* using log directory ‘/data/blackswan/ripley/R/packages/tests-devel/hbsaems.Rcheck’
* using R Under development (unstable) (2025-12-09 r89129)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)
    GNU Fortran (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)
* running under: Fedora Linux 40 (Workstation Edition)
* using session charset: UTF-8
* checking for file ‘hbsaems/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘hbsaems’ version ‘0.1.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘hbsaems’ can be installed ... [14s/14s] OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [20s/21s] OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
* checking examples with --run-donttest ... [38m/36m] ERROR
Running examples in ‘hbsaems-Ex.R’ failed
The error most likely occurred in:

> ### Name: hbm_betalogitnorm
> ### Title: Small Area Estimation using Hierarchical Bayesian under Beta
> ###   Distribution
> ### Aliases: hbm_betalogitnorm
> 
> ### ** Examples
> 
> ## No test: 
> 
> # Load the example dataset
> library(hbsaems)
> data("data_betalogitnorm")
> 
> # Prepare the dataset
> data <- data_betalogitnorm
> 
> # Fit Beta Model
> model1 <- hbm_betalogitnorm(
+ response = "y",
+ predictors = c("x1", "x2", "x3"),
+ data = data
+ )
Compiling Stan program...
Start sampling

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 0.000208 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.08 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
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Chain 1: 
Chain 1:  Elapsed Time: 2.234 seconds (Warm-up)
Chain 1:                1.453 seconds (Sampling)
Chain 1:                3.687 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 7.5e-05 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.75 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2: 
Chain 2: 
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Chain 2: 
Chain 2:  Elapsed Time: 2.01 seconds (Warm-up)
Chain 2:                1.469 seconds (Sampling)
Chain 2:                3.479 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 0.000129 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.29 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3: 
Chain 3: 
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Chain 3: 
Chain 3:  Elapsed Time: 2.112 seconds (Warm-up)
Chain 3:                1.49 seconds (Sampling)
Chain 3:                3.602 seconds (Total)
Chain 3: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 7.4e-05 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.74 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4: 
Chain 4: 
Chain 4: Iteration:    1 / 4000 [  0%]  (Warmup)
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Chain 4: 
Chain 4:  Elapsed Time: 2.011 seconds (Warm-up)
Chain 4:                1.503 seconds (Sampling)
Chain 4:                3.514 seconds (Total)
Chain 4: 
Warning: There were 1 divergent transitions after warmup. See
https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
to find out why this is a problem and how to eliminate them.
Warning: Examine the pairs() plot to diagnose sampling problems

> summary(model1)

======= Hierarchical Bayesian Model Summary (from hbmfit) =======

--- Full brms Model Summary ---
Warning: There were 1 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
 Family: beta 
  Links: mu = logit 
Formula: y ~ x1 + x2 + x3 + (1 | group) 
   Data: data (Number of observations: 100) 
  Draws: 4 chains, each with iter = 4000; warmup = 2000; thin = 1;
         total post-warmup draws = 8000

Multilevel Hyperparameters:
~group (Number of levels: 100) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     0.16      0.12     0.01     0.46 1.00     4929     4634

Regression Coefficients:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept    -0.46      0.73    -1.90     0.97 1.00    15426     5727
x1           -0.13      0.06    -0.25    -0.01 1.00    14558     5871
x2           -0.07      0.04    -0.14     0.02 1.00    15224     5961
x3            0.06      0.03    -0.01     0.13 1.00    15312     6121

Further Distributional Parameters:
    Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
phi     0.88      0.11     0.68     1.11 1.00     9328     5803

Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
Warning: There were 1 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup


===== Detailed Group-Level Effects (Random Effects) Summary =====

--- Grouping Factor: group ---
The table below (in the next console output) contains group-level estimates
for the factor 'group', with dimensions 1 rows x 7 columns.

Columns: Estimate, Est.Error, l-95% CI, u-95% CI, Rhat, Bulk_ESS, Tail_ESS.               Estimate Est.Error    l-95% CI  u-95% CI      Rhat Bulk_ESS
sd(Intercept) 0.1595482 0.1245037 0.006149041 0.4610262 0.9999258 4928.578
              Tail_ESS
sd(Intercept) 4633.865


=========== Missing Data Handling (specified in hbm) ============
No specific missing data handling method was applied through the hbm function.
(brms internal defaults for NA handling in predictors/response may have applied if NAs were present and not pre-processed).
> 
> # if you have the information of n and deff values you can use the following model
> model1 <- hbm_betalogitnorm(
+ response = "y",
+ predictors = c("x1", "x2", "x3"),
+ n = "n",
+ deff = "deff",
+ data = data
+ )
Compiling Stan program...
Start sampling

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 0.000163 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.63 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Iteration:    1 / 4000 [  0%]  (Warmup)
Chain 1: Iteration:  400 / 4000 [ 10%]  (Warmup)
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Chain 1: Iteration: 3600 / 4000 [ 90%]  (Sampling)
Chain 1: Iteration: 4000 / 4000 [100%]  (Sampling)
Chain 1: 
Chain 1:  Elapsed Time: 2.461 seconds (Warm-up)
Chain 1:                1.771 seconds (Sampling)
Chain 1:                4.232 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 8.1e-05 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.81 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2: 
Chain 2: 
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Chain 2: Iteration: 4000 / 4000 [100%]  (Sampling)
Chain 2: 
Chain 2:  Elapsed Time: 2.444 seconds (Warm-up)
Chain 2:                2.092 seconds (Sampling)
Chain 2:                4.536 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 7.1e-05 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.71 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3: 
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Chain 3: Iteration: 4000 / 4000 [100%]  (Sampling)
Chain 3: 
Chain 3:  Elapsed Time: 2.403 seconds (Warm-up)
Chain 3:                1.803 seconds (Sampling)
Chain 3:                4.206 seconds (Total)
Chain 3: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 8.4e-05 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.84 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4: 
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Chain 4: Iteration: 4000 / 4000 [100%]  (Sampling)
Chain 4: 
Chain 4:  Elapsed Time: 2.428 seconds (Warm-up)
Chain 4:                1.906 seconds (Sampling)
Chain 4:                4.334 seconds (Total)
Chain 4: 
> summary(model1)

======= Hierarchical Bayesian Model Summary (from hbmfit) =======

--- Full brms Model Summary ---
 Family: beta 
  Links: mu = logit; phi = identity 
Formula: y ~ x1 + x2 + x3 + (1 | group) 
         phi ~ 0 + offset(phi_fixed)
   Data: data (Number of observations: 100) 
  Draws: 4 chains, each with iter = 4000; warmup = 2000; thin = 1;
         total post-warmup draws = 8000

Multilevel Hyperparameters:
~group (Number of levels: 100) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     2.44      0.20     2.07     2.87 1.00     1598     3358

Regression Coefficients:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept    -1.40      1.50    -4.34     1.54 1.00     1094     1869
x1           -0.34      0.12    -0.58    -0.10 1.00      931     1961
x2           -0.15      0.08    -0.32     0.01 1.00     1145     1917
x3            0.17      0.07     0.03     0.31 1.00     1114     1964

Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).


===== Detailed Group-Level Effects (Random Effects) Summary =====

--- Grouping Factor: group ---
The table below (in the next console output) contains group-level estimates
for the factor 'group', with dimensions 1 rows x 7 columns.

Columns: Estimate, Est.Error, l-95% CI, u-95% CI, Rhat, Bulk_ESS, Tail_ESS.              Estimate Est.Error l-95% CI u-95% CI     Rhat Bulk_ESS Tail_ESS
sd(Intercept) 2.442547 0.2031652 2.071052 2.869263 1.003755 1597.815 3357.677


=========== Missing Data Handling (specified in hbm) ============
No specific missing data handling method was applied through the hbm function.
(brms internal defaults for NA handling in predictors/response may have applied if NAs were present and not pre-processed).
> 
> # From this stage to the next will be explained the construction of the model with 
> # the condition that the user has information on the value of n and deff. 
> # If you do not have information related to the value of n and deff 
> # then simply delete the parameters n and deff in your model.
> 
> # Fit Beta Model with Grouping Variable as Random Effect
> model2 <- hbm_betalogitnorm(
+ response = "y",
+ predictors = c("x1", "x2", "x3"),
+ n = "n",
+ deff = "deff",
+ group = "group",
+ data = data
+ )
Compiling Stan program...
Start sampling

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 0.000136 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.36 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Iteration:    1 / 4000 [  0%]  (Warmup)
Chain 1: Iteration:  400 / 4000 [ 10%]  (Warmup)
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Chain 1: Iteration: 4000 / 4000 [100%]  (Sampling)
Chain 1: 
Chain 1:  Elapsed Time: 2.415 seconds (Warm-up)
Chain 1:                2.185 seconds (Sampling)
Chain 1:                4.6 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 8.6e-05 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.86 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2: 
Chain 2: 
Chain 2: Iteration:    1 / 4000 [  0%]  (Warmup)
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Chain 2: Iteration: 4000 / 4000 [100%]  (Sampling)
Chain 2: 
Chain 2:  Elapsed Time: 2.562 seconds (Warm-up)
Chain 2:                1.726 seconds (Sampling)
Chain 2:                4.288 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 8.3e-05 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.83 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3: 
Chain 3: 
Chain 3: Iteration:    1 / 4000 [  0%]  (Warmup)
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Chain 3: Iteration: 4000 / 4000 [100%]  (Sampling)
Chain 3: 
Chain 3:  Elapsed Time: 2.445 seconds (Warm-up)
Chain 3:                1.717 seconds (Sampling)
Chain 3:                4.162 seconds (Total)
Chain 3: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 9.1e-05 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.91 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4: 
Chain 4: 
Chain 4: Iteration:    1 / 4000 [  0%]  (Warmup)
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Chain 4: 
Chain 4:  Elapsed Time: 2.557 seconds (Warm-up)
Chain 4:                1.687 seconds (Sampling)
Chain 4:                4.244 seconds (Total)
Chain 4: 
> summary(model2)

======= Hierarchical Bayesian Model Summary (from hbmfit) =======

--- Full brms Model Summary ---
 Family: beta 
  Links: mu = logit; phi = identity 
Formula: y ~ x1 + x2 + x3 + (1 | group) 
         phi ~ 0 + offset(phi_fixed)
   Data: data (Number of observations: 100) 
  Draws: 4 chains, each with iter = 4000; warmup = 2000; thin = 1;
         total post-warmup draws = 8000

Multilevel Hyperparameters:
~group (Number of levels: 100) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     2.42      0.19     2.07     2.84 1.00     1763     3727

Regression Coefficients:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept    -1.48      1.45    -4.30     1.45 1.00      934     2060
x1           -0.34      0.12    -0.57    -0.10 1.01      988     2029
x2           -0.15      0.08    -0.32     0.02 1.01     1116     2396
x3            0.17      0.07     0.04     0.30 1.01      969     2240

Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).


===== Detailed Group-Level Effects (Random Effects) Summary =====

--- Grouping Factor: group ---
The table below (in the next console output) contains group-level estimates
for the factor 'group', with dimensions 1 rows x 7 columns.

Columns: Estimate, Est.Error, l-95% CI, u-95% CI, Rhat, Bulk_ESS, Tail_ESS.              Estimate Est.Error l-95% CI u-95% CI     Rhat Bulk_ESS Tail_ESS
sd(Intercept) 2.418471 0.1946439 2.070403  2.83801 1.003646 1762.504 3727.412


=========== Missing Data Handling (specified in hbm) ============
No specific missing data handling method was applied through the hbm function.
(brms internal defaults for NA handling in predictors/response may have applied if NAs were present and not pre-processed).
> 
> # Fit Beta Model With Missing Data
> data_miss <- data
> data_miss[5:7, "y"] <- NA
> 
> # a. Handling missing data by deleted (Only if missing in response)
> model3 <- hbm_betalogitnorm(
+ response = "y",
+ predictors = c("x1", "x2", "x3"),
+ n = "n",
+ deff = "deff",
+ data = data_miss,
+ handle_missing = "deleted"
+ )
Rows with missing response variable were removed due to handle_missing = 'deleted'.
Compiling Stan program...
Start sampling

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 0.000121 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.21 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
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Chain 1: 
Chain 1:  Elapsed Time: 2.42 seconds (Warm-up)
Chain 1:                1.923 seconds (Sampling)
Chain 1:                4.343 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 7.2e-05 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.72 seconds.
Chain 2: Adjust your expectations accordingly!
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Chain 2: 
Chain 2:  Elapsed Time: 2.44 seconds (Warm-up)
Chain 2:                1.729 seconds (Sampling)
Chain 2:                4.169 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 7.9e-05 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.79 seconds.
Chain 3: Adjust your expectations accordingly!
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Chain 3: 
Chain 3:  Elapsed Time: 2.344 seconds (Warm-up)
Chain 3:                1.863 seconds (Sampling)
Chain 3:                4.207 seconds (Total)
Chain 3: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 9.5e-05 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.95 seconds.
Chain 4: Adjust your expectations accordingly!
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Chain 4: 
Chain 4:  Elapsed Time: 2.389 seconds (Warm-up)
Chain 4:                1.671 seconds (Sampling)
Chain 4:                4.06 seconds (Total)
Chain 4: 
> summary(model3)

======= Hierarchical Bayesian Model Summary (from hbmfit) =======

--- Full brms Model Summary ---
 Family: beta 
  Links: mu = logit; phi = identity 
Formula: y ~ x1 + x2 + x3 + (1 | group) 
         phi ~ 0 + offset(phi_fixed)
   Data: data (Number of observations: 97) 
  Draws: 4 chains, each with iter = 4000; warmup = 2000; thin = 1;
         total post-warmup draws = 8000

Multilevel Hyperparameters:
~group (Number of levels: 97) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     2.41      0.21     2.04     2.86 1.01     1613     2752

Regression Coefficients:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept    -2.09      1.49    -5.00     0.79 1.00     1045     1970
x1           -0.29      0.12    -0.53    -0.05 1.00     1043     2143
x2           -0.14      0.08    -0.30     0.02 1.00     1229     2223
x3            0.18      0.07     0.04     0.32 1.00     1042     2255

Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).


===== Detailed Group-Level Effects (Random Effects) Summary =====

--- Grouping Factor: group ---
The table below (in the next console output) contains group-level estimates
for the factor 'group', with dimensions 1 rows x 7 columns.

Columns: Estimate, Est.Error, l-95% CI, u-95% CI, Rhat, Bulk_ESS, Tail_ESS.              Estimate Est.Error l-95% CI u-95% CI     Rhat Bulk_ESS Tail_ESS
sd(Intercept) 2.410979 0.2076733 2.043443 2.856038 1.006512 1612.774 2752.146


=========== Missing Data Handling (specified in hbm) ============
Missing data handling method specified in hbm: deleted
> 
> # b. Handling missing data using multiple imputation (m=5)
> model4 <- hbm_betalogitnorm(
+ response = "y",
+ predictors = c("x1", "x2", "x3"),
+ n = "n",
+ deff = "deff",
+ data = data_miss,
+ handle_missing = "multiple"
+ )
Missing data detected. Using brms_multiple imputation with m = 5

 iter imp variable
  1   1  y*
  1   2  y*
  1   3  y*
  1   4  y*
  1   5  y*
  2   1  y*
  2   2  y*
  2   3  y*
  2   4  y*
  2   5  y*
  3   1  y*
  3   2  y*
  3   3  y*
  3   4  y*
  3   5  y*
  4   1  y*
  4   2  y*
  4   3  y*
  4   4  y*
  4   5  y*
  5   1  y*
  5   2  y*
  5   3  y*
  5   4  y*
  5   5  y*
Warning: Number of logged events: 55
Compiling the C++ model

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 0.000164 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.64 seconds.
Chain 1: Adjust your expectations accordingly!
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Chain 1: 
Chain 1:  Elapsed Time: 2.522 seconds (Warm-up)
Chain 1:                1.762 seconds (Sampling)
Chain 1:                4.284 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 0.0001 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1 seconds.
Chain 2: Adjust your expectations accordingly!
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Chain 2:                4.295 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 8.5e-05 seconds
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Chain 3: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 9e-05 seconds
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Chain 4: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 9.9e-05 seconds
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Chain 1: Adjust your expectations accordingly!
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Chain 1: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 9.2e-05 seconds
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Chain 2: Adjust your expectations accordingly!
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Chain 2: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 7.2e-05 seconds
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Chain 3:  Elapsed Time: 2.415 seconds (Warm-up)
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Chain 3: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 7.8e-05 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.78 seconds.
Chain 4: Adjust your expectations accordingly!
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Chain 4: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 0.000105 seconds
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Chain 1: Adjust your expectations accordingly!
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Chain 1:  Elapsed Time: 2.427 seconds (Warm-up)
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Chain 1: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 7.8e-05 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.78 seconds.
Chain 2: Adjust your expectations accordingly!
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Chain 2: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 7.6e-05 seconds
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Chain 3: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 7.9e-05 seconds
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Chain 4: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 8e-05 seconds
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Chain 1: Adjust your expectations accordingly!
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Chain 1:  Elapsed Time: 2.342 seconds (Warm-up)
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Chain 1: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 9e-05 seconds
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Chain 2: Adjust your expectations accordingly!
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Chain 2: 
Chain 2:  Elapsed Time: 2.451 seconds (Warm-up)
Chain 2:                1.682 seconds (Sampling)
Chain 2:                4.133 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 7.8e-05 seconds
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Chain 3: Adjust your expectations accordingly!
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Chain 3: 
Chain 3:  Elapsed Time: 2.522 seconds (Warm-up)
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Chain 3:                4.207 seconds (Total)
Chain 3: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 8.3e-05 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.83 seconds.
Chain 4: Adjust your expectations accordingly!
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Chain 4: 
Chain 4:  Elapsed Time: 2.347 seconds (Warm-up)
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Chain 4:                4.002 seconds (Total)
Chain 4: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 9.2e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.92 seconds.
Chain 1: Adjust your expectations accordingly!
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Chain 1: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 7.3e-05 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.73 seconds.
Chain 2: Adjust your expectations accordingly!
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Chain 2: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 9e-05 seconds
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Chain 3: 

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 8.4e-05 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.84 seconds.
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Chain 4: 
Chain 4:  Elapsed Time: 2.458 seconds (Warm-up)
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Chain 4:                4.264 seconds (Total)
Chain 4: 
Fitting imputed model 1
Start sampling
Fitting imputed model 2
Start sampling
Fitting imputed model 3
Start sampling
Fitting imputed model 4
Start sampling
Fitting imputed model 5
Start sampling
> summary(model4)

======= Hierarchical Bayesian Model Summary (from hbmfit) =======

--- Full brms Model Summary ---
 Family: beta 
  Links: mu = logit; phi = identity 
Formula: y ~ x1 + x2 + x3 + (1 | group) 
         phi ~ 0 + offset(phi_fixed)
   Data: data_multiple (Number of observations: 100) 
  Draws: 20 chains, each with iter = 4000; warmup = 2000; thin = 1;
         total post-warmup draws = 40000

Multilevel Hyperparameters:
~group (Number of levels: 100) 
              Estimate Est.Error l-95% CI u-95% CI
sd(Intercept)     2.38      0.20     2.02     2.80

Regression Coefficients:
          Estimate Est.Error l-95% CI u-95% CI
Intercept    -1.96      1.46    -4.80     0.91
x1           -0.30      0.12    -0.53    -0.07
x2           -0.15      0.08    -0.32     0.00
x3            0.18      0.07     0.05     0.32

Draws were sampled using sampling(NUTS). Overall Rhat and ESS estimates
are not informative for brm_multiple models and are hence not displayed.
Please see ?brm_multiple for how to assess convergence of such models.


===== Detailed Group-Level Effects (Random Effects) Summary =====

--- Grouping Factor: group ---
The table below (in the next console output) contains group-level estimates
for the factor 'group', with dimensions 1 rows x 4 columns.

Columns: Estimate, Est.Error, l-95% CI, u-95% CI.              Estimate Est.Error l-95% CI u-95% CI
sd(Intercept) 2.378592 0.1991114 2.021503 2.796996


=========== Missing Data Handling (specified in hbm) ============
Missing data handling method specified in hbm: multiple
> 
> # c. Handle missing data during model fitting using mi()
> data_miss <- data
> data_miss$x1[3:5] <- NA 
> data_miss$x2[14:17] <- NA 
> model5 <- hbm_betalogitnorm(
+ response = "y",
+ predictors = c("x1", "x2", "x3"),
+ n = "n",
+ deff = "deff",
+ group = "group",
+ data = data_miss,
+ handle_missing = "model"
+ )
Missing data detected. Using mi() specification.
Setting 'rescor' to FALSE by default for this model
Error in `.validate_prior()`:
! The following priors do not correspond to any model parameter: 
Intercept ~ student_t(4,0,10)
b ~ student_t(4,0,2.5)
Function 'default_prior' might be helpful to you.
Backtrace:
    ▆
 1. └─hbsaems::hbm_betalogitnorm(...)
 2.   └─hbsaems::hbm(...)
 3.     └─brms::brm(...)
 4.       └─brms:::.validate_prior(prior, bframe = bframe, sample_prior = sample_prior)
 5.         └─brms:::stop2(...)
 6.           └─rlang::abort(...)
Execution halted
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘spelling.R’
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... [327s/328s] OK
* checking PDF version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* checking for new files in some other directories ... OK
* DONE

Status: 1 ERROR
See
  ‘/data/blackswan/ripley/R/packages/tests-devel/hbsaems.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 43:21.83, 2523.83 + 214.75