similar to: Data contamination

Hi,I am currently doing a project in which we are to investigate the size and power of three different one sample tests over three different distributions using a number of different sample sizes and values for mu1. I have written a function and was trying to get my answer for each test into the right position in an array so the output is the power of each combination of test, distribution, sample

Hi, I'm trying to use ad.test or ad2.test to test whether a given data set is exponential. I see that one of the function inputs is "distn", but I try "exponential" and 5 other variants, but I still get the error message: ad.test(test2, distn="exponential", fit=list(estimate = 0.167685), H=NA, sim=100) Error in ad.test(test2, distn = "exponential",

On Sun, 27 Aug 2023 19:54:23 +0100 John Logsdon <j.logsdon at quantex-research.com> wrote: > Not so although it did lower the gc() time to 95.84%. > > This was on a 16 core Threadripper 1950X box so I was intending to > use library parallel but I tried it on my lowly windows box that is > years old and got it down to 88.07%. Does the Windows box have the same version of R

Hi, the functions in distn.R were converted from .Internal to .External ([1], in 2012), and to .Call ([2], in 2014). They are still listed as .Internal in names.c, although they are not used in that way. Shouldn?t they be removed? There?s quite some simplification to be had, e.g., do_math3 could go away and do_math2 would be simpler. If that makes sense (I may be missing something?), I?d be

Hi, I believe I have significantly improved [dpq]wilcox functions by implementing Harding's algorithm: Harding, E.F. (1984): An Efficient, Minimal-storage Procedure for Calculating the Mann-Whitney U, Generalized U and Similar Distributions, App. Statist., 33, 1-6 Results on my computer show (against R-2.9.1): > system.time( dwilcox( 800, 800, 80) ) user system elapsed 0.240

Folks I have come across an issue with gc() hogging the processor according to Rprof. Platform is Ubuntu 20.04 all up to date R version 4.3.1 libraries: survival, MASS, gtools and openxlsx. With default gc.auto options, the profiler notes the garbage collector as self.pct 99.39%. So I have tried switching it off using options(gc.auto=Inf) in the R session before running my program using

Hi R-devels, I noticed that for the scheduled R 2.3.0, there is still no function df() for non-centrality != 0. Easter is quickly approaching, so as my little gift, I would like to offer you some code to fill this gap: After excluding some "unusual" cases, it amounts to differentiating the corresponding function pnf() --- which in turn leads to the function dnbeta() already available

I have determined why there are many "WARNING: Duplicate symbol:" messages. *** My apologies for the length *** There is a lot of detail - but I hope the detail will help R - and others - setup correct options for shared libraries. *** As I press send, I have not stopped testing (my final trial here might not even work) - but! *** the message is that shared libraries do not need to have

Hi, I've written some C code for density evaluation of the non-central t distribution. It works with the R-1.7.1 source code if placed in the src/nmath directory and after appropriate changes are made to Makefiles, to the dt function in src/library/base/R/distn.R etc. I haven't read a lot of R source code so it may need some R-ification, but I've tried to use the dt.c file as a

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

Several of R's p/d/q/r (probability/density/quantile/random.number) functions have argument names different from the ones S(-plus) uses [e.g., pf(.), rbinom(.)]. Does everyone agree that we want to become compatible here? Any volunteer for changing this in R? If you do, please give us/me a not, and please get a current snapshot of the R development version from

Dear R users: I was trying to fit a HMM with mixture of Gaussian into the dataset, and I tried to implement it by R2Winbugs. But I got the following errer. * Error in FUN(X[[1L]], ...) : .C(..): 'type' must be "real" for this format* Does anybody know what's the problem? Does R2Winbugs accept some matrix as inits? I would really appreciate your help. Thank you very much.

Conservative robust estimation methods do not appear to be currently available in the standard mixed model methods for R, where by conservative robust estimation I mean methods which work almost as well as the methods based on assumptions of normality when the assumption of normality *IS* satisfied. We are considering adding such a conservative robust estimation option for the random effects to

Craig, Thanks for your follow-up note on using the asypow package. My problem was not only constructing the "constraints" vector but, for my particular situation (Poisson regression, two groups, sample sizes of (1081,3180), I get very different results using asypow package compared to my other (home grown) approaches. library(asypow) pois.mean<-c(0.0065,0.0003) info.pois <-

Oof. :( Offhand, I can’t think of any place in particular. As one might imagine, accommodating 50+ people isn’t always super easy for places to do. Suggestions welcome! On Tue, Dec 24, 2019 at 10:27 AM Sean Silva <chisophugis at gmail.com> wrote: > It looks like Tied House will be shutting down :( Do we have a replacement > venue? > > >

Hi, I try to make an analyses to discover what is the time that an area begin to have spacial autocorrelation. And after, what is the number of individuals responsible for this autocorrelation. The main idea is to discover if exist a contamination of a quadrat from others quadrats and how is the population needed to make this contamination. This is very common to use automata to simulate

Dear list: I'm hoping to tap in to the statistical expertise in the group, especially those familiar with simulation techniques. I'm finalizing a study where I obtain standard errors from two sources. The first source is a monte carlo simulation and the other source is an analytical model I have developed that appears to recover the standard errors from the simulation. All analysis are

We'll be at Tied House as usual, starting on Thursday the 5th at 7pm! If you can, help us plan and RSVP here: https://www.meetup.com/LLVM-Bay-Area-Social/events/kncsjlyzqbhb See everyone there! -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20191204/cd110a36/attachment.html>

Dear list members, Can anyone tell me how the notches in boxplot(Y~X,notch=T) are calculated? What do these notches represent exactly? I?d suppose they are Conficence Intervals for the median, but I?ve also been told they might show Least Significant Difference (LSD) equivalents. I would very much appreciate any help from you. Best regards Chris.

Hi all, I''ve got a slight problem with using Ferret in unit tests. In order to create as little cross-contamination between test suites as possible, some of my tests are creating a fresh index per test case, and then calling Index#close and deleting the containing dir during the teardown. The problem comes when GC.start kicks in after the deleting the directory: IOError: IO Error