Hello all:
I would just like to add some information to help the discussion along.

In addition to the variance-covariance matrix that is outputted in the .cov 
file that Ken mentioned, the Fisher information matrix itself (inverse of 
variance-covariance) is also outputted in the .coi file.   Additional files, 
such as .rmt (R matrix), and .smt (S matrix) are also outputted upon user 
request ($COV PRINT=RS, for example)

A test related to Wald and log-likelihood ratio tests is the Lagrange 
Multiplier test.  For this purpose, NONMEM outputs the following in the .ext 
file:
Iteration -1000000008 lists the partial derivative of the log likelihood (-1/2 
OFV) with respect to each estimated parameter.

PFIM, POPED, and NONMEM's $DESIGN calculate the expected FIM with respect to 
the data, and the expected value R matrix is equivalent to the expected value 
of the S matrix.  That is, Ey(R)= Ey(S).

Several companion/interface software to NONMEM have additional model evaluation 
facilities, such as stepwise covariate model (scm) building in Perl Speaks 
NONMEM, and Wald test in PDxPop.


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Pharmacometrics R&D
ICON Early Phase
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