: No variance estimation is
Chain 2:          performed for num_warmup < 20
Chain 2:
Chain 2: Iteration: 1 / 10 [ 10%]  (Warmup)
Chain 2: Iteration: 2 / 10 [ 20%]  (Warmup)
Chain 2: Iteration: 3 / 10 [ 30%]  (Warmup)
Chain 1: Iteration: 5 / 10 [ 50%]  (Warmup)
Chain 2: Iteration: 4 / 10 [ 40%]  (Warmup)
Chain 1: Iteration: 6 / 10 [ 60%]  (Sampling)
Chain 1: Iteration: 7 / 10 [ 70%]  (Sampling)
Chain 1: Iteration: 8 / 10 [ 80%]  (Sampling)
Chain 1: Iteration: 9 / 10 [ 90%]  (Sampling)
Chain 2: Iteration: 5 / 10 [ 50%]  (Warmup)
Chain 1: Iteration: 10 / 10 [100%]  (Sampling)
Chain 1:
Chain 1:  Elapsed Time: 0.215 seconds (Warm-up)
Chain 1:                0.282 seconds (Sampling)
Chain 1:                0.497 seconds (Total)
Chain 1:

CHECKING DATA AND PREPROCESSING FOR MODEL 'CCCMGARCH' NOW.

COMPILING MODEL 'CCCMGARCH' NOW.

STARTING SAMPLER FOR MODEL 'CCCMGARCH' NOW.

SAMPLING FOR MODEL 'CCCMGARCH' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: WARNING: No variance estimation is
Chain 3:          performed for num_warmup < 20
Chain 3:
Chain 3: Iteration: 1 / 10 [ 10%]  (Warmup)
Chain 2: Iteration: 6 / 10 [ 60%]  (Sampling)
Chain 3: Iteration: 2 / 10 [ 20%]  (Warmup)
Chain 3: Iteration: 3 / 10 [ 30%]  (Warmup)
Chain 3: Iteration: 4 / 10 [ 40%]  (Warmup)
Chain 3: Iteration: 5 / 10 [ 50%]  (Warmup)
Chain 2: Iteration: 7 / 10 [ 70%]  (Sampling)
Chain 2: Iteration: 8 / 10 [ 80%]  (Sampling)
Chain 3: Iteration: 6 / 10 [ 60%]  (Sampling)
Chain 3: Iteration: 7 / 10 [ 70%]  (Sampling)
Chain 3: Iteration: 8 / 10 [ 80%]  (Sampling)
Chain 2: Iteration: 9 / 10 [ 90%]  (Sampling)
Chain 3: Iteration: 9 / 10 [ 90%]  (Sampling)