Displays a summary of a fitted Bayesian Neural Network (BNN) model, including the function call and the Stan fit details.
Usage
# S3 method for class 'bnns'
print(x, ...)Arguments
- x
An object of class
"bnns", typically the result of a call tobnns.default.- ...
Additional arguments (currently not used).
Value
The function is called for its side effects and does not return a value. It prints the following:
The function call used to generate the
"bnns"object.A summary of the Stan fit object stored in
x$fit.
Examples
# \donttest{
# Example usage:
data <- data.frame(x1 = runif(10), x2 = runif(10), y = rnorm(10))
model <- bnns(y ~ -1 + x1 + x2,
data = data, L = 1, nodes = 2, act_fn = 2,
iter = 1e1, warmup = 5, chains = 1
)
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: WARNING: No variance estimation is
#> Chain 1: performed for num_warmup < 20
#> Chain 1:
#> Chain 1: Iteration: 1 / 10 [ 10%] (Warmup)
#> Chain 1: Iteration: 2 / 10 [ 20%] (Warmup)
#> Chain 1: Iteration: 3 / 10 [ 30%] (Warmup)
#> Chain 1: Iteration: 4 / 10 [ 40%] (Warmup)
#> Chain 1: Iteration: 5 / 10 [ 50%] (Warmup)
#> Chain 1: Iteration: 6 / 10 [ 60%] (Sampling)
#> Chain 1: Iteration: 7 / 10 [ 70%] (Sampling)
#> Chain 1: Iteration: 8 / 10 [ 80%] (Sampling)
#> Chain 1: Iteration: 9 / 10 [ 90%] (Sampling)
#> Chain 1: Iteration: 10 / 10 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0 seconds (Warm-up)
#> Chain 1: 0 seconds (Sampling)
#> Chain 1: 0 seconds (Total)
#> Chain 1:
print(model)
#> Call:
#> bnns.default(formula = y ~ -1 + x1 + x2, data = data, L = 1,
#> nodes = 2, act_fn = 2, iter = 10, warmup = 5, chains = 1)
#>
#> Stan fit:
#> Inference for Stan model: anon_model.
#> 1 chains, each with iter=10; warmup=5; thin=1;
#> post-warmup draws per chain=5, total post-warmup draws=5.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> w1[1,1] -0.22 0.55 1.04 -1.41 -0.96 -0.26 0.51 1.01 3 0.85
#> w1[1,2] -0.42 0.54 1.01 -1.63 -1.08 -0.48 0.44 0.69 3 0.71
#> w1[2,1] 0.35 0.10 0.20 0.17 0.21 0.24 0.53 0.58 3 1.20
#> w1[2,2] -0.31 0.84 1.57 -1.69 -1.50 -0.88 0.48 1.89 3 2.17
#> b1[1] 0.44 0.90 1.69 -1.83 -0.70 1.27 1.62 1.92 3 3.01
#> b1[2] 0.24 0.60 1.12 -1.21 -0.14 0.22 0.77 1.61 3 1.50
#> w_out[1] -0.13 0.24 0.45 -0.79 -0.17 -0.02 0.04 0.31 3 1.22
#> w_out[2] 0.38 0.17 0.32 0.18 0.22 0.22 0.36 0.88 3 1.32
#> b_out 0.53 0.10 0.19 0.32 0.42 0.44 0.70 0.74 3 0.75
#> sigma 1.24 0.10 0.18 0.98 1.16 1.30 1.36 1.41 3 0.74
#> lp__ -10.33 1.20 2.25 -13.08 -11.62 -10.14 -9.35 -7.51 3 0.72
#>
#> Samples were drawn using NUTS(diag_e) at Tue Jan 14 03:41:18 2025.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
# }