-----Original Message----- From: r-sig-geo-bounces at stat.math.ethz.ch [SMTP:r-sig-geo-bounces at stat.math.ethz.ch] On Behalf Of liyun ma Sent: Wednesday, October 19, 2005 2:45 PM To: r-sig-geo at stat.math.ethz.ch Subject: [R-sig-Geo] 2 questions about likfit in geoR Dear all, I have two questions about using the likfit function in geoR. One is about the trend estimation. The likfit function estimates the mean parameters and the covariance parameters at the same time. Is there any way to specify the trend function to be a fixed number? For example let mean be zero? The second question is the function gives beta.var, which is the estimated variance for the mean parameter beta. Can we also get the estimated variance for the covariance parameters kappa, sigma^2 and phi? Thanks a lot! Lauren Ma
-------------- Hi Lauren, First, you have to pass the argument Hessian=TRUE in likfit Then to see the Hessian, you can use: yourlikfitobject$info.minimisation.function The Hessian will be the last item. By inverting the Hessian you will have the observed covariance matrix. The observed variances will be in the main diagonal. In the Hessian the order of parameters is as follows: phi, ratio of sigma^2 over tau^2, kappa, and lambda (the latter when you have included the Box-Cox parameter, recommended). In my experience (always with lambda<>1) the parameter beta is underestimated. I have compared it with what you get after predictions (with mean(krige.object$predict); this should be similar to, though not identical with beta) and with the simple sample mean of the original data and beta has always been much smaller. I contacted the author of geoR (Paulo Ribeiro Jr) and he said that he would check the code. To get beta in the original units you have to back-transform it (assuming lambda is <>1) usign the BCtransform function. I hope this helps. Ruben