Nothing
\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)
}
}
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