[R] nnet question
Erik.B.Johnson at colorado.edu
Mon Apr 26 19:26:20 CEST 2004
I am using R 1.8.0, and am attempting to fit a Neural Network model of a
time series (here called Metrics.data). It consists of one time series
variable run on its lag (AR(1)). Basically, in an OLS model it would
Metrics.data$ewindx ~ Metrics.data$ewindx.lag1
However, I am trying to run this through a neural network estimation.
So far, I have been getting convergence very quickly, and do not believe
it too be true.
Here is the code and output. Please note that I am using all of the
values for training and testing in one matrix, as I do not care about
the testing results right now, I only want to capture weights. Here is
the code and output
> nnet(metrics.data$ewindxlag1,metrics.data$ewindx,size=2, entropy=FALSE)
# weights: 7
initial value 78858370643.085342
final value 78841786515.212158
a 1-2-1 network with 7 weights
options were -
When I run the iris3 example, the convergence looks much nicer
(consisting of more than one iteration). Am I missing some fundamental
understanding of this example? Thanks for any input.
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From: Erik Johnson <ebjohnso at colorado.edu>
Subject: [R] nnet question
Date: Mon, 26 Apr 2004 11:01:12 -0600
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