# [R] I cannot register but I have a burning Question about mcmcPack

lloyd no_last_name lloyd.relaxed at gmail.com
Fri Aug 22 20:00:51 CEST 2014

```I want to construct a simple MCMC Neural Net with normal errors

library(nnet)

nn.ts <- nnet( y=response, x = data.frame(result\$bestColSpace), size = 2,
skip = T, softmax=F, entropy=F, linout = T, maxit = 150, Hess = F, trace =
F, Wts=result\$coefficients[] )

L1Metric(nn.ts\$residuals)

 0.6978838

# I created a maximum likelihood version of

# my regular nn function

*mcmcNN = function( proposed.nwts, # proposed wts in Neural Net *

*y=result\$response, # response vector *

*X=result\$bestColSpace ) # matrix same rows as y*

*{ *

result\$coefficients = proposed.nwts

uHat = pureNN( result )\$fit;

# expected values of the NN given X, y and proposed parms

singlelikelihoods = dnorm(y, mean = uHat, sd = sd(x=(y-uHat)), log = T)

LL = sum(singlelikelihoods)

return( LL );

*} # end of function mcmcNN*

# function for metropolis-hastings

and I ran the Metropolis-hastings in

library(mcmcPack);

*post.samp <- MCMCmetrop1R( mcmcNN, theta.init = nn.ts\$wts,*

X=result\$bestColSpace, y=response,

V=NULL, # or =covV

thin=4, mcmc=12000, burnin=12000,

tune=rep( 0.55, length(nn.ts\$wts) ),

logfun=TRUE, force.samp=T )

# with each row of parms ( neural net weights ) in the posterior sample

# I computed the fitted.values with my neural net function

# next, I located the best fit of all of these 12,000 models

# I even started init.theta with the nnet weights

> minErr # from all proposed paramaters ( NN wts ) with fixed X and y

 0.8727279

This is significantly worse than nnet;

The neural net, being nonlinear regression, is sensitive to initial
values. Therefore, it will find a local minimum. I expected mcmc to return
the global minimum ( average absolute deviation ).

Thanx,

Lloyd L

Is there some way I can help R-users?

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