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Update brms models based on multiple data sets

Source: R/update.R
update.brmsfit_multiple.Rd

This method allows to update an existing brmsfit_multiple object.

Usage

# S3 method for class 'brmsfit_multiple'
update(object, formula., newdata = NULL, data2 = NULL, ...)

Arguments

object

An object of class brmsfit_multiple.

formula.

Changes to the formula; for details see update.formula and brmsformula.

newdata

List of data.frames to update the model with new data. Currently required even if the original data should be used.

data2

A list of named lists each of which will be used to fit a separate model. Each of the named lists contains objects representing data which cannot be passed via argument data (see brm for examples). The length of the outer list should match the length of the list passed to the data argument. Currently required even if the original data should be used.

...

Other arguments passed to update.brmsfit and brm_multiple.

Examples

# \dontrun{
library(mice)
imp <- mice(nhanes2)
#> 
#>  iter imp variable
#>   1   1  bmi  hyp  chl
#>   1   2  bmi  hyp  chl
#>   1   3  bmi  hyp  chl
#>   1   4  bmi  hyp  chl
#>   1   5  bmi  hyp  chl
#>   2   1  bmi  hyp  chl
#>   2   2  bmi  hyp  chl
#>   2   3  bmi  hyp  chl
#>   2   4  bmi  hyp  chl
#>   2   5  bmi  hyp  chl
#>   3   1  bmi  hyp  chl
#>   3   2  bmi  hyp  chl
#>   3   3  bmi  hyp  chl
#>   3   4  bmi  hyp  chl
#>   3   5  bmi  hyp  chl
#>   4   1  bmi  hyp  chl
#>   4   2  bmi  hyp  chl
#>   4   3  bmi  hyp  chl
#>   4   4  bmi  hyp  chl
#>   4   5  bmi  hyp  chl
#>   5   1  bmi  hyp  chl
#>   5   2  bmi  hyp  chl
#>   5   3  bmi  hyp  chl
#>   5   4  bmi  hyp  chl
#>   5   5  bmi  hyp  chl

# initially fit the model
fit_imp1 <- brm_multiple(bmi ~ age + hyp + chl, data = imp, chains = 1)
#> Compiling the C++ model
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 9e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.072 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 6e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 6e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 6e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
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#> Chain 1: 
#> 
#> 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.
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#> Chain 1: 
#> 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(fit_imp1)
#>  Family: gaussian 
#>   Links: mu = identity 
#> Formula: bmi ~ age + hyp + chl 
#>    Data: imp (Number of observations: 25) 
#>   Draws: 5 chains, each with iter = 2000; warmup = 1000; thin = 1;
#>          total post-warmup draws = 5000
#> 
#> Regression Coefficients:
#>           Estimate Est.Error l-95% CI u-95% CI
#> Intercept    19.41      4.04    11.49    27.61
#> age40M59     -5.31      1.95    -9.06    -1.31
#> age60M99     -7.00      2.52   -12.01    -2.29
#> hypyes        1.82      2.80    -4.27     6.39
#> chl           0.05      0.02     0.00     0.09
#> 
#> Further Distributional Parameters:
#>       Estimate Est.Error l-95% CI u-95% CI
#> sigma     3.37      0.67     2.22     4.91
#> 
#> 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 in case of such models.

# update the model using fewer predictors
fit_imp2 <- update(fit_imp1, formula. = . ~ hyp + chl, newdata = imp)
#> Start sampling
#> the number of chains is less than 1; sampling not done
#> 
#> 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!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 7e-06 seconds
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#> Chain 1: 
#> 
#> 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.
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#> Chain 1: 
#> 
#> 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.
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#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
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#> Chain 1: 
#> 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(fit_imp2)
#>  Family: gaussian 
#>   Links: mu = identity 
#> Formula: bmi ~ hyp + chl 
#>    Data: imp (Number of observations: 25) 
#>   Draws: 5 chains, each with iter = 2000; warmup = 1000; thin = 1;
#>          total post-warmup draws = 5000
#> 
#> Regression Coefficients:
#>           Estimate Est.Error l-95% CI u-95% CI
#> Intercept    21.90      5.06    11.31    31.56
#> hypyes       -0.76      2.91    -6.10     4.99
#> chl           0.02      0.03    -0.03     0.08
#> 
#> Further Distributional Parameters:
#>       Estimate Est.Error l-95% CI u-95% CI
#> sigma     4.27      0.73     3.09     5.97
#> 
#> 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 in case of such models.

# 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_imp_seed <- brm_multiple(bmi ~ age + hyp + chl,
                             data = imp,
                             seed = 1234)
#> Compiling the C++ model
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 6e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
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#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
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#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
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#> 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

update_imp_seed <- update(fit_imp_seed,
                          formula. = . ~ hyp + chl,
                          newdata = imp,
                          seed = 1234)
#> Start sampling
#> the number of chains is less than 1; sampling not done
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 8e-06 seconds
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> 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).
#> Chain 4: 
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#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 6e-06 seconds
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#> Chain 1: 
#> 
#> 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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#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
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#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 7e-06 seconds
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
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#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
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#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 6e-06 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: 
#> Chain 3: Gradient evaluation took 4e-06 seconds
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#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
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#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 6e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
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#> 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.
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#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 3e-06 seconds
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#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 3e-06 seconds
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#> 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
# }