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Summarize Neural Network of class citodnn

Source: R/dnn.R
summary.citodnn.Rd

Performs a Feature Importance calculation based on Permutations

Usage

# S3 method for citodnn
summary(object, n_permute = NULL, device = NULL, ...)

# S3 method for citodnnBootstrap
summary(object, n_permute = NULL, device = NULL, adjust_se = FALSE, ...)

Arguments

object

a model of class citodnn created by dnn

n_permute

number of permutations performed. Default is \(3 * \sqrt{n}\), where n euqals then number of samples in the training set

device

for calculating variable importance and conditional effects

...

additional arguments

adjust_se

adjust standard errors for importance (standard errors are multiplied with 1/sqrt(3) )

Value

summary.citodnn returns an object of class "summary.citodnn", a list with components

Details

Performs the feature importance calculation as suggested by Fisher, Rudin, and Dominici (2018), and the mean and standard deviation of the average conditional Effects as suggested by Pichler & Hartig (2023).

Feature importances are in their interpretation similar to a ANOVA. Main and interaction effects are absorbed into the features. Also, feature importances are prone to collinearity between features, i.e. if two features are collinear, the importances might be overestimated.

Average conditional effects (ACE) are similar to marginal effects and approximate linear effects, i.e. their interpretation is similar to effects in a linear regression model.

The standard deviation of the ACE informs about the non-linearity of the feature effects. Higher values correlate with stronger non-linearities.

For each feature n permutation get done and original and permuted predictive mean squared error (\(e_{perm}\) & \(e_{orig}\)) get evaluated with \( FI_j= e_{perm}/e_{orig}\). Based on Mean Squared Error.