Re: [R] Fitting gamma and exponential Distributions with fitdist

From: vioravis <vioravis_at_gmail.com>
Date: Thu, 28 Apr 2011 01:26:06 -0700 (PDT)

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

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