For models fit using MCMC (algorithm="sampling") or one of the
variational approximations ("meanfield" or "fullrank"), the
predictive_interval function computes Bayesian predictive intervals.
The method for stanreg objects calls posterior_predict
internally, whereas the method for objects of class "ppd" accepts the
matrix returned by posterior_predict as input and can be used to avoid
multiple calls to posterior_predict.
# S3 method for stanreg predictive_interval( object, prob = 0.9, newdata = NULL, draws = NULL, re.form = NULL, fun = NULL, seed = NULL, offset = NULL, ... ) # S3 method for ppd predictive_interval(object, prob = 0.9, ...)
| object | Either a fitted model object returned by one of the
rstanarm modeling functions (a stanreg
object) or, for the |
|---|---|
| prob | A number \(p \in (0,1)\) indicating the desired
probability mass to include in the intervals. The default is to report
\(90\)% intervals ( |
| newdata, draws, fun, offset, re.form, seed | Passed to
|
| ... | Currently ignored. |
A matrix with two columns and as many rows as are in newdata.
If newdata is not provided then the matrix will have as many rows as
the data used to fit the model. For a given value of prob, \(p\),
the columns correspond to the lower and upper \(100p\)% central interval
limits and have the names \(100\alpha/2\)% and \(100(1 -
\alpha/2)\)%, where \(\alpha = 1-p\). For example, if prob=0.9 is
specified (a \(90\)% interval), then the column names will be
"5%" and "95%", respectively.
#> #> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1). #> Chain 1: #> Chain 1: Gradient evaluation took 2e-05 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1: #> Chain 1: #> Chain 1: Iteration: 1 / 300 [ 0%] (Warmup) #> Chain 1: Iteration: 30 / 300 [ 10%] (Warmup) #> Chain 1: Iteration: 60 / 300 [ 20%] (Warmup) #> Chain 1: Iteration: 90 / 300 [ 30%] (Warmup) #> Chain 1: Iteration: 120 / 300 [ 40%] (Warmup) #> Chain 1: Iteration: 150 / 300 [ 50%] (Warmup) #> Chain 1: Iteration: 151 / 300 [ 50%] (Sampling) #> Chain 1: Iteration: 180 / 300 [ 60%] (Sampling) #> Chain 1: Iteration: 210 / 300 [ 70%] (Sampling) #> Chain 1: Iteration: 240 / 300 [ 80%] (Sampling) #> Chain 1: Iteration: 270 / 300 [ 90%] (Sampling) #> Chain 1: Iteration: 300 / 300 [100%] (Sampling) #> Chain 1: #> Chain 1: Elapsed Time: 0.012552 seconds (Warm-up) #> Chain 1: 0.00663 seconds (Sampling) #> Chain 1: 0.019182 seconds (Total) #> Chain 1: #> #> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2). #> Chain 2: #> Chain 2: Gradient evaluation took 9e-06 seconds #> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds. #> Chain 2: Adjust your expectations accordingly! #> Chain 2: #> Chain 2: #> Chain 2: Iteration: 1 / 300 [ 0%] (Warmup) #> Chain 2: Iteration: 30 / 300 [ 10%] (Warmup) #> Chain 2: Iteration: 60 / 300 [ 20%] (Warmup) #> Chain 2: Iteration: 90 / 300 [ 30%] (Warmup) #> Chain 2: Iteration: 120 / 300 [ 40%] (Warmup) #> Chain 2: Iteration: 150 / 300 [ 50%] (Warmup) #> Chain 2: Iteration: 151 / 300 [ 50%] (Sampling) #> Chain 2: Iteration: 180 / 300 [ 60%] (Sampling) #> Chain 2: Iteration: 210 / 300 [ 70%] (Sampling) #> Chain 2: Iteration: 240 / 300 [ 80%] (Sampling) #> Chain 2: Iteration: 270 / 300 [ 90%] (Sampling) #> Chain 2: Iteration: 300 / 300 [100%] (Sampling) #> Chain 2: #> Chain 2: Elapsed Time: 0.011152 seconds (Warm-up) #> Chain 2: 0.004945 seconds (Sampling) #> Chain 2: 0.016097 seconds (Total) #> Chain 2: #> #> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3). #> Chain 3: #> Chain 3: Gradient evaluation took 9e-06 seconds #> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds. #> Chain 3: Adjust your expectations accordingly! #> Chain 3: #> Chain 3: #> Chain 3: Iteration: 1 / 300 [ 0%] (Warmup) #> Chain 3: Iteration: 30 / 300 [ 10%] (Warmup) #> Chain 3: Iteration: 60 / 300 [ 20%] (Warmup) #> Chain 3: Iteration: 90 / 300 [ 30%] (Warmup) #> Chain 3: Iteration: 120 / 300 [ 40%] (Warmup) #> Chain 3: Iteration: 150 / 300 [ 50%] (Warmup) #> Chain 3: Iteration: 151 / 300 [ 50%] (Sampling) #> Chain 3: Iteration: 180 / 300 [ 60%] (Sampling) #> Chain 3: Iteration: 210 / 300 [ 70%] (Sampling) #> Chain 3: Iteration: 240 / 300 [ 80%] (Sampling) #> Chain 3: Iteration: 270 / 300 [ 90%] (Sampling) #> Chain 3: Iteration: 300 / 300 [100%] (Sampling) #> Chain 3: #> Chain 3: Elapsed Time: 0.011701 seconds (Warm-up) #> Chain 3: 0.007061 seconds (Sampling) #> Chain 3: 0.018762 seconds (Total) #> Chain 3: #> #> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4). #> Chain 4: #> Chain 4: Gradient evaluation took 1.1e-05 seconds #> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds. #> Chain 4: Adjust your expectations accordingly! #> Chain 4: #> Chain 4: #> Chain 4: Iteration: 1 / 300 [ 0%] (Warmup) #> Chain 4: Iteration: 30 / 300 [ 10%] (Warmup) #> Chain 4: Iteration: 60 / 300 [ 20%] (Warmup) #> Chain 4: Iteration: 90 / 300 [ 30%] (Warmup) #> Chain 4: Iteration: 120 / 300 [ 40%] (Warmup) #> Chain 4: Iteration: 150 / 300 [ 50%] (Warmup) #> Chain 4: Iteration: 151 / 300 [ 50%] (Sampling) #> Chain 4: Iteration: 180 / 300 [ 60%] (Sampling) #> Chain 4: Iteration: 210 / 300 [ 70%] (Sampling) #> Chain 4: Iteration: 240 / 300 [ 80%] (Sampling) #> Chain 4: Iteration: 270 / 300 [ 90%] (Sampling) #> Chain 4: Iteration: 300 / 300 [100%] (Sampling) #> Chain 4: #> Chain 4: Elapsed Time: 0.015004 seconds (Warm-up) #> Chain 4: 0.006421 seconds (Sampling) #> Chain 4: 0.021425 seconds (Total) #> Chain 4:#> 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 #> http://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 #> http://mc-stan.org/misc/warnings.html#tail-esspredictive_interval(fit)#> 5% 95% #> Mazda RX4 18.005730 28.73362 #> Mazda RX4 Wag 16.686794 27.18966 #> Datsun 710 19.378961 30.52869 #> Hornet 4 Drive 14.519490 25.10673 #> Hornet Sportabout 13.858227 23.77673 #> Valiant 13.570513 23.81552 #> Duster 360 13.430621 23.73982 #> Merc 240D 15.234610 25.43294 #> Merc 230 14.983108 26.00783 #> Merc 280 13.603792 24.16747 #> Merc 280C 13.801138 24.17822 #> Merc 450SE 10.235224 21.25631 #> Merc 450SL 11.855471 22.57667 #> Merc 450SLC 11.840450 22.22804 #> Cadillac Fleetwood 3.719861 14.98026 #> Lincoln Continental 2.676172 13.98966 #> Chrysler Imperial 2.946626 14.44062 #> Fiat 128 20.187047 31.33859 #> Honda Civic 23.376361 33.82195 #> Toyota Corolla 22.403555 32.95855 #> Toyota Corona 19.140440 29.59045 #> Dodge Challenger 13.174725 23.31733 #> AMC Javelin 14.185602 24.37029 #> Camaro Z28 11.334363 22.38741 #> Pontiac Firebird 11.885473 22.05875 #> Fiat X1-9 21.806661 32.30304 #> Porsche 914-2 20.879908 31.00981 #> Lotus Europa 24.027026 34.48600 #> Ford Pantera L 15.035939 25.99151 #> Ferrari Dino 17.493230 28.27952 #> Maserati Bora 12.657825 23.44672 #> Volvo 142E 17.245372 27.26530#> 25% 75% #> 1 26.876641 31.28335 #> 2 6.258314 10.66288# stanreg vs ppd methods preds <- posterior_predict(fit, seed = 123) all.equal( predictive_interval(fit, seed = 123), predictive_interval(preds) )#> [1] TRUE