Gaussian quadrature for bivariate normal distribution
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Dear R Users
I am maximizing a likelihood function it has two two correlated random effects which follows bivariate normal distribution. To get the marginal distribution I want to integrate out with respect to these two correlated random effects. Does any body know how can I implement gaussian quadrature to approximate this integral.
What distribution's log-likelihood are you using? If sigma is supposed
to be square-rooted, you may have to put a constraint or use of abs()
might suffice -- though, admittedly I'm not sure what that will do to
convergence behavior -- but it's hard to help without seeing the
function at hand.
> Dear R Users
> I am maximizing a user defined log likelihood function. It includes variance
> parameter (sigma). I used R function optim with BFGS maximization method.
> However, it stops before the solution saying “sqrt(sigma): NaNs produced”
> Could anybody know a proper transformation for sigma which can be passed in
> the function? For the correlation parameter I used Fishers’ transformation
> so it didn’t give any problems. I know we can use ‘L-BFGS-B’ method instead
> of ‘BFGS’. However, it produces some non sensible estimates.
> Thanks for taking time
> View this message in context: http://r.789695.n4.nabble.com/Maximization-problem-in-the-optim-function-tp4473408p4473408.html > Sent from the R help mailing list archive at Nabble.com.
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