LMC manually fitted
Nikkie,
you made sure that the Cauchy-Schwartz inequality holds, and although it
is a necessary condition, it is not a sufficient condition for the
coefficient matrices to be positive definite.
function fit.lmc in package gstat contains a function that computes the
positive definite approximation (I believe in a least squares sense) to
a non-positive definite matrix by zero-ing the negative eigenvectors:
posdef = function(X) {
q = eigen(X)
d = q$values
d[d < 0] = 0
q$vectors %*% diag(d, nrow = length(d)) %*% t(q$vectors)
}
You could try to use that on the partial sill matrices. I believe it
still has the risk that things are perfectly correlated, in the end,
hence the correct.diagonal option (mess?) in fit.lmc.
Hth,
On 03/18/2011 12:20 PM, Nikki roy wrote:
Dear friends, I am doing cokriging and i could not fit the LMC to my variograms automatically, therefore i ftted the LMC manually. i have a total of 15 variograms (direct variograms and cross variograms). eg: clay.g$model$clay[1,2] clay.g$model$clay[1,2] = 0.22 clay.g$model$clay[2,2] = 0.15 clay.g$model$evi[1,2] = 0.005 clay.g$model$evi[2,2] = 0.0105 clay.g$model$clay.evi[1,2] = 0.185 clay.g$model$clay.evi[2,2] = 0.025 then i checked my matrices with this condition which they need to satisfy:nugget(clay.evi) =<sqrt(nugget(clay) * nugget(evi)) and i got something like this for example: clay.g$model$clay[1,2] = 0.22 clay.g$model$clay[2,2] = 0.15 clay.g$model$evi[1,2] = 0.005 clay.g$model$evi[2,2] = 0.0105 clay.g$model$clay.evi[1,2] = 0.030 clay.g$model$clay.evi[2,2] = 0.025 still after this correction i am getting this error: predict.gstat(clay.g, newdata = dem) non-positive definite coefficient matrix in structure 1non-positive definite coefficient matrix in structure 2Warning: No Intrinsic Correlation or Linear Model of Coregionalization found Reason: coefficient matrix not positive definite Warning: [add `set nocheck = 1;' to the command file to ignore the following error] Error in predict.gstat(clay.g, newdata = dem) : gstat: value not allowed for: variograms do not satisfy a legal model May you please help, what went wrong and how to solve this. Thanks for your usual co-operation, Nikkie [[alternative HTML version deleted]]
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