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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 1.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.067 seconds (Warm-up)
#> Chain 1:                0.019 seconds (Sampling)
#> Chain 1:                0.086 seconds (Total)
#> 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.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.06 seconds (Warm-up)
#> Chain 1:                0.017 seconds (Sampling)
#> Chain 1:                0.077 seconds (Total)
#> 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.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.06 seconds (Warm-up)
#> Chain 1:                0.015 seconds (Sampling)
#> Chain 1:                0.075 seconds (Total)
#> 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.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.058 seconds (Warm-up)
#> Chain 1:                0.021 seconds (Sampling)
#> Chain 1:                0.079 seconds (Total)
#> 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.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.066 seconds (Warm-up)
#> Chain 1:                0.016 seconds (Sampling)
#> Chain 1:                0.082 seconds (Total)
#> 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    17.95      3.19    11.78    24.43
#> age40M59     -4.84      1.98    -8.75    -0.90
#> age60M99     -7.23      2.19   -11.31    -2.86
#> hypyes        2.33      2.40    -2.15     7.02
#> chl           0.06      0.02     0.02     0.09
#> 
#> Further Distributional Parameters:
#>       Estimate Est.Error l-95% CI u-95% CI
#> sigma     2.99      0.58     2.07     4.29
#> 
#> 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 6e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.029 seconds (Warm-up)
#> Chain 1:                0.013 seconds (Sampling)
#> Chain 1:                0.042 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!
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.032 seconds (Warm-up)
#> Chain 1:                0.013 seconds (Sampling)
#> Chain 1:                0.045 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!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.027 seconds (Warm-up)
#> Chain 1:                0.012 seconds (Sampling)
#> Chain 1:                0.039 seconds (Total)
#> 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.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.028 seconds (Warm-up)
#> Chain 1:                0.012 seconds (Sampling)
#> Chain 1:                0.04 seconds (Total)
#> 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.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> 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:                0.013 seconds (Sampling)
#> Chain 1:                0.045 seconds (Total)
#> 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    20.63      4.15    12.34    28.65
#> hypyes       -0.24      2.53    -5.27     4.49
#> chl           0.03      0.02    -0.01     0.08
#> 
#> Further Distributional Parameters:
#>       Estimate Est.Error l-95% CI u-95% CI
#> sigma     3.92      0.60     2.95     5.25
#> 
#> 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.
# }