[ADMB Users] Integrating R and ADMB to generalize TPL files

[dave fournier]

Here is an example of why I dislike the R approach
so much.

https://stat.ethz.ch/pipermail/r-sig-mixed-models/2012q4/019349.html

I was curious to find out why introducing to additional parameters for the
variance of the random effects would produce no improvement in the
model.  So first I wanted to run glmmadmb as seth had done.

I put his data in a file and read it into R.

Then I ran his model by grabbing his call to glmmadmb.
I was surprised to see that I got a different result from Seth.

Examination of the design matrix and I realized that one of the columns of
the data needed to be changed to a factor.  If I had not got the
different result I would have thought that the analysis was correct.
That was the first gotcha.  With R you never really know what
you are doing.   This was for the simpler model with paramerter for
the variance of the RE's.

Then I used Seth's R script to run the more complicated model.
He was right that the likelihood did not change.  Strange!

So I examined the design matrix for the RE's.
The problem was obvious.  rather than a different
parameter for each time period the design matrix had
an overall mean and differences from it.  While this
would be the same for fixed effects it is not equivalent
for random effects because it assumes a different covariance
structure.

I hacked together the correct model by hand and ran it on his data.
Results are:

# Number of parameters = 8  Objective function value = 255.508 Maximum 
gradient component = 1.44753e-06
# pz:
0.000100000000000
# beta:
  -10.7785676169 -2.55267985235 -4.72926437596 6.91095066201
# tmpL:
  0.352458239856 0.489012745656 0.496221176099
# tmpL1:
  0.000100000000000
# log_alpha:
0.591420283490
the original fit is

     # Number of parameters = 6  Objective function value = 255.758 
Maximum gradient component = 2.23893e-05
# pz:
0.000100000000000
# beta:
  -10.4061157821 -1.87212686310 -3.41511253652 8.12797421786
# tmpL:
  0.431551628285
# tmpL1:
  0.000100000000000
# log_alpha:
0.593869268695

So it appears that the variance is larger in the second and third time 
period, but not
significantly so.

I welcome your snide comments.