| Library: | nn |
| See also: | nnrnet ann nnrpredict2 |
| Quantlet: | nnrpredict | |
| Description: | estimates the response for a given net and a dataset |
| Usage: | yh = nnrpredict(x, net) | |
| Input: | ||
| x | n x p matrix, input variables | |
| net | list, composed object from nnrnet | |
| Output: | ||
| yh | list, composed from yh.result and yh.hess | |
| yh.result | n x q matrix, result variables | |
| yh.hess | matrix, the hessian matrix | |
library("nn")
x = read("kredit")
t = read("tkredit")
randomize(55)
y = x[,1]
x = x[,2:21]
x =(x-min(x))./(max(x)-min(x))
net = nnrnet(x, y, matrix(rows(x)), 10)
yh = nnrpredict(x, net)
yh.result
runs a neural network with 10 hidden units for the kredit data of Fahrmeier and Hammerle and computes the predicted values. Contents of ts [1,] "A 20 - 10 - 1 network:" [2,] "# weights : 221" [3,] "linear output : no" [4,] "error function: least squares" [5,] "log prob model: no" [6,] "skip links : no" [7,] "max. weight : 0.70" [8,] "decay : 0" [9,] "max. Iterat : 100" Contents of result [ 1,] 0.71751 [ 2,] 1 [ 3,] 1 [ 4,] 1 [ 5,] 0.99967 ... [ 996,] 5.4697e-06 [ 997,] 0 [ 998,] 1 [ 999,] 1 [1000,] 0