I have a nonlinear model with residuals that are negatively autocorrelated at short distances.
I can find no spatial correlation structures in nlme that can easily handle negative autocorrelation as most have bounds on parameter values so that the correlation is between 0 and 1.
First, is there something I am missing?
I tried to roll my own by calculating a correlation matrix with some negative values off the diagonal and then setting the correlation structure as follows within the function gnls:
correlation=corSymm(corr9x[lower.tri(corr9x)]) where corr9x is the $n times n$ matrix of correlations that I set up based upon distances between points in the data set. Some of these correlations are positive and some are negative. They are based on a Moran's I correlogram that I calculated from the residuals returned from a gnls model fit with NO spatial correlation employed.
I get the following error:
Initial values for corSymm do not define a positive-definite correlation structure I am unsure if the matrix is rejected out of hand because it contains negative values or if there is something I can do to coerce it. I have checked the lower triangle matrix returned and it matches what I intended.
Any input is appreciated.
Best Answer
I was having the same problem. I'm not sure if it's correct – but I was able to get past the positive definite problem by using the command nearPD(), which computes the nearest positive definite matrix.
I am now running into the problem that @wvguy8258 mentioned about the wrong dimensions.
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