I tried using JMP for the same and get two distinct recommendations when
using the unscaled values.
When using the unscaled values, Log Normal appears to be best fit. fitdist
in R is unable to provide a fit in this case.
Compare Distributions
ShowDistributionNumber of Parameters -2
Joshua, thanks for your reply.
I have tried out the following scaling and it seems to work fine:
scaledVariable <- (test-min(test)+0.001)/(max(test)-min(test)+0.002)
The gamma distribution parameters are obtained using the scaled variable and
samples obtained from this distributions are scaled
On Wed, 27 Apr 2011, Joshua Wiley wrote:
Hi,
I am not incredibly knowledgeable about gamma distributions, but
looking at your data, you have a tiny mean:variance ratio, which, I
believe, means that the bulk of the distribution will be near 0 and
you may run into computational problems (again I
Hi,
I am not incredibly knowledgeable about gamma distributions, but
looking at your data, you have a tiny mean:variance ratio, which, I
believe, means that the bulk of the distribution will be near 0 and
you may run into computational problems (again I think. I would
gladly be corrected). This
There was a small error in the data creation step and have fixed it as below:
test <- c(895.1358,2915.7447,335.5472,1470.4022,194.5461,1814.2328,
1056.3067,3110.0783,11441.8656,142.1714,2136.0964,1958.9022,
891.89,352.6939,1341.7042,167.4883,2502.0528,1742.1306,
837.1481,867.8533,3590.4308,1125
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