Extra weight values in neural network (nnet for R package)
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I'm attempting to reverse engineer in Excel how the nnet package works using some simple input data. Here's the steps I've taken

1. Import dummy data:`test <- read.csv('dataScaled.csv',header=TRUE,sep = ",")`

2. Train the network:
`anntrain <- nnet(Price ~ Sqft + Bedrooms + Bathrooms,test[1:650,],size=2, maxit=5000,linout=TRUE)`

3. Grab the weights of the ANN:
`anntrain$wts`
This outputs:

     [1] -2.12443010  6.68900321  0.85338018 -0.73329823 -3.95336239  7.91917321
     [7] -5.38893137  4.05941771 -0.02062346  0.26584364  0.32881035

4. Grab fitted values of trained network:
`anntrain$fitted.values`
This outputs what I believe to be the scaled Price predictions of the trained network for each of the 650 transactions I trained it on above. 

5. Prove out the fitted values by recalculating using the above weights using the sigmoid function.

My confusion is that it outputs 11 weight values. If I only have 3 inputs, 2 hidden nodes and 1 output, shouldn't that equate to only 8 weights? What are the 3 extra weights for?