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Posterior Predictive Checks for brmsfit Objects

Source: R/pp_check.R
pp_check.brmsfit.Rd

Perform posterior predictive checks with the help of the bayesplot package.

Usage

# S3 method for class 'brmsfit'
pp_check(
  object,
  type,
  ndraws = NULL,
  prefix = c("ppc", "ppd"),
  group = NULL,
  x = NULL,
  newdata = NULL,
  resp = NULL,
  draw_ids = NULL,
  nsamples = NULL,
  subset = NULL,
  ...
)

Arguments

object

An object of class brmsfit.

type

Type of the ppc plot as given by a character string. See PPC for an overview of currently supported types. You may also use an invalid type (e.g. type = "xyz") to get a list of supported types in the resulting error message.

ndraws

Positive integer indicating how many posterior draws should be used. If NULL all draws are used. If not specified, the number of posterior draws is chosen automatically. Ignored if draw_ids is not NULL.

prefix

The prefix of the bayesplot function to be applied. Either `"ppc"` (posterior predictive check; the default) or `"ppd"` (posterior predictive distribution), the latter being the same as the former except that the observed data is not shown for `"ppd"`.

group

Optional name of a factor variable in the model by which to stratify the ppc plot. This argument is required for ppc *_grouped types and ignored otherwise.

x

Optional name of a variable in the model. Only used for ppc types having an x argument and ignored otherwise.

newdata

An optional data.frame for which to evaluate predictions. If NULL (default), the original data of the model is used. NA values within factors (excluding grouping variables) are interpreted as if all dummy variables of this factor are zero. This allows, for instance, to make predictions of the grand mean when using sum coding. NA values within grouping variables are treated as a new level.

resp

Optional names of response variables. If specified, predictions are performed only for the specified response variables.

draw_ids

An integer vector specifying the posterior draws to be used. If NULL (the default), all draws are used.

nsamples

Deprecated alias of ndraws.

subset

Deprecated alias of draw_ids.

...

Further arguments passed to predict.brmsfit as well as to the PPC function specified in type.

Value

A ggplot object that can be further customized using the ggplot2 package.

Details

For a detailed explanation of each of the ppc functions, see the PPC documentation of the bayesplot package.

Examples

# \dontrun{
fit <-  brm(count ~ zAge + zBase * Trt
            + (1|patient) + (1|obs),
            data = epilepsy, family = poisson())
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 4.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.48 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: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 4.517 seconds (Warm-up)
#> Chain 1:                2.438 seconds (Sampling)
#> Chain 1:                6.955 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 4.8e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.48 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 4.477 seconds (Warm-up)
#> Chain 2:                2.475 seconds (Sampling)
#> Chain 2:                6.952 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 4.1e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 4.512 seconds (Warm-up)
#> Chain 3:                2.462 seconds (Sampling)
#> Chain 3:                6.974 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 3.9e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.39 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 4.134 seconds (Warm-up)
#> Chain 4:                2.435 seconds (Sampling)
#> Chain 4:                6.569 seconds (Total)
#> Chain 4: 

pp_check(fit)  # shows dens_overlay plot by default
#> Using 10 posterior draws for ppc type 'dens_overlay' by default.

pp_check(fit, type = "error_hist", ndraws = 11)
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

pp_check(fit, type = "scatter_avg", ndraws = 100)

pp_check(fit, type = "stat_2d")
#> Using all posterior draws for ppc type 'stat_2d' by default.
#> Note: in most cases the default test statistic 'mean' is too weak to detect anything of interest.

pp_check(fit, type = "rootogram")
#> Using all posterior draws for ppc type 'rootogram' by default.

pp_check(fit, type = "loo_pit")
#> Error in pp_check.brmsfit(fit, type = "loo_pit"): Type 'loo_pit' is not a valid ppc type. Valid types are:
#> 'bars', 'bars_grouped', 'boxplot', 'dens', 'dens_overlay', 'dens_overlay_grouped', 'dots', 'ecdf_overlay', 'ecdf_overlay_grouped', 'error_binned', 'error_hist', 'error_hist_grouped', 'error_scatter', 'error_scatter_avg', 'error_scatter_avg_grouped', 'error_scatter_avg_vs_x', 'freqpoly', 'freqpoly_grouped', 'hist', 'intervals', 'intervals_grouped', 'km_overlay', 'km_overlay_grouped', 'loo_intervals', 'loo_pit_ecdf', 'loo_pit_overlay', 'loo_pit_qq', 'loo_ribbon', 'pit_ecdf', 'pit_ecdf_grouped', 'ribbon', 'ribbon_grouped', 'rootogram', 'scatter', 'scatter_avg', 'scatter_avg_grouped', 'stat', 'stat_2d', 'stat_freqpoly', 'stat_freqpoly_grouped', 'stat_grouped', 'violin_grouped'

## get an overview of all valid types
pp_check(fit, type = "xyz")
#> Error in pp_check.brmsfit(fit, type = "xyz"): Type 'xyz' is not a valid ppc type. Valid types are:
#> 'bars', 'bars_grouped', 'boxplot', 'dens', 'dens_overlay', 'dens_overlay_grouped', 'dots', 'ecdf_overlay', 'ecdf_overlay_grouped', 'error_binned', 'error_hist', 'error_hist_grouped', 'error_scatter', 'error_scatter_avg', 'error_scatter_avg_grouped', 'error_scatter_avg_vs_x', 'freqpoly', 'freqpoly_grouped', 'hist', 'intervals', 'intervals_grouped', 'km_overlay', 'km_overlay_grouped', 'loo_intervals', 'loo_pit_ecdf', 'loo_pit_overlay', 'loo_pit_qq', 'loo_ribbon', 'pit_ecdf', 'pit_ecdf_grouped', 'ribbon', 'ribbon_grouped', 'rootogram', 'scatter', 'scatter_avg', 'scatter_avg_grouped', 'stat', 'stat_2d', 'stat_freqpoly', 'stat_freqpoly_grouped', 'stat_grouped', 'violin_grouped'

## get a plot without the observed data
pp_check(fit, prefix = "ppd")
#> Using 10 posterior draws for ppc type 'dens_overlay' by default.

# }