inst/examples/conditionalEffects-example.R

In cito: Building and Training Neural Networks

\donttest{
if(torch::torch_is_installed()){
library(cito)

# Build and train  Network
nn.fit = dnn(Sepal.Length~., data = datasets::iris)

# Calculate average conditional effects
ACE = conditionalEffects(nn.fit)

## Main effects (categorical features are not supported)
ACE

## With interaction effects:
ACE = conditionalEffects(nn.fit, interactions = TRUE)
## The off diagonal elements are the interaction effects
ACE[[1]]$mean
## ACE is a list, elements correspond to the number of response classes
## Sepal.length == 1 Response so we have only one
## list element in the ACE object

# Re-train NN with bootstrapping to obtain standard errors
nn.fit = dnn(Sepal.Length~., data = datasets::iris, bootstrap = 30L)
## The summary method calculates also the conditional effects, and if
## bootstrapping was used, it will also report standard errors and p-values:
summary(nn.fit)


}
}