Which package is gofstat in? can you show us your data, or some details about your data?
Note that the KS test (and all goodness of fit tests) are rule out tests, they can show that the data is unlikely to come from a distribution, but can never prove that it does come from a distribution. -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of baxy77 Sent: Friday, July 29, 2011 4:44 AM To: r-help@r-project.org Subject: [R] How to interpret Kolmogorov-Smirnov stats Hi, Interpretation problem ! so what i did is by using the: >fit1 <- fitdist(vectNorm,"beta") Warning messages: 1: In dbeta(x, shape1, shape2, log) : NaNs produced 2: In dbeta(x, shape1, shape2, log) : NaNs produced 3: In dbeta(x, shape1, shape2, log) : NaNs produced 4: In dbeta(x, shape1, shape2, log) : NaNs produced 5: In dbeta(x, shape1, shape2, log) : NaNs produced 6: In dbeta(x, shape1, shape2, log) : NaNs produced ##Is this a real problem - the input contains of 900 data points of which 6 caused this message# got the following shape parameters for my distribution: >summary(fit1) Fitting of the distribution ' beta ' by maximum likelihood Parameters : estimate Std. Error shape1 2.148779 0.1458042 shape2 810.067515 61.8608126 Loglikelihood: 1917.51 AIC: -3831.02 BIC: -3823.15 Correlation matrix: shape1 shape2 shape1 1.0000000 0.8880194 shape2 0.8880194 1.0000000 now if i do : >gofstat(fit1, print.test=TRUE) Kolmogorov-Smirnov statistic: 0.06630064 Kolmogorov-Smirnov test: not rejected The result of this test may be too conservative as it assumes that the distribution parameters are known Cramer-von Mises statistic: 0.3866663 Crame-von Mises test: not calculated Anderson-Darling statistic: 2.820576 Anderson-Darling test: not calculated So then what i did is I bootstrapped the data based on gathered parameters a i b : r_beta <- rbeta(378, 2.148779, 810.067515, ncp = 0); ks.boot(vectNorm, r_beta, nboots=1000, alternative = c("two.sided", "less", "greater"), print.level=0) and got : $ks.boot.pvalue [1] 0.002 $ks Two-sample Kolmogorov-Smirnov test data: Tr and Co D = 0.1323, p-value = 0.002684 alternative hypothesis: two.sided $nboots [1] 1000 attr(,"class") [1] "ks.boot" So I'm not a stats type of a guy so I need some reassurance that i did this by the book. and also the part that confuses me and which i do not understand is if Kolmogorov-Smirnov statistic reports the difference of 0.06630064 which indicate that my data fits the beta quite well, why the bootstrap rejects the hypothesis that both data sets come from the same population. Or did i misunderstood something??? Please do correct me. Furthermore, should I be worried that other two tests were not computed ? Thank you baxy -- View this message in context: http://r.789695.n4.nabble.com/How-to-interpret-Kolmogorov-Smirnov-stats-tp3703655p3703655.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@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. ______________________________________________ R-help@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.
