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Posterior predictive draws averaged across models

Source: R/model_weights.R
pp_average.brmsfit.Rd

Compute posterior predictive draws averaged across models. Weighting can be done in various ways, for instance using Akaike weights based on information criteria or marginal likelihoods.

Usage

# S3 method for class 'brmsfit'
pp_average(
  x,
  ...,
  weights = "stacking",
  method = "posterior_predict",
  ndraws = NULL,
  nsamples = NULL,
  summary = TRUE,
  probs = c(0.025, 0.975),
  robust = FALSE,
  model_names = NULL,
  control = list(),
  seed = NULL
)

pp_average(x, ...)

Arguments

x

A brmsfit object.

...

More brmsfit objects or further arguments passed to the underlying post-processing functions. In particular, see prepare_predictions for further supported arguments.

weights

Name of the criterion to compute weights from. Should be one of "loo", "waic", "kfold", "stacking" (current default), "bma", or "pseudobma". For the former three options, Akaike weights will be computed based on the information criterion values returned by the respective methods. For "stacking" and "pseudobma", method loo_model_weights will be used to obtain weights. For "bma", method post_prob will be used to compute Bayesian model averaging weights based on log marginal likelihood values (make sure to specify reasonable priors in this case). For some methods, weights may also be a numeric vector of pre-specified weights.

method

Method used to obtain predictions to average over. Should be one of "posterior_predict" (default), "posterior_epred", "posterior_linpred" or "predictive_error".

ndraws

Total number of posterior draws to use.

nsamples

Deprecated alias of ndraws.

summary

Should summary statistics (i.e. means, sds, and 95% intervals) be returned instead of the raw values? Default is TRUE.

probs

The percentiles to be computed by the quantile function. Only used if summary is TRUE.

robust

If FALSE (the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If TRUE, the median and the median absolute deviation (MAD) are applied instead. Only used if summary is TRUE.

model_names

If NULL (the default) will use model names derived from deparsing the call. Otherwise will use the passed values as model names.

control

Optional list of further arguments passed to the function specified in weights.

seed

A single numeric value passed to set.seed to make results reproducible.

Value

Same as the output of the method specified in argument method.

Details

Weights are computed with the model_weights method.

See also

model_weights, posterior_average

Examples

# \dontrun{
# model with 'treat' as predictor
fit1 <- brm(rating ~ treat + period + carry, data = inhaler)
#> Compiling Stan program...
#> 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: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.022 seconds (Warm-up)
#> Chain 1:                0.02 seconds (Sampling)
#> Chain 1:                0.042 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 4e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.021 seconds (Warm-up)
#> Chain 2:                0.02 seconds (Sampling)
#> Chain 2:                0.041 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!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.022 seconds (Warm-up)
#> Chain 3:                0.02 seconds (Sampling)
#> Chain 3:                0.042 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 6e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.042 seconds (Total)
#> Chain 4: 
summary(fit1)
#>  Family: gaussian 
#>   Links: mu = identity 
#> Formula: rating ~ treat + period + carry 
#>    Data: inhaler (Number of observations: 572) 
#>   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     1.43      0.03     1.38     1.48 1.00     4589     2877
#> treat        -0.25      0.07    -0.39    -0.11 1.00     3616     2925
#> period        0.05      0.05    -0.05     0.15 1.00     4587     2860
#> carry        -0.04      0.05    -0.15     0.06 1.00     3538     3239
#> 
#> Further Distributional Parameters:
#>       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma     0.61      0.02     0.57     0.65 1.00     5194     2878
#> 
#> 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).

# model without 'treat' as predictor
fit2 <- brm(rating ~ period + carry, data = inhaler)
#> Compiling Stan program...
#> 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)
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.016 seconds (Warm-up)
#> Chain 1:                0.016 seconds (Sampling)
#> Chain 1:                0.032 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!
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.015 seconds (Warm-up)
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#> Chain 2:                0.027 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!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.016 seconds (Warm-up)
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#> Chain 3:                0.03 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.03 seconds (Total)
#> Chain 4: 
summary(fit2)
#>  Family: gaussian 
#>   Links: mu = identity 
#> Formula: rating ~ period + carry 
#>    Data: inhaler (Number of observations: 572) 
#>   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     1.43      0.02     1.38     1.48 1.00     4023     2800
#> period        0.05      0.05    -0.05     0.15 1.00     4763     3007
#> carry        -0.17      0.04    -0.24    -0.10 1.00     4795     3321
#> 
#> Further Distributional Parameters:
#>       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma     0.61      0.02     0.58     0.65 1.00     4074     2465
#> 
#> 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).

# compute model-averaged predicted values
(df <- unique(inhaler[, c("treat", "period", "carry")]))
#>     treat period carry
#> 1     0.5    0.5     0
#> 60   -0.5    0.5     0
#> 287  -0.5   -0.5    -1
#> 346   0.5   -0.5     1
pp_average(fit1, fit2, newdata = df)
#>      Estimate Est.Error       Q2.5    Q97.5
#> [1,] 1.355118 0.6052531 0.17856944 2.536315
#> [2,] 1.575359 0.5989236 0.42295301 2.744175
#> [3,] 1.596402 0.6044013 0.42720091 2.770042
#> [4,] 1.240386 0.6170943 0.01388588 2.432680
#> attr(,"weights")
#>      fit1      fit2 
#> 0.8516506 0.1483494 
#> attr(,"ndraws")
#> fit1 fit2 
#> 3407  593 

# compute model-averaged fitted values
pp_average(fit1, fit2, method = "fitted", newdata = df)
#>      Estimate  Est.Error     Q2.5    Q97.5
#> [1,] 1.349206 0.06765766 1.229548 1.491723
#> [2,] 1.564406 0.06526306 1.427076 1.677502
#> [3,] 1.577877 0.05157725 1.475464 1.677291
#> [4,] 1.235182 0.05214174 1.132670 1.335806
#> attr(,"weights")
#>      fit1      fit2 
#> 0.8516506 0.1483494 
#> attr(,"ndraws")
#> fit1 fit2 
#> 3407  593 
# }