similar to: Kolmogorov-Smirnoff test

I am trying to write a function that will simulate observed counts X and Y as below, I want the function to be able give many replicates, can somebody advise? thanks Oarabile #alpha,n sdx and sdy are constant, and N is a vector of length n. unstructured<-function(n,N,alpha,sdx,sdy){ Vx<-rnorm(n,mean=0,sd=sdx) Vy<-rnorm(n,mean=0,sd=sdy) thetax<-exp(alpha+Vx) thetay<-exp(alpha+Vy)

I'm trying to write a function that takes a vector of length n and then takes the first value of the vector i.e j=1 and forms a new vector of length n (i.e replicate the first value n times). This function will then calculate the absoulte difference of the original vector and the new vector and store the results omitting the difference between the value and itself. This function should

Tried to look for package GLLMGibbs-(package for fitting mixed models by Gibbs samppling contributed by Myles' and Clayton) in CRAN but not found it, Does anybody know if it is still available? thanks Oarabile

I'm trying to produce empirical bayes estimates based on the lognormal model in disease mapping Is there a way this can be done in R? thanks Oarabile

I want to do kernel smoothing on the standardised mortality rates at specified location(x-y coordinates) and be able to produce contour map. I've tried to use the package called fields in R (function smooth.2d) but the problem is I get negative results after smoothing and I do not understand why? Your contributions will be highly appreciated or if there is any package that can do this I

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Hi, I have a problem with Kolmogorov-Smirnov test fit. I try fit distribution to my data. Actualy I create two test: - # First Kolmogorov-Smirnov Tests fit - # Second Kolmogorov-Smirnov Tests fit see below. This two test return difrent result and i don't know which is properly. Which result is properly? The first test return lower D = 0.0234 and lower p-value = 0.00304. The lower 'D'

Although I saw this issue being discussed many times before, I still did not find the answer to: why does R can not calculate p-values for data with ties (i.e. - sample with two or more values the same)? Can anyone elaborate some details about how does R calculate the p- values for the Kolmogorov Smirnov test statistics? I can understand the theoretical problem that continuous distributions do

Hello! I need to compare 2 datasets whether they come from the same distribution. I use function ks.boot{Matching}. And what is the confidence level of the p-value, returned by ks.boot function? The code is: set=read.table("http://stella.sai.msu.ru:8080/~gala/data/testsets.csv", header=T,sep=',') set1=set[!is.na(set$set1),'set1']

Hi r-users,   I would like to use Kolmogorov smirnov test but in my observed data(xobs) there are ties.  I got the warning message.  My question is can I do something about it?   ks.test(xobs, xsyn)           Two-sample Kolmogorov-Smirnov test data:  xobs and xsyn D = 0.0502, p-value = 0.924 alternative hypothesis: two-sided Warning message: In ks.test(xobs, xsyn) : cannot compute correct

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

I got a distribution function and a empirical distribution function. How do I make to Kolmogorov-Smirnov test in R. Lets call the empirical distribution function >Fn on [0,1] and the distribution function >F on [0,1] ks.test( ) thanks for the help -- View this message in context: http://www.nabble.com/Kolmogorov-Smirnov-test-tp23296096p23296096.html Sent

Is the kolmogorov-smirnov test valid on both continuous and discrete data? I don't think so, and the example below helped me understand why. A suggestion on testing the discrete data would be appreciated. Thanks, a <- rnorm(1000, 10, 1);a # normal distribution a b <- rnorm(1000, 12, 1.5);b # normal distribution b c <- rnorm(1000, 8, 1);c # normal distribution c d <- rnorm(1000,

I'm trying to use modified Kolmogorov-Smirnov test with a Normal which I don't know it's parameters. Somebody told me about the lilifor function in R, but just can't find it. Does anybody know how I can test with the modified Kolmogorov-Smirnov test? Porqu? usar una base de datos relacional cualquiera, si pod?s usar PostgreSQL?

Hi, I was wondering whether there is an implementation of the Kolmogorov-Smirnov goodness of fit test for discrete, ordinal scale data in R - I've only managed to find the test for continuous data. Thanks! Gila

Dear All, I've got a problem with ks.test. I've two realy large vectors, that I'd like to test, but I get an overflow, and the p-value cannot be calculated: > length(genomesv) [1] 390025 > length(scopv) [1] 140002 > ks.test(genomesv, scopv) Two-sample Kolmogorov-Smirnov test data: genomesv and scopv D = 0.2081, p-value = NA alternative hypothesis: two.sided

Hello, I got a rather simple question: Can I find somewhere in R the significance values for a Kolmogorov distribution (I know the degrees of freedom and I have already the maximum deviation). ks.test is not really doing what I want. All I need is the values, like one can get the values for a chi-squared distribution by 'qchisq(0.05, 375)'. tnx, Kurt.

The reference below describes the utility of the two-dimensional Kolmogorov-Smirnow (2DKS) test for detecting relationships in bivariate data. If this test has been implemented in R I would love to know about it! Thanks, jake Garvey, J. E., E.A. Marschall, and R.A. Wright (1998). "From star charts to stoneflies: detecting relationships in continuous bivariate data." Ecology

Hello, I am curious if anyone has had any success with finding a R version of a k-sample Kolmogorov-Smirnov test. Most of the references that I have able to find on this are fairly old and I am wondering if this type of analysis has fallen out of favour. If so, how do people tend to compare distributions when they have more than two? Is it reasonable to pursue an adjusted p-value method. That is,

Hi, I have two sets of data, an observed data and generated data. The generated data is obtained from the model where the parameters is estimated from the observed data. So I'm not sure which to use either one-sample test ks.test(x+2, "pgamma", 3, 2) # two-sided, exact or two-sample test ks.test(x, x2, alternative="l") If I use the one-sample test I need to