Hi, In my code, I want to sample from the posterior distribution to get estimates for each parameter via the Bayesian approach. My model has spatial coefficient and lasso penalty.
When I run this line gibbs_lasso(y = Y, x= X, W=W.rook, tau = 0.5, M=2) It works, however, when I changed M from 2 to 5, I get the following error: Error in rgig(1, 0.5, (SIGMA[m, ]/theta2) * ((y[ik] - crossprod(x[ik, : invalid parameters for GIG distribution: lambda=0.5, chi=nan, psi=nan In addition: Warning message: In rgamma(1, shape = 1.5 * n + a0, rate = SIGMAgamma + b0) : NAs produced It makes sense that rgig cannot accept NaN values of sigma parameter in its computation. But why does rgamma produces NaN value for sigma? The gamma distribution require both shape and scale parameters to be positive and my R computations for both should always be positive then sigma can be sampled easily. So, what went wrong in my code? Best regards Sanna ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
