similar to: ks.test() output interpretation

Hello, While doing test of normality under R and SAS, in order to prove the efficiency of R to my company, I notice that Anderson Darling, Cramer Van Mises and Shapiro-Wilk tests results are quite the same under the two environnements, but the Kolmogorov-smirnov p-value really is different. Here is what I do: > ks.test(w,pnorm,mean(w),sd(w)) One-sample Kolmogorov-Smirnov test data: w D

All, Happy World Cup and Wimbledon. This morning finds me with the first of my many daily questions. I am running a ks.test on residuals obtained from a regression model. I use this code: > ks.test(Year5.lm$residuals,pnorm) and obtain this output One-sample Kolmogorov-Smirnov test data: Year5.lm$residuals D = 0.7196, p-value < 2.2e-16 alternative hypothesis: two.sided Warning

Is it possible to read fortran binaries with R? I tried unsucessfully and my understanding is that fortran write binaries with leading and trailing bytes. I get numbers but not the right ones. Thanks ps: the binary I'm interested in reading is a MODFLOW output with a mix of character, double and integers. [[alternative HTML version deleted]]

hello! i don't want to test my sample data for normality, but exponential- lognormal- or gammadistribution. as i've learnt the anderson-darling-test in R is only for normality and i am not supposed to use the kolmogorov-smirnov test of R for parameter estimates from sample data, is that true? can you help me, how to do this anyway! thank you very much! nadja

I have data on a regular grid in the format: x y z data. I would like to be able to plot them in 3d kind of like a volume or a mesh with colors. Is that possible in R and how? Thanks [[alternative HTML version deleted]]

The structure of the file is clear (see below) the first line is made of integers and doubles with the fifth being a text string followed by arrays of double precision number.: int1 int2 double1 double2 text int3 int4 int5 (array of double) here is an example of file: 1 1 1.0 1.0 "HEAD" 160 224 3 23.4 34.5 ...... I tried to read the first line with readBin (results are copied below):

I'm trying to read a binary file created by a fortran code using readBin and readChar. Everything reads fine (integers and strings) except for double precision numbers, they are read as huge or very small number (1E-250,...). I tried various endianness, swap, But nothing has worked so far. I also tried on R 64 bit for linux and windows (R 2.14) and R 2.11 on windows XP 32 bit. Any help would

Hello all, Would somebody be kind enough to show me how to do a KS test in R for a lognormal distribution with ESTIMATED parameters. The R function ks.test()says "the parameters specified must be prespecified and not estimated from the data" Is there a way to correct this when one uses estimated data? Regards, Kwabena. -------------------------------------------- Kwabena Adusei-Poku

Hi R Users, I have two vectors, x and y, of equal length representing two types of data from two studies. I would like to test if they are similar enough to use them interchangeably. No assumptions about distributions can be made (initial tests clearly show that they are not normal). Here some result: Two-sample Kolmogorov-Smirnov test data: x and y D = 0.1091, p-value < 2.2e-16 alternative

The following code should give me (at least that's what I think) two list boxes with their individual scrollbars. But instead both scrollbars stick to the same listbox even though they work as they should (scroll individual listboxes). When I remove all the widgets on top everything works perfectly. Any help would be highly apprecciated. ps: all subsequent listboxes and scrollbars work

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

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)

> 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

Folks, Normally, in a data frame, one observation counts as one observation of the distribution. Thus one can easily produce a CDF and (in Splus atleast) use cdf.compare to compare the CDF (BTW: what is the R equivalent of the SPlus cdf.compare() function, if any?) However, if each point should not count equally, how can I weight the points before comparing the distributions? I was thinking of

Hello, I want to do normality test on my data I write this but I don't understand the display of the results ks.test(data,"pnorm") In fact I want to know if my data is a normal distribution. I have to check the p-value or D? Thanks. _____________________________________________________________________________ l [[alternative HTML version deleted]]

I'm looking for goodness of fit tests for gamma distributions with large data sizes. I have a matrix with around 10,000 data values in it and i have fitted a gamma distribution over a histogram of the data. The problem is testing how well that distribution fits. Chi-squared seems to be used more for discrete distributions and kolmogorov-smirnov seems that large sample sizes make it had to

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

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 all, One can use ks.test(x,y) for a one-sample kolmogorov-smirnov test: x being the data sample y being a string specifying a distribution I notice the help on ks.test does not tell you how to get such a list. Is this a hole in my R knowledge? Where can I get a list of the strings specifying the possible distributions? and more specifically What would be the string and following