This method allows to update an existing brmsfit object.
Usage
# S3 method for class 'brmsfit'
update(object, formula., newdata = NULL, recompile = NULL, ...)Arguments
- object
An object of class
brmsfit.- formula.
Changes to the formula; for details see
update.formulaandbrmsformula.- newdata
Optional
data.frameto update the model with new data. Data-dependent default priors will not be updated automatically.- recompile
Logical, indicating whether the Stan model should be recompiled. If
NULL(the default),updatetries to figure out internally, if recompilation is necessary. Setting it toFALSEwill cause all Stan code changing arguments to be ignored.- ...
Other arguments passed to
brm.
Details
When updating a brmsfit created with the cmdstanr
backend in a different R session, a recompilation will be triggered
because by default, cmdstanr writes the model executable to a
temporary directory. To avoid that, set option
"cmdstanr_write_stan_file_dir" to a nontemporary path of your choice
before creating the original brmsfit (see section 'Examples' below).
Examples
# \dontrun{
fit1 <- brm(time | cens(censored) ~ age * sex + disease + (1|patient),
data = kidney, family = gaussian("log"))
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1:
#> Chain 1: Gradient evaluation took 2.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.29 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.091 seconds (Warm-up)
#> Chain 1: 0.051 seconds (Sampling)
#> Chain 1: 0.142 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.179 seconds (Warm-up)
#> Chain 2: 0.259 seconds (Sampling)
#> Chain 2: 0.438 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1.8e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.21 seconds (Warm-up)
#> Chain 3: 0.149 seconds (Sampling)
#> Chain 3: 0.359 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4:
#> Chain 4: Gradient evaluation took 1.8e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.242 seconds (Warm-up)
#> Chain 4: 0.091 seconds (Sampling)
#> Chain 4: 0.333 seconds (Total)
#> Chain 4:
#> Warning: There were 3248 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: There were 3 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 5.75, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
summary(fit1)
#> Warning: Inference for the model posterior has not converged (some Rhats are > 1.05). Be careful when analysing the results! We recommend running more iterations or setting stronger priors.
#> Warning: There were 3248 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: gaussian
#> Links: mu = log
#> Formula: time | cens(censored) ~ age * sex + disease + (1 | patient)
#> Data: kidney (Number of observations: 76)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Multilevel Hyperparameters:
#> ~patient (Number of levels: 38)
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept) 1.91 1.38 0.23 3.95 5.28 4 NA
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 2.81 10.05 -14.53 9.43 5.76 4 4
#> age -0.60 0.31 -0.87 -0.07 5.76 4 NA
#> sexfemale -0.02 1.83 -1.93 1.90 5.42 4 NA
#> diseaseGN -0.31 1.48 -1.90 1.77 3.93 4 4
#> diseaseAN -1.31 0.70 -1.78 -0.11 5.16 4 NA
#> diseasePKD 0.03 0.44 -0.72 0.44 4.17 5 22
#> age:sexfemale 0.72 0.11 0.57 0.87 5.72 4 4
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 2.38 0.96 1.60 4.01 5.51 4 NA
#>
#> 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).
## remove effects of 'disease'
fit2 <- update(fit1, formula. = ~ . - disease)
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1:
#> Chain 1: Gradient evaluation took 6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.6 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.19 seconds (Warm-up)
#> Chain 1: 0.137 seconds (Sampling)
#> Chain 1: 0.327 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.077 seconds (Warm-up)
#> Chain 2: 0.05 seconds (Sampling)
#> Chain 2: 0.127 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3:
#> Chain 3: Gradient evaluation took 1.9e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.176 seconds (Warm-up)
#> Chain 3: 0.455 seconds (Sampling)
#> Chain 3: 0.631 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4: Rejecting initial value:
#> Chain 4: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4:
#> Chain 4: Gradient evaluation took 2.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.076 seconds (Warm-up)
#> Chain 4: 0.051 seconds (Sampling)
#> Chain 4: 0.127 seconds (Total)
#> Chain 4:
#> Warning: There were 2510 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: There were 2 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is Inf, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
summary(fit2)
#> Warning: Inference for the model posterior has not converged (some Rhats are > 1.05). Be careful when analysing the results! We recommend running more iterations or setting stronger priors.
#> Warning: There were 2510 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: gaussian
#> Links: mu = log
#> Formula: time | cens(censored) ~ age + sex + (1 | patient) + age:sex
#> Data: kidney (Number of observations: 76)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Multilevel Hyperparameters:
#> ~patient (Number of levels: 38)
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept) 2.08 1.69 0.41 4.62 12.76 4 NA
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept -28.30 39.42 -79.50 11.58 13.15 4 4
#> age 0.14 0.97 -0.93 1.45 13.15 4 NA
#> sexfemale 0.12 1.10 -1.08 1.77 9.40 4 NA
#> age:sexfemale 0.67 0.14 0.49 0.87 5.97 4 4
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 4.91 2.23 2.00 7.54 5.97 4 11
#>
#> 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).
## remove the group specific term of 'patient' and
## change the data (just take a subset in this example)
fit3 <- update(fit1, formula. = ~ . - (1|patient),
newdata = kidney[1:38, ])
#> The desired updates require recompiling the model
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 3.182 seconds (Warm-up)
#> Chain 1: 4.721 seconds (Sampling)
#> Chain 1: 7.903 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.1e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.532 seconds (Warm-up)
#> Chain 2: 5.304 seconds (Sampling)
#> Chain 2: 5.836 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1.1e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.128 seconds (Warm-up)
#> Chain 3: 3.965 seconds (Sampling)
#> Chain 3: 4.093 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1.1e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 2.826 seconds (Warm-up)
#> Chain 4: 5.022 seconds (Sampling)
#> Chain 4: 7.848 seconds (Total)
#> Chain 4:
#> Warning: There were 2598 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See
#> https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded
#> Warning: There were 2 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 2.35, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
summary(fit3)
#> Warning: Inference for the model posterior has not converged (some Rhats are > 1.05). Be careful when analysing the results! We recommend running more iterations or setting stronger priors.
#> Family: gaussian
#> Links: mu = log
#> Formula: time | cens(censored) ~ age + sex + disease + age:sex
#> Data: kidney[1:38, ] (Number of observations: 38)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept -53.42 104.09 -363.93 10.53 1.36 10 84
#> age -0.19 2.36 -6.37 3.72 1.28 20 40
#> sexfemale 58.96 104.12 -4.90 369.67 1.37 9 84
#> diseaseGN -4.92 14.60 -58.97 0.22 1.45 8 11
#> diseaseAN -177.68 382.81 -1335.14 -1.18 1.60 7 13
#> diseasePKD -157.22 403.90 -1606.74 0.07 1.93 6 20
#> age:sexfemale 0.19 2.36 -3.73 6.36 1.29 20 40
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 109.56 38.01 1.48 154.88 1.47 8 11
#>
#> 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).
## use another family and add population-level priors
fit4 <- update(fit1, family = weibull(), init = "0",
prior = set_prior("normal(0,5)"))
#> The desired updates require recompiling the model
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 2.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.28 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 1.456 seconds (Warm-up)
#> Chain 1: 0.592 seconds (Sampling)
#> Chain 1: 2.048 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 2.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 1.566 seconds (Warm-up)
#> Chain 2: 0.585 seconds (Sampling)
#> Chain 2: 2.151 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 2.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 1.48 seconds (Warm-up)
#> Chain 3: 0.587 seconds (Sampling)
#> Chain 3: 2.067 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 2.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 1.69 seconds (Warm-up)
#> Chain 4: 0.587 seconds (Sampling)
#> Chain 4: 2.277 seconds (Total)
#> Chain 4:
summary(fit4)
#> Family: weibull
#> Links: mu = log
#> Formula: time | cens(censored) ~ age * sex + disease + (1 | patient)
#> Data: kidney (Number of observations: 76)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Multilevel Hyperparameters:
#> ~patient (Number of levels: 38)
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept) 0.54 0.25 0.04 1.00 1.01 573 691
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 2.88 0.93 1.13 4.74 1.00 2123 2676
#> age 0.02 0.02 -0.03 0.07 1.00 1703 2509
#> sexfemale 2.77 1.10 0.56 4.87 1.00 2106 2416
#> diseaseGN -0.27 0.50 -1.28 0.71 1.00 2531 2835
#> diseaseAN -0.57 0.47 -1.52 0.37 1.00 2509 2631
#> diseasePKD 0.75 0.72 -0.69 2.12 1.00 2285 2604
#> age:sexfemale -0.03 0.03 -0.08 0.02 1.00 1945 2244
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> shape 1.12 0.16 0.86 1.46 1.00 1251 2546
#>
#> 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).
## to avoid a recompilation when updating a 'cmdstanr'-backend fit in a fresh
## R session, set option 'cmdstanr_write_stan_file_dir' before creating the
## initial 'brmsfit'
## CAUTION: the following code creates some files in the current working
## directory: two 'model_<hash>.stan' files, one 'model_<hash>(.exe)'
## executable, and one 'fit_cmdstanr_<some_number>.rds' file
set.seed(7)
fname <- paste0("fit_cmdstanr_", sample.int(.Machine$integer.max, 1))
options(cmdstanr_write_stan_file_dir = getwd())
fit_cmdstanr <- brm(rate ~ conc + state,
data = Puromycin,
backend = "cmdstanr",
file = fname)
# now restart the R session and run the following (after attaching 'brms')
set.seed(7)
fname <- paste0("fit_cmdstanr_", sample.int(.Machine$integer.max, 1))
fit_cmdstanr <- brm(rate ~ conc + state,
data = Puromycin,
backend = "cmdstanr",
file = fname)
upd_cmdstanr <- update(fit_cmdstanr,
formula. = rate ~ conc)
#> Start sampling
#> Running MCMC with 4 sequential chains...
#>
#> Chain 1 Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1 Iteration: 100 / 2000 [ 5%] (Warmup)
#> Chain 1 Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1 Iteration: 300 / 2000 [ 15%] (Warmup)
#> Chain 1 Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1 Iteration: 500 / 2000 [ 25%] (Warmup)
#> Chain 1 Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1 Iteration: 700 / 2000 [ 35%] (Warmup)
#> Chain 1 Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1 Iteration: 900 / 2000 [ 45%] (Warmup)
#> Chain 1 Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1 Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1 Iteration: 1100 / 2000 [ 55%] (Sampling)
#> Chain 1 Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1 Iteration: 1300 / 2000 [ 65%] (Sampling)
#> Chain 1 Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1 Iteration: 1500 / 2000 [ 75%] (Sampling)
#> Chain 1 Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1 Iteration: 1700 / 2000 [ 85%] (Sampling)
#> Chain 1 Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1 Iteration: 1900 / 2000 [ 95%] (Sampling)
#> Chain 1 Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1 finished in 0.0 seconds.
#> Chain 2 Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2 Iteration: 100 / 2000 [ 5%] (Warmup)
#> Chain 2 Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2 Iteration: 300 / 2000 [ 15%] (Warmup)
#> Chain 2 Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2 Iteration: 500 / 2000 [ 25%] (Warmup)
#> Chain 2 Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2 Iteration: 700 / 2000 [ 35%] (Warmup)
#> Chain 2 Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2 Iteration: 900 / 2000 [ 45%] (Warmup)
#> Chain 2 Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2 Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2 Iteration: 1100 / 2000 [ 55%] (Sampling)
#> Chain 2 Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2 Iteration: 1300 / 2000 [ 65%] (Sampling)
#> Chain 2 Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2 Iteration: 1500 / 2000 [ 75%] (Sampling)
#> Chain 2 Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2 Iteration: 1700 / 2000 [ 85%] (Sampling)
#> Chain 2 Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2 Iteration: 1900 / 2000 [ 95%] (Sampling)
#> Chain 2 Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2 finished in 0.0 seconds.
#> Chain 3 Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3 Iteration: 100 / 2000 [ 5%] (Warmup)
#> Chain 3 Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3 Iteration: 300 / 2000 [ 15%] (Warmup)
#> Chain 3 Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3 Iteration: 500 / 2000 [ 25%] (Warmup)
#> Chain 3 Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3 Iteration: 700 / 2000 [ 35%] (Warmup)
#> Chain 3 Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3 Iteration: 900 / 2000 [ 45%] (Warmup)
#> Chain 3 Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3 Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3 Iteration: 1100 / 2000 [ 55%] (Sampling)
#> Chain 3 Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3 Iteration: 1300 / 2000 [ 65%] (Sampling)
#> Chain 3 Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3 Iteration: 1500 / 2000 [ 75%] (Sampling)
#> Chain 3 Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3 Iteration: 1700 / 2000 [ 85%] (Sampling)
#> Chain 3 Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3 Iteration: 1900 / 2000 [ 95%] (Sampling)
#> Chain 3 Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3 finished in 0.0 seconds.
#> Chain 4 Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4 Iteration: 100 / 2000 [ 5%] (Warmup)
#> Chain 4 Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4 Iteration: 300 / 2000 [ 15%] (Warmup)
#> Chain 4 Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4 Iteration: 500 / 2000 [ 25%] (Warmup)
#> Chain 4 Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4 Iteration: 700 / 2000 [ 35%] (Warmup)
#> Chain 4 Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4 Iteration: 900 / 2000 [ 45%] (Warmup)
#> Chain 4 Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4 Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4 Iteration: 1100 / 2000 [ 55%] (Sampling)
#> Chain 4 Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4 Iteration: 1300 / 2000 [ 65%] (Sampling)
#> Chain 4 Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4 Iteration: 1500 / 2000 [ 75%] (Sampling)
#> Chain 4 Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4 Iteration: 1700 / 2000 [ 85%] (Sampling)
#> Chain 4 Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4 Iteration: 1900 / 2000 [ 95%] (Sampling)
#> Chain 4 Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4 finished in 0.0 seconds.
#>
#> All 4 chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
#> Total execution time: 1.0 seconds.
#>
# the updated fit will use a random seed unless specified, even if the
# original fit had a seed argument. Use seed again for a reproducible fit
fit_seed <- brm(rate ~ conc + state,
data = Puromycin,
seed = 1234)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 8e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.049 seconds (Warm-up)
#> Chain 1: 0.013 seconds (Sampling)
#> Chain 1: 0.062 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 3e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.051 seconds (Warm-up)
#> Chain 2: 0.011 seconds (Sampling)
#> Chain 2: 0.062 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 3e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.042 seconds (Warm-up)
#> Chain 3: 0.013 seconds (Sampling)
#> Chain 3: 0.055 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 3e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.043 seconds (Warm-up)
#> Chain 4: 0.014 seconds (Sampling)
#> Chain 4: 0.057 seconds (Total)
#> Chain 4:
update_seed <- update(fit_seed,
formula. = ~ . - state,
seed = 1234)
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 7e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.032 seconds (Warm-up)
#> Chain 1: 0.011 seconds (Sampling)
#> Chain 1: 0.043 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 3e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.038 seconds (Warm-up)
#> Chain 2: 0.011 seconds (Sampling)
#> Chain 2: 0.049 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 3e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.034 seconds (Warm-up)
#> Chain 3: 0.012 seconds (Sampling)
#> Chain 3: 0.046 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 3e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.029 seconds (Warm-up)
#> Chain 4: 0.011 seconds (Sampling)
#> Chain 4: 0.04 seconds (Total)
#> Chain 4:
# }