Compute posterior predictions of smooth s and t2 terms of
models fitted with brms.
Usage
# S3 method for class 'brmsfit'
posterior_smooths(
object,
smooth,
newdata = NULL,
resp = NULL,
dpar = NULL,
nlpar = NULL,
ndraws = NULL,
draw_ids = NULL,
...
)
posterior_smooths(object, ...)Arguments
- object
An object of class
brmsfit.- smooth
Name of a single smooth term for which predictions should be computed.
- newdata
An optional
data.framefor which to evaluate predictions. IfNULL(default), the original data of the model is used. Only those variables appearing in the chosensmoothterm are required.- resp
Optional names of response variables. If specified, predictions are performed only for the specified response variables.
- dpar
Optional name of a predicted distributional parameter. If specified, expected predictions of this parameters are returned.
- nlpar
Optional name of a predicted non-linear parameter. If specified, expected predictions of this parameters are returned.
- ndraws
Positive integer indicating how many posterior draws should be used. If
NULL(the default) all draws are used. Ignored ifdraw_idsis notNULL.- draw_ids
An integer vector specifying the posterior draws to be used. If
NULL(the default), all draws are used.- ...
Currently ignored.
Value
An S x N matrix, where S is the number of posterior draws and N is the number of observations.
Examples
# \dontrun{
set.seed(0)
dat <- mgcv::gamSim(1, n = 200, scale = 2)
#> Gu & Wahba 4 term additive model
fit <- brm(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 4.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.42 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)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 4.981 seconds (Warm-up)
#> Chain 1: 4.056 seconds (Sampling)
#> Chain 1: 9.037 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 3.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.35 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)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 4.999 seconds (Warm-up)
#> Chain 2: 4.064 seconds (Sampling)
#> Chain 2: 9.063 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 3.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.35 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
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#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 5.248 seconds (Warm-up)
#> Chain 3: 4.011 seconds (Sampling)
#> Chain 3: 9.259 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 3.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.33 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
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#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
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#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 4.525 seconds (Warm-up)
#> Chain 4: 4.061 seconds (Sampling)
#> Chain 4: 8.586 seconds (Total)
#> Chain 4:
#> Warning: There were 2 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
summary(fit)
#> Warning: There were 2 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> Family: gaussian
#> Links: mu = identity
#> Formula: y ~ s(x0) + s(x1) + s(x2) + s(x3)
#> Data: dat (Number of observations: 200)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Smoothing Spline Hyperparameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sds(sx0_1) 2.84 1.53 0.88 6.49 1.00 2639 2533
#> sds(sx1_1) 2.76 1.72 0.59 7.06 1.00 2048 1573
#> sds(sx2_1) 21.86 6.20 12.82 37.42 1.00 1571 2206
#> sds(sx3_1) 1.44 1.28 0.06 4.86 1.00 1689 1931
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 7.32 0.16 6.99 7.63 1.00 7382 2765
#> sx0_1 -1.54 5.78 -13.97 9.99 1.00 2963 2420
#> sx1_1 13.24 5.73 2.37 25.57 1.00 2741 2388
#> sx2_1 33.91 18.50 -4.01 67.94 1.00 2742 2438
#> sx3_1 0.59 4.02 -7.85 9.65 1.00 2513 1803
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma 2.18 0.12 1.96 2.42 1.00 6516 3268
#>
#> 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).
newdata <- data.frame(x2 = seq(0, 1, 10))
str(posterior_smooths(fit, smooth = "s(x2)", newdata = newdata))
#> num [1:4000, 1] -5.13 -5.13 -3.83 -5.38 -4.71 ...
# }