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
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
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#> Chain 4:
#> Warning: There were 1520 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 2000 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 1 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 18.67, 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 1520 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) 2.89 1.25 1.59 4.78 18.66 4 NA
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept -8.50 17.24 -31.40 9.83 18.66 4 4
#> age -0.46 0.53 -1.06 0.25 18.66 4 NA
#> sexfemale 0.71 0.46 0.27 1.38 18.66 4 NA
#> diseaseGN -0.19 0.77 -1.24 0.77 6.20 4 NA
#> diseaseAN 0.17 0.30 -0.22 0.47 16.34 4 4
#> diseasePKD 0.06 0.91 -0.84 1.58 12.51 4 NA
#> age:sexfemale 0.84 0.23 0.57 1.20 18.66 4 NA
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 3.37 0.91 1.86 4.24 6.21 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).
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#> Chain 1:
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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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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
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#> Chain 4:
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
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#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Warning: There were 2328 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 4.7, 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 2328 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) 0.74 0.43 0.15 1.26 2.49 5 11
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept -3.83 11.95 -22.14 4.64 2.67 5 4
#> age -0.11 0.28 -0.38 0.24 2.99 5 11
#> sexfemale 1.81 6.04 -1.71 14.61 2.88 5 4
#> age:sexfemale 0.27 0.23 -0.22 0.46 3.90 4 NA
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 30.44 42.79 4.74 125.00 4.02 4 4
#>
#> 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!
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 8e-06 seconds
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#> Chain 2: Adjust your expectations accordingly!
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 8e-06 seconds
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#> Chain 4:
#> Warning: There were 3026 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: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 2.13, 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 -617.18 691.42 -2270.84 19.05 1.77 6 31
#> age 5.74 14.71 -34.24 34.20 2.14 5 11
#> sexfemale 622.74 691.39 -13.48 2276.45 1.77 6 31
#> diseaseGN -34.56 90.14 -371.29 0.25 1.25 12 13
#> diseaseAN -295.00 373.57 -1412.84 -1.94 1.61 7 19
#> diseasePKD -1115.12 1502.26 -5261.27 -1.87 1.74 6 11
#> age:sexfemale -5.75 14.71 -34.20 34.22 2.14 5 11
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 125.35 16.93 97.94 161.80 1.08 47 434
#>
#> 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 3.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.32 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
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#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
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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) 0.51 0.25 0.04 1.00 1.01 605 996
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 2.87 0.92 1.15 4.76 1.00 2320 2260
#> age 0.02 0.02 -0.03 0.06 1.00 1981 2187
#> sexfemale 2.79 1.06 0.68 4.88 1.00 2239 2248
#> diseaseGN -0.27 0.48 -1.22 0.67 1.00 2609 2816
#> diseaseAN -0.58 0.47 -1.52 0.34 1.00 2378 2355
#> diseasePKD 0.77 0.73 -0.69 2.25 1.00 2056 2652
#> age:sexfemale -0.03 0.02 -0.08 0.02 1.00 2126 1978
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> shape 1.12 0.16 0.85 1.46 1.00 1092 2086
#>
#> 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 1e-05 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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#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
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update_seed <- update(fit_seed,
formula. = ~ . - state,
seed = 1234)
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
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# }