Recalculating predicted values of nerual network
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I have the following code in R using the nnet package to get the weights and predicted values:

    test <- read.csv('ScaledData.csv',header=TRUE,sep = ",")
    
    ANNtrain <- nnet(Price ~ Sqft + Bedrooms + Bathrooms,test[1:650,],size=2, maxit=5000, linout=TRUE)
    
    ANNtrain$wts
    
    ANNtrain$fitted.values

So I end up with these weights:

    b->h1	2.3681687
    i1->h1	-0.3898256
    i2->h1	1.3565967
    i3->h1	-1.8423163
    b->h2	1.4826518
    i1->h2	0.6584391
    i2->h2	0.8964167
    i3->h2	-1.3290192
    b->o	1.0835755
    h1->o	-4.977095
    h2->o	4.2466241

I'm attempting to recalculate the fitted values, for example the fitted value of the first transaction is .025014924.

I'm using the sigmoid function on each hidden node which I understand to be 1/e^-p where p=the sum of the weighted values of each input variable.

I've been playing with this for a couple hours now and I'm stumped. I can't recalculate the fitted value no matter what I do. Any ideas? Does the linout parameter change the calculation at all?