similar to: Fwd: Re: Goodness-of-fit test for gamma distribution?

Hi all, I am wondering if anyone has written (or knows of) a function that will conduct a goodness-of-fit test for a gamma distribution. I am especially interested in test statistics have some asymptotic parametric distribution that is independent of sample size or values of fitted parameters (e.g., a chi-squared distribution with some fixed df), because I want to fit gamma distributions to

Hi all, I am trying to model (continuous) spatial variation in a response variable as a function of one or more of several explanatory variables. I am principally interested in obtaining some measure of the relative "importance" of the explanatory variables. I have found several R libraries that are tailored to this sort of problem (geoR, geoRglm, gstat, etc.); however, as near

Dear R helpers, I need to find out maximized log likelihoods, parameters estimates and standard errors (in parentheses) of r largest-order statistics model, with different values of r by using the function rlarg.fit. I want to specify required number of order statistics to the model. I attached my data file with this mail.please help me. Ruposh --- r-help-request at stat.math.ethz.ch wrote:

Dear David, you asked this question a while ago on the R mailing list and got no answer. I have the same problem and was wondering if you had found a solution Cheers Loic Loic Thibaut, PhD candidate, ARC Centre of Excellence for Coral Reef Studies, School of Marine Biology, James Cook University, Townsville, Qld, 4811, Australia. Tel + 61 747 815 735, Fax: + 61 747 251 570, email:

Hi I am experiencing a problem with the random number generator. When I call any function that involve RNG such as "runif" or "sample" I get this error: .Random.seed is not an integer vector but of type 'double' I can't coerce the random seed and it's recommended not to alter it anyway. Can anybody help me? Thanks and kind regards, Piero Visconti, PhD

Dear list, I have two dissimilarity matrices, one for a training data set which I then clustered using PAM. The second is a diss matrix for a validation data set (an independent field sample). I have been trying to use knn to distinguish distances between the validation data set and the 6 mediods of the training data defined by using PAM. I continue to get error messages in regards to either the

I'm trying out a basic plot, but something about the way I subset my data leads to problems with the plot. Here is the first bit of my data set year,date,location,quadrat_juvenile,photo_location,photo_exists,genus,count,divers 2005,2005-04-30, 1 Fringing Reef,1, 1 Fringing Reef Coral Transect Pole 1-2 Quadrat 1,t,Acanthastrea,0,HP+MEM 2005,2005-04-30, 1 Fringing Reef,1, 1 Fringing Reef

Hello, I recently started using the metafor package (version 1.6-0) in R (2.15.1, 64-bit Windows 7) and noticed that I was getting slightly different values when I manually calculated the standardized mean difference versus what escalc was giving me. Here''s a very simple example: escalc(measure="SMD", m1i=5,m2i=10,n1i=5,n2i=5,sd1i=1,sd2i=2,vtype="LS") The result

Hello List, My question is an elementary one. I have run a fuzzy kmeans cluster using FANNY to group freshwater fish assemblages. I then went in the field to validate that classification and have retrieved new assemblage data for a new suite of streams. Therefore I would like to use Predict to determine how well the original clustering fits the new data. However I have not figured out a

For block effects with small variance, lmer will sometimes estimate the variance as being very close to zero and issue a warning. I don't have a problem with this -- I've explored things a bit with some simulations (see below) and conclude that this is probably inevitable when trying to incorporate random effects with not very much data (the means and medians of estimates are plausibly

Dear useRs, I was searching CRAN for implementation of kurtosis and skewness tests, and found that there is some kind of lack on it. So, I have written three functions: 1. Anscombe-Glynn test for kurtosis 2. Bonett-Seier test based on Geary's kurtosis (which is not widely known, but I was inspired by original paper describing it, found coincidentally in Elsevier database) 3.

I have a question about the D'Agostino skewness test and the Anscombe-Glynn kurtosis test. agostino.test(x, alternative = c("two.sided", "less", "greater")) anscombe.test(x, alternative = c("two.sided", "less", "greater")) The option "alternative" in those two functions seems to be the null hypothesis. In the output, the

Ady2 suggested to me on IRC to add additional information to the ML. With BIOS P89 v1.32 (03/05/2015) I saw errors from core_udp_sendto: Getting cached packet My IP is 0.0.0.0 core_udp_sendto: stalling on configure with no mapping core_udp_sendto: stalling on configure with no mapping core_udp_sendto: stalling on configure with no mapping core_udp_sendto: stalling on configure with no mapping

Greetings, I have generated several ROC curves and would like to compare the AUCs. The data are cross sectional and the outcomes are binary. I am testing which of several models provide the best discrimination. Would it be most appropriate to report AUC with 95% CI's? I have been looking in to the "net reclassification improvement" (see below for reference) but thus far I can only

On Mon, Jun 22, 2015 at 6:03 AM, S. Schauenburg <s.schauenburg at gmail.com> wrote: > Ady2 suggested to me on IRC to add additional information to the ML. > > With BIOS P89 v1.32 (03/05/2015) I saw errors from core_udp_sendto: > Getting cached packet > My IP is 0.0.0.0 > core_udp_sendto: stalling on configure with no mapping > core_udp_sendto: stalling on configure with

Hi, is there a platform independent way to log a complete R session (input + output + warnings + errors + ???) into a text file? I can use sink(file, split=T) and savehistory() but it is quite cumbersome to merge the files and still there is some loss (eg. warnings). I wondered that I only found this thread from 2003(!) related to this topic:

Hi, my call of optim() with the L-BFGS-B method ended with the following error message: ERROR: ABNORMAL_TERMINATION_IN_LNSRCH Further tracing shows: Line search cannot locate an adequate point after 20 function and gradient evaluations final value 0.086627 stopped after 7 iterations Could someone pls tell me whether it is possible to increase the limit of 20 evaluations? Is it even worth

Full_Name: Petr Klasterecky Version: 1.8.1 OS: Windows XP, Linux Submission from: (NULL) (195.113.27.212) The isoreg() function causes R to crash when called repeatedly. Consider the following simple script: { library(modreg) N <- 10 x <- rnorm(N) print("Original x values:") print(x) for(n in (1:N)){print(y <- isoreg(x[1:n])$yf)} } I am able to run (call) it several

Hi there, I have been having trouble running negative binomial regression (glm.nb) using library MASS in R v2.15.0 on Mac OSX. I am running multiple models on the variables influencing the group size of damselfish in coral reefs (count data). For total group size and two of my species, glm.nb is working great to deal with overdispersion in my count data. For two of my species, I am getting a

dear R-helper, I tried to find out a package in which i can have penalized maximum likelihood estimator applying on generalized extreme value distribution with beta function) but could not. would you please help me to know the name of the package. thanks for your help. S.Murshed --- r-help-request at stat.math.ethz.ch wrote: > Send R-help mailing list submissions to > r-help at