brnn r package (Bayesian Regularization for Feed-Forward Neural Networks)

Question

[elisa.micarelli]

Hi,

I'm using the "brnn" package (Bayesian Regularized Neural Networks), in particular I run the train() function from caret package. My data are stored in a data.frame object and I run the caret function using the "formula" way y ~. (in the "point" there are seven variables that we can call here x1,x2,x3....x7) I would like to understand how to write the formula of brnn method using the values obtained from R. I obtained the following results.

• MY_results$Model$finalModel$theta
• MY_results$Model$finalModel$alpha
• MY_results$Model$finalModel$beta
• MY_results$Model$finalModel$gamma
• MY_results$Model$finalModel$Ed
• MY_results$Model$finalModel$Ew
• MY_results$Model$finalModel$F_history
• MY_results$Model$finalModel$reason
• MY_results$Model$finalModel$epoch
• MY_results$Model$finalModel$neurons
• MY_results$Model$finalModel$p
• MY_results$Model$finalModel$n
• MY_results$Model$finalModel$npar
• MY_results$Model$finalModel$x_normalized
• MY_results$Model$finalModel$x_base
• MY_results$Model$finalModel$x_spread
• MY_results$Model$finalModel$y_base
• MY_results$Model$finalModel$y_spread
• MY_results$Model$finalModel$y
• MY_results$Model$finalModel$normalize
• MY_results$Model$finalModel$call
• MY_results$Model$finalModel$xNames
• MY_results$Model$finalModel$problemType
• MY_results$Model$finalModel$tuneValue
• MY_results$Model$finalModel$obsLevels
• MY_results$Model$finalModel$param

I tried to reproduce the brnn formula described in https://cran.r-project.org/web/packages/brnn/brnn.pdf, but I'm not sure how to consider variables x1,x2....x7.

Thank you in advance for any kind of suggestions.

Elisa