Dear list members.
I am trying to make a sensitivity analysis (as derived by Zurada 1994,
Engelbrecht 1995) of input parameters (gene expression data) when
applying a neural network to classify different cancer subtypes. Since
I am no expert in the field, (rather a newbie), I wonder if there
exists an implementation in R that can be used to measure the relative
importance of the input variables for the neural network. The created
network has (after PCA dimension reduction) 8 inputs, 2 nodes in the
hidden layer and two outputs.
I read somewhere that the sensitivity matrix is calculated by using a
Jacobian matrix of the output parameters over the input parameters. I
think that somehow I can grasp the concept, I just don’t know how to
implement it in R. Any help, or guidelines would be greatly appreciated.
Best regards
Marcus
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
