bnns (version 0.1.2)

summary.bnns: Summary of a Bayesian Neural Network (BNN) Model

Description

Provides a comprehensive summary of a fitted Bayesian Neural Network (BNN) model, including details about the model call, data, network architecture, posterior distributions, and model fitting information.

Usage

# S3 method for bnns
summary(object, ...)

Value

A list (returned invisibly) containing the following elements:

• "Number of observations": The number of observations in the training data.

• "Number of features": The number of features in the training data.

• "Number of hidden layers": The number of hidden layers in the neural network.

• "Nodes per layer": A comma-separated string representing the number of nodes in each hidden layer.

• "Activation functions": A comma-separated string representing the activation functions used in each hidden layer.

• "Output activation function": The activation function used in the output layer.

• "Stanfit Summary": A summary of the Stan model, including key parameter posterior distributions.

• "Iterations": The total number of iterations used for sampling in the Bayesian model.

• "Warmup": The number of iterations used as warmup in the Bayesian model.

• "Thinning": The thinning interval used in the Bayesian model.

• "Chains": The number of Markov chains used in the Bayesian model.

• "Performance": Predictive performance metrics, which vary based on the output activation function.

The function also prints the summary to the console.

Arguments

object

An object of class bnns, representing a fitted Bayesian Neural Network model.

...

Additional arguments (currently unused).

Details

The function prints the following information:

• Call: The original function call used to fit the model.

• Data Summary: Number of observations and features in the training data.

• Network Architecture: Structure of the BNN including the number of hidden layers, nodes per layer, and activation functions.

• Posterior Summary: Summarized posterior distributions of key parameters (e.g., weights, biases, and noise parameter).

• Model Fit Information: Bayesian sampling details, including the number of iterations, warmup period, thinning, and chains.

• Notes: Remarks and warnings, such as checks for convergence diagnostics.

See Also

bnns, print.bnns

Examples

# \donttest{
# Fit a Bayesian Neural Network
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
)

# Get a summary of the model
summary(model)
# }