I am trying to fit gamma and exponential distributions using fitdist function
in the "fitdistrplus" package to the data I have and obtain the parameters
along with the AIC values of the fit. However, I am getting errors with both
distributions. I have given an reproducible example with the errors I am
getting below. Can someone please let me know how to overcome this issue???
library("fitdistrplus")
test <- (895.13582915.7447,335.5472,1470.4022,194.5461,1814.2328,
1056.3067,3110.0783,11441.8656,142.1714,2136.0964,1958.9022,
891.89,352.6939,1341.7042,167.4883,2502.0528,1742.1306,
837.1481,867.8533,3590.4308,1125.9889,1200.605,4321.0011,
1873.9706,323.6633,1912.3147,865.6058,2870.8592,236.7214,
580.2861,350.9269,6842.4969,1886.2403,265.5094,199.9825,
1215.6197,7241.8075,2381.9517,3078.1331,5461.3703,2051.3997,
751.6575,714.3536,598.4539,425.6656,215.2103,608.785,
369.4744,2398.6506,918.6844,525.6925,2549.3694,4108.8983,
2824.0758,1068.7508,249.995,3863.9839,1152.1506,531.6844)
fitdist(test,"gamma",method ="mle")
Error in fitdist(test, "gamma", method = "mle") :
the function mle failed to estimate the parameters,
with the error code 100
In addition: Warning messages:
1: In dgamma(x, shape, scale, log) : NaNs produced
2: In dgamma(x, shape, scale, log) : NaNs produced
3: In dgamma(x, shape, scale, log) : NaNs produced
4: In dgamma(x, shape, scale, log) : NaNs produced
5: In dgamma(x, shape, scale, log) : NaNs produced
6: In dgamma(x, shape, scale, log) : NaNs produced
7: In dgamma(x, shape, scale, log) : NaNs produced
8: In dgamma(x, shape, scale, log) : NaNs produced
9: In dgamma(x, shape, scale, log) : NaNs produced
fitdist(test,"exp",method ="mle")
Error in fitdist(test, "exp", method = "mle") :
the function mle failed to estimate the parameters,
with the error code 100
In addition: Warning message:
In dexp(x, 1/rate, log) : NaNs produced
Thank you.
Ravi
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Fitting gamma and exponential Distributions with fitdist
6 messages · vioravis, Joshua Wiley, Brian Ripley
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, 837.1481,867.8533,3590.4308,1125.9889,1200.605,4321.0011, 1873.9706,323.6633,1912.3147,865.6058,2870.8592,236.7214, 580.2861,350.9269,6842.4969,1886.2403,265.5094,199.9825, 1215.6197,7241.8075,2381.9517,3078.1331,5461.3703,2051.3997, 751.6575,714.3536,598.4539,425.6656,215.2103,608.785, 369.4744,2398.6506,918.6844,525.6925,2549.3694,4108.8983, 2824.0758,1068.7508,249.995,3863.9839,1152.1506,531.6844) Any help would be appreciated. Thank you. -- View this message in context: http://r.789695.n4.nabble.com/Fitting-gamma-and-exponential-Distributions-with-fitdist-tp3477391p3480133.html Sent from the R help mailing list archive at Nabble.com.
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 all makes me think it might be a convergence issue. Perhaps you can transform your data for estimation and then transform it back (not sure if this would yield equivalent results)? fitdist(test + 10^4, "gamma") fitdist(test/10^4, "gamma") Good luck, Josh
On Wed, Apr 27, 2011 at 9:42 PM, vioravis <vioravis at gmail.com> wrote:
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, 837.1481,867.8533,3590.4308,1125.9889,1200.605,4321.0011, 1873.9706,323.6633,1912.3147,865.6058,2870.8592,236.7214, 580.2861,350.9269,6842.4969,1886.2403,265.5094,199.9825, 1215.6197,7241.8075,2381.9517,3078.1331,5461.3703,2051.3997, 751.6575,714.3536,598.4539,425.6656,215.2103,608.785, 369.4744,2398.6506,918.6844,525.6925,2549.3694,4108.8983, 2824.0758,1068.7508,249.995,3863.9839,1152.1506,531.6844) Any help would be appreciated. Thank you. -- View this message in context: http://r.789695.n4.nabble.com/Fitting-gamma-and-exponential-Distributions-with-fitdist-tp3477391p3480133.html Sent from the R help mailing list archive at Nabble.com.
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Joshua Wiley Ph.D. Student, Health Psychology University of California, Los Angeles http://www.joshuawiley.com/
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 back using: scaled <- (randomSamples*(max(test) - min(test) + 0.002)) + min(test) - 0.001 Is there a better way to scale the variable??? I would prefer fitting a distribution without scaling it. Thank you. Ravi -- View this message in context: http://r.789695.n4.nabble.com/Fitting-gamma-and-exponential-Distributions-with-fitdist-tp3477391p3480265.html Sent from the R help mailing list archive at Nabble.com.
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 think. I would gladly be corrected).
No, the data are well-fitted by an exponential of mean about 2000.
This all makes me think it might be a convergence issue. Perhaps you can transform your data for estimation and then transform it back (not sure if this would yield equivalent results)? fitdist(test + 10^4, "gamma")
No.
fitdist(test/10^4, "gamma")
Yes. This indeed a scaling issue: the estimated rate is very small.
Good luck, Josh On Wed, Apr 27, 2011 at 9:42 PM, vioravis <vioravis at gmail.com> wrote:
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, 837.1481,867.8533,3590.4308,1125.9889,1200.605,4321.0011, 1873.9706,323.6633,1912.3147,865.6058,2870.8592,236.7214, 580.2861,350.9269,6842.4969,1886.2403,265.5094,199.9825, 1215.6197,7241.8075,2381.9517,3078.1331,5461.3703,2051.3997, 751.6575,714.3536,598.4539,425.6656,215.2103,608.785, 369.4744,2398.6506,918.6844,525.6925,2549.3694,4108.8983, 2824.0758,1068.7508,249.995,3863.9839,1152.1506,531.6844) Any help would be appreciated. Thank you. -- View this message in context: http://r.789695.n4.nabble.com/Fitting-gamma-and-exponential-Distributions-with-fitdist-tp3477391p3480133.html Sent from the R help mailing list archive at Nabble.com.
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
-- Joshua Wiley Ph.D. Student, Health Psychology University of California, Los Angeles http://www.joshuawiley.com/
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595
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 Show Distribution Number of Parameters -2*LogLikelihood AICc X LogNormal 2 1016.29587 1020.50639 Johnson Sl 3 1015.21183 1021.6404 GLog 3 1016.29587 1022.72444 Exponential 1 1021.58662 1023.65559 Johnson Su 4 1015.21183 1023.9391 Gamma 2 1021.02475 1025.23528 Weibull 2 1021.50762 1025.71815 Extreme Value 2 1021.50762 1025.71815 Normal 2 Mixture 5 1042.55455 1053.66566 Normal 3 Mixture 8 1042.74433 1061.56786 Normal 2 1082.36992 1086.58045 However, when using the scaled values, Gamma appears to be best fit. I am getting the same using R as well. Compare Distributions Show Distribution Number of Parameters -2*LogLikelihood AICc X Gamma 2 -114.92911 -110.71858 Weibull 2 -113.54302 -109.3325 Extreme Value 2 -113.54302 -109.3325 Exponential 1 -108.01019 -105.94122 Johnson Sl 3 -104.69191 -98.263335 Johnson Su 4 -104.69191 -95.964634 GLog 3 -102.35037 -95.921798 LogNormal 2 -70.727608 -66.517082 Normal 2 Mixture 5 -77.349192 -66.238081 Normal 3 Mixture 8 -77.159407 -58.335878 Normal 2 -37.533813 -33.323287 What is the difference between the MLE methods in JMP and R??? Is it advisable to go with the scaled values in R??? Thank you. Ravi -- View this message in context: http://r.789695.n4.nabble.com/Fitting-gamma-and-exponential-Distributions-with-fitdist-tp3477391p3480422.html Sent from the R help mailing list archive at Nabble.com.