[R] lmom package - Resending the email
Dear R forum I sincerely apologize as my earlier mail with the captioned subject, since all the values got mixed up and the email is not readable. I am trying to write it again. My problem is I have a set of data and I am trying to fit some distributions to it. As a part of this exercise, I need to find out the parameter values of various distributions e.g. Normal distribution, Log normal distribution etc. I am using lmom package to do the same, however the parameter values obtained using lmom pacakge differ to a large extent from the parameter values obtained using say MINITAB and SPSS as given below - _ amounts = c(38572.5599129508,11426.6705314315,21974.1571641187,118530.32782443,3735.43055996748,66309.5211176106,72039.2934132668,21934.8841708626,78564.9136114375,1703.65825161293,2116.89180930203,11003.495671332,19486.3296339113,1871.35861218795,6887.53851253407,148900.978055447,7078.56497101651,79348.1239806592,20157.6241066905,1259.99802108593,3934.45912233674,3297.69946631591,56221.1154121067,13322.0705174134,45110.2498756567,31910.3686613912,3196.71168501252,32843.0140437202,14615.1499458453,13013.9915051561,116104.176753387,7229.03056392023,9833.37962177814,2882.63239493673,165457.372543821,41114.066453219,47188.1677766245,25708.5883755617,82703.7378298092,8845.04197017415,844.28834047836,35410.8486123933,19446.3808445684,17662.2398792892,11882.8497070776,4277181.17817307,30239.0371267968,45165.7512343364,22102.8513746687,5988.69296597127,51345.0146170238,1275658.35495898,15260.4892854214,8861.76578480635,37647.1638704867,4979.53544046949,7012.48134772332,3385.20612391205,1911.03114395959,66886.5036605189,2223.47536156462,814.947809578378,234.028589468841,5397.4347625133,13346.3226579065,28809.3901352898,6387.69226236731,5639.42730553242,2011100.92675507,4150.63707173462,34098.7514446498,3437.10672573502,289710.315303182,8664.66947305203,13813.3867161134,208817.521491857,169317.624400274,9966.78447705792,37811.1721605562,2263.19211279927,80434.5581206454,19057.8093104899,24664.5067589624,25136.5042354789,3582.85741610706,6683.13898432794,65423.9991390846,134848.302304064,3018.55371579808,546249.641168158,172926.689143006,3074.15064180208,1521.70624812788,59012.4248281661,21226.928522236,17572.5682970983,226.646947337851,56232.2982652019,14641.0043361533,6997.94414914865) library(lmom) lmom = samlmu(amounts) # __ # Normal Distribution parameters parameters_of_NOR <- pelnor(lmom); parameters_of_NOR mu sigma 115148.4 175945.8 Location Scale Minitab 115148.4 485173SPSS 115148.4 485173 # __ # Log Normal (3 Parameter) Distribution parameters zeta mu sigma 3225.798890 9.114879 2.240841 Location Scale Shape MINITAB 9.73361 1.76298 75.51864SPSS 9.7336 1.763 75.519 # __ Besides Genaralized extreme Value distributions, all the other distributions e.g. Gamma, Exponential (2 parameter) distributions etc give different results than MINITAB and SPSS. Can some one guide me? Regards Katherine [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
[R] lmom package
Dear R Forum I have a set of data say as given below and as an exercise of trying to fit statistical distribution to this data, I am estimating parameters. amounts = c(38572.5599129508,11426.6705314315,21974.1571641187,118530.32782443,3735.43055996748,66309.5211176106,72039.2934132668,21934.8841708626,78564.9136114375,1703.65825161293,2116.89180930203,11003.495671332,19486.3296339113,1871.35861218795,6887.53851253407,148900.978055447,7078.56497101651,79348.1239806592,20157.6241066905,1259.99802108593,3934.45912233674,3297.69946631591,56221.1154121067,13322.0705174134,45110.2498756567,31910.3686613912,3196.71168501252,32843.0140437202,14615.1499458453,13013.9915051561,116104.176753387,7229.03056392023,9833.37962177814,2882.63239493673,165457.372543821,41114.066453219,47188.1677766245,25708.5883755617,82703.7378298092,8845.04197017415,844.28834047836,35410.8486123933,19446.3808445684,17662.2398792892,11882.8497070776,4277181.17817307,30239.0371267968,45165.7512343364,22102.8513746687,5988.69296597127,51345.0146170238,1275658.35495898,15260.4892854214,8861.76578480635,37647.1638704867,4979.53544046949,7012.48134772332,3385.20612391205,1911.03114395959,66886.5036605189,2223.47536156462,814.947809578378,234.028589468841,5397.4347625133,13346.3226579065,28809.3901352898,6387.69226236731,5639.42730553242,2011100.92675507,4150.63707173462,34098.7514446498,3437.10672573502,289710.315303182,8664.66947305203,13813.3867161134,208817.521491857,169317.624400274,9966.78447705792,37811.1721605562,2263.19211279927,80434.5581206454,19057.8093104899,24664.5067589624,25136.5042354789,3582.85741610706,6683.13898432794,65423.9991390846,134848.302304064,3018.55371579808,546249.641168158,172926.689143006,3074.15064180208,1521.70624812788,59012.4248281661,21226.928522236,17572.5682970983,226.646947337851,56232.2982652019,14641.0043361533,6997.94414914865) library(lmom)lmom <- samlmu(amounts) # # Normal distribution parameters_of_NOR <- pelnor(lmom); parameters_of_NOR > parameters_of_NOR <- pelnor(lmom); parameters_of_NOR mu sigma > 115148.4 175945.8 # Minitab and SPSS parameter values Location Scale Minitab 115148.4 485173SPSS 115148.4 485173 # __ # Log normal 3 parameter distribution parameters_of_LN3 <- pelln3(lmom); parameters_of_LN3 > parameters_of_LN3 <- pelln3(lmom); parameters_of_LN3 zeta mu sigma 3225.798890 9.114879 2.240841 Location Scale ShapeMinitab 9.73361 1.76298 75.51864SPSS 9.7336 1.763 75.519 Similarly besides Generalized extreme Value distribution, all the parameter values vary significantly than parameter values obtained using Minitab and SPSS. In case of Normal distribution, the dispersion parameter is simply sample standard deviation and excel also gives the parameter value 485172.8 and varies significantly than what we get from R. And parameter values do differ even for many other distributions too viz. Gamma distribution etc. Is there any different algorithm or logic used in R? Can someone please guide.? Regards Katherine [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
