R help - Feb 2014 - Product of MSE and number of parameters when generating covariance matrix for Nonlinear least squares?

Hi!

In 'An Introduction to R', section 11.7 on nonlinear least squares
fitting,
the following example is given for obtaining the standard errors of the
estimated parameters:

"To obtain the approximate standard errors (SE) of the estimates we do:
     > sqrt(diag(2*out$minimum/(length(y) - 2) * solve(out$hessian)))
The 2 in the line above represents the number of parameters."

I know the inverted Hessian is multiplied by the mean square error and that
the denominator of the MSE is the degrees of freedom (number of samples -
number of parameters) but why does the numerator of the MSE (which is the
RSS) get multiplied by the number of parameters? I have read through
explanations of the method for obtaining the SE but I don't see where the
MSE gets multiplied by the number of parameters or why this is needed as
shown in the example?

Thanks for any help!




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