- Fixed issues with translation to matlab and
applyfun. Element-wise calls can use meshgrid arguments directly, and one can force functions to use element-wise operations with
bxfun. So the following are equivalent: :
[X, Y] = meshgrid(x,y) applyfun(@min, X, Y)
function out = element_min(x,y) bxfun(@min, x, y) element_min(X, Y)
Meanwhile my challenge with additional arguments to
applyfun can be circumvented with an inline function,
applyfun(@(x,y) ssum(x,y, additional_arg), X, Y)
- In other developments, may have missed a rownormalization on growth noise.
- Time to run and compare R and matlab codes and send to Jim.
Check status of runs on farm. Appears that a type error blocked calculations of null distribution. Also, may need further threshold, a threshold of 220 for deviation occurs in 75% of (2000) runs (though depends on run length whether that is sufficiently rare). Threshold of 200 is reached in about 2%. re-running now.
tweaked my original code a bit to run the case with a single stable state and condition on observing large deviations, and see if the observations prior to the deviation show the ‘spurious’ increase in autocorrelation and variance observed in the original paper.
- Data for the conditional on deviation warning signal tau values (44 of the timeseries)
- tau values for unconditional set (2000 timeseries)
Looks like there is still a bias to me.