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:
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#> 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!
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.059 seconds (Warm-up)
#> Chain 1: 0.03 seconds (Sampling)
#> Chain 1: 0.089 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 1.8e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.104 seconds (Warm-up)
#> Chain 2: 0.148 seconds (Sampling)
#> Chain 2: 0.252 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3:
#> Chain 3: Elapsed Time: 0.117 seconds (Warm-up)
#> Chain 3: 0.085 seconds (Sampling)
#> Chain 3: 0.202 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.
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#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4:
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
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#> Chain 4:
#> Chain 4: Elapsed Time: 0.14 seconds (Warm-up)
#> Chain 4: 0.052 seconds (Sampling)
#> Chain 4: 0.192 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:
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#> Chain 1:
#> Chain 1: Gradient evaluation took 2.4e-05 seconds
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#> Chain 1: Adjust your expectations accordingly!
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
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#> Chain 2:
#> Chain 2: Gradient evaluation took 1.9e-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).
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#> Chain 3: Stan can't start sampling from this initial value.
#> Chain 3:
#> Chain 3: Gradient evaluation took 1.7e-05 seconds
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#> 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.
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#> Chain 4: Stan can't start sampling from this initial value.
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#> Chain 4: Stan can't start sampling from this initial value.
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#> Chain 4: Stan can't start sampling from this initial value.
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#> Chain 4: Stan can't start sampling from this initial value.
#> Chain 4:
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
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#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
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#> Chain 4: 0.024 seconds (Sampling)
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#> 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 1.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 3.751 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.1 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 8e-06 seconds
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#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 9e-06 seconds
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#> Chain 4: Adjust your expectations accordingly!
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#> 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 3.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.31 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 1.7e-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:
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#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
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#> Chain 4: Adjust your expectations accordingly!
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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...
#>
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#>
# 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 1.1e-05 seconds
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
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#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 2e-06 seconds
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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
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#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
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#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
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# }