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(Always check original at SFI for current updates of plver.m - plverify.m is not yet posted at the Clauset site) But for these city size data Cosma thinks we shouldn't calculate the cumulative distribution function at each of your data points. "What you want to do instead is create a fitted Pareto distribution, which would be a command. concise on-line guide to using MATLAB Random number generators - randht.m (Matlab; used in plvar.m below to generate an empirical vector generated according to alpha and Xmin) (Always check original at SFI for current updates!) The functions pareto.fit and lnorm.fit produce R data structures, and the log-likelihood ratio functions, like pareto.lnorm.llr use those structures to extract the necessary parameters." Comparing Tsallis q to other distributions. (Always check original at SFI for current updates of plvar.m!) x = [500,150,90,81,75,75,70,65,60,58,49,47,40] put in the work folder plfit.m plvar.m plpva.m plver.m. Inside R copy and paste (or your modifications) with appropriate tresholds from Matlab for batch runs. Contents of the companion files: Matlab and R programs. or, for the cutoff of 47. Then you can do. Pareto.R. R code in a downloadable tgz file Updated 25 July 29 July 2007 Zip file Updated 25 July 29 July 2007 Documentation. But for these city size data Cosma thinks we shouldn't calculate the cumulative distribution function at each of your data points. "What you want to do instead is create a fitted Pareto distribution, which would be a command. Input: as above, e.g., (this is a command; its execution does not change array x) Calculating p-value for fitted power-law model (Matlab) Comparing fit of Tsallis q-exponential using LR=Loglikelihood Ratios. Update status. x = (5400,4800, 4000, . etc); [alpha, xmin, D] = plfit(x); once the randht.m function is loaded into Matlab, paste the command into the command window and run. (Always check original at SFI for current updates!) source(pareto.R) #c900 x = c(500,150,90,81,75,75,70,65,60,58,49,47,40) x.pareto x.pareto #c1000 x = c(400,90,85,80,78,70,68,58,50,50,50,50,45,43,40,40,40,40) x.pareto x.pareto #c1100 x = c(442,100,90,88,85,80,67,63,55,55,50,48,45,45,45,43,40,40,40) x.pareto x.pareto #c1150 x = c(150,145,130,100,100,90,80,72,72,60,60,56,52,45,45) x.pareto x.pareto #c1200 x = c(255,130,130,110,100,100,80,80,74,70,67,63,60,50,50,50,50,47,46) x.pareto x.pareto #c1250 x = c(320,140,140,130,100,100,90,80,77,75,67,60,60,54,50,47) x.pareto x.pareto #c1300 x = c(432,401,150,118,95,91,90,85,84,80,70,54,50,50,50,48) x.pareto x.pareto #c1350 x = c(432,400,150,114,96,95,90,90,90,75,70,50,50,50) x.pareto x.pareto #c1400 x = c(487,235,150,150,129,115,90,90,81,75,65,60,60,60,51,50,50) x.pareto x.pareto. Fitting a power-law distribution (Matlab) Comparing Pareto v. Lognormal fit. Inside R copy and paste (or your modifications) Then you can do. x = (5400,4800, 4000, . etc); [alpha, xmin, ntail] = plvar(x); Estimating uncertainty in the fitted parameters (Matlab) Shalizi R code (note: this is not yet a proper R package). is at. (Always check original at SFI for current updates of randht.m!) plfit.m (Matlab) - cut and paste your data over (1-rand (10000,1)).^(-1/(2.5-1)) in the format of the example in the code, which is x = (1-rand (10000,1)).^(-1/(2.5-1)); [alpha, xmin, D] = plfit(x); Contents. plvar.m (Matlab) Updated 25 July 2007. Doug White 07:19, 9 September 2007 (PDT) (First instructional edition, DRW and LT) 7 September 2007: corrected interim reporting in plpva.m; changed plfit.m, plvar.m and plpva.m to reshape input vector to column format, and to prevent using continuous approximation in small-sample regime for discrete data. Input: e.g., (this is a command; its execution does not change array x) or whatever threshold you decide to use. 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