similar to: KS test from ctest package

I frequently want to test for differences between animal size frequency distributions. The obvious test (I think) to use is the Kolmogorov-Smirnov two sample test (provided in R as the function ks.test in package ctest). The KS test is for continuous variables and this obviously includes length, weight etc. However, limitations in measuring (e.g length to the nearest cm/mm, weight to the nearest

Hello R friends, I'm wondering why I get funny qqnorm() results. It seems that they should all be reflected in the normal qqline(). For instance: if I qqnorm() bimodal or uniform data I get a sigmoidal in which the qqnorm() points lie above the qqline() at -ve theoretical quantiles, and the qqnorm() points lie below the qqline() at +ve theoretical quantiles. Yet I expect such platykurtic

Hi, I'm trying to figure out how anova works in R by translating the examples in Sokal And Rohlf's (1995 3rd edition) Biometry. I've hit a snag with planned comparisons, their box 9.4 and section 9.6. It's a basic anova design: treatment <- factor(rep(c("control", "glucose", "fructose", "gluc+fruct",

Dear R-users, I am trying to analyse the data of the box 10.5 in the Biometry from Sokal and Rohlf (2001) using R. This is a three-level nested anova with equal sample size : 3 different treatments are compared ; 2 rats (coded 1 or 2) / treatment are studied ; 3 preparations (coded 1, 2 or 3) / rats are available ; 2 readings of the glycogen content / preparations are realised. Treatment is

Hi, I couldn't figure out how to use the functions from the ctest library. I'm using the r-base package that comes with debian potato. library("ctest") told me that no such package existed. I checked the CRAN, but no such package was availiable, instead I was told that it would be part of the standard installation. But functions from ctest like shapiro-wilk don't work. The

The note to 1004 says "fixed for 1.3.1" Uh. No. It ain't. The problem was more serious than guessed as even the simplest testing would show. For example, Example 5.4 in Hollander and Wolfe (Nonparametric Statistical, Methods, 2nd ed., Wiley, 1999, pp. 180-181) R Version 1.3.1 (SuSE Linux 7.1) > X <-

1. There is, I believe, some redundant code in the calculation of the test statistic in ks.test in the package ctest. Lines 34-37 of the code read x <- y(sort(x), ...) - (0:(n - 1))/n STATISTIC <- switch(alternative, two.sided = max(abs(c(x, x - 1/n))), greater = max(c(x, x - 1/n)), less = -min(c(x, x - 1/n))) Lines 35-37 could read

Dear r-helpers, Prior to analysis of variance, I ran the Boxcox function (MASS library) to find the best power transformation of my data. However, reading the Boxcox help file, I cannot figure out if this function (through its associated log-likelihood function) corrects for * normality only * or if it also induces * homogeneity of variances *. I found in Biometry (Sokal and Rohlf, p. 419)

Hello I have used recently the kolmogorov smirnov test, which is a test of normality. This test is named ks.test() in ctest library of R. I wonder if the results of ks.test () are true, because the results are strange, time to time. thank you for help meriema -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi, I''m trying to understand the lme output and procedure. I''m using the Crawley''s book. I''m try to analyse the rats example take from Sokal and Rohlf (1995). I make a nested analysis using aov following the book. > summary(rats) Glycogen Treatment Rat Liver Min. :125.0 Min. :1 Min. :1.0 Min. :1 1st Qu.:135.8

Hi everybody, while performing ks.test for a standard exponential distribution on samples of dimension 2500, generated everytime as new, i had this strange behaviour: >data<-rexp(2500,0.4) >ks.test(data,"pexp",0.4) One-sample Kolmogorov-Smirnov test data: data D = 0.0147, p-value = 0.6549 alternative hypothesis: two.sided >data<-rexp(2500,0.4)

I've written a g.test() aka log-likelihood ratio test function for my opwn use. It's something I've seen requested (and looked to find myself) on this list a few times. It has the same basic syntax as chisq.test(). It does both goodness of fit tests and tests of independence. Yates' and Williams' corrections are implemented. I've put some examples from Sokal & Rohlf

> x <- runif(100) > y <- runif(100) > ks.test(x,y) Two-sample Kolmogorov-Smirnov test data: x and y D = 0.11, p-value = 0.5806 alternative hypothesis: two-sided ok I expected that, but: > ks.test(runif(100), "runif") One-sample Kolmogorov-Smirnov test data: runif(100) D = 0.9106, p-value < 2.2e-16 alternative hypothesis: two-sided How

Full_Name: Thomas Waterhouse Version: 2.9.1 OS: OS X 10.5.7 Submission from: (NULL) (216.239.45.4) ks.test uses a biased approximation to the p-value in the case of the two-sample test with ties in the pooled data. This has been justified in R in the past by the argument that the KS test assumes a continuous distribution. But the two-sample test can be extended to arbitrary distributions by a

[This was filtered (looking like spam) and manually approved by your list maintainer, MM ] EvolDir wrote: > Colleagues, > > A few days ago I posted a message inquiring what folks were using for > teaching biostats. I received a stunning 38 replies offering various > opinions, some quite surprising, along with links to web pages, pointers for > coverage, commentaries on

Hi, I was trying to draw an empirical distribution function with uniform confidence bands. So I tried to find a way to calculate values of the Kolmogorov-Smirnov Distribution but failed. I guess it must be hidden somewhere (since the ks-test is implemented), but I was unable to find it. Is there any way to do this? Thanks Leif Boysen

Hello all. This may be a trivially simple question to answer, but I'm a little bit stumped with respect to the calculation of the F statistics in nested anovas in R. If I understand correctly, the F statistic for the among-subgroups but within groups hypothesis is calculated as MS_subgroups/MS_error, while the F statistic for the factor is calculated as MS_factor/MS_subgroups (I'm

On Sun, 1 Jul 2001 mcdowella@mcdowella.demon.co.uk wrote: > Full_Name: Andrew Grant McDowell > Version: R 1.1.1 (but source in 1.3.0 looks fishy as well) > OS: Windows 2K Professional (Consumer) > Submission from: (NULL) (194.222.243.209) Please upgrade: we've found a number of Win2k bugs and worked around them since then, let alone teh bug fixes and improvements in R .... >

Hi there! I have two little different data. One is a computer test on people, the other is a paper and pencil test. two boxplots show me that the data is almost the same. So now I'd like to know if I could handle all data as one, by testing with ks.test: ==== > ks.test(el$angststoer, fl$angststoer) Two-sample Kolmogorov-Smirnov test data: el$angststoer and fl$angststoer D =

Dear R users I am using KS test to compare two different distribution for the same variable (temperature) for two different time periods. H0: the two distributions are equal H1: the two distributions are different ks.test (temp12, temp22) Two-sample Kolmogorov-Smirnov test data: temp12 and temp22 D = 0.2047, p-value < 2.2e-16 alternative hypothesis: two-sided Warning message: In