similar to: power and sample size for a GLM with poisson response variable

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 <-

Dear list, Is anyone aware of a library for sample size calculation in R, similar to NQuery? I have to give a course in this area, and I would like to enable the students playing around with this. Best wishes, Matthias

we have some initial data from our field sampling. From the means and variances, we see your sampling field is much heterogeneous (not uniform). We are looking for a "R" program for sampling size determination. Right now, we are not good enough to write a whole "R" program, so please help. [[alternative HTML version deleted]]

I am trying to do power calculations for the proportional odds model using the asypow library. The code noncenta90b10<-asypow.noncent(theta.ha=a9010,info.mat=infomatrixa90b10,constraints=constrt) returns Error in max(..., na.rm = na.rm) : invalid "mode" of argument. the various arguments I've used are: a9010 [,1] [1,] -1.7357568 [2,] -0.1928619 specifying the

Hola! I got past the problems I asked about two days ago, thanks. No I am updating CRAN package asypow (the daily package check on CRAN gave warnings due to .Rd problems, fixed). Now it PASSED Rcmd check (WindowsXP home edition, rw2000dev, on a new toshiba laptop, if that matters.) but then Rcmd build --binary gives problems: . . . preparing package asypow for lazy loading Error in

Hi all I am beginning in asterisk and am making tests with an ata-186. For the time being the tests are going well, however have a doubt. I am thinking about using a canal e1 with plate digium. Assuming that the company of telecommunications supplies e1 with 30 canals and numeration to me 4000-0001 4000-0029. she is possible to configure asterisk in way that somebody of is dials 4000-0025, to

This is the beginnings of HDMI 2.0 support. All of the "extra" features are left out, such as 12/16bpc, YUV420, etc. I've verified that with this code, a GP108 (GT1030) can switch between 4k at 60 and 1920x1080 at 60 on a LG 4K TV. Further, I've verified via i2c tools, that the SCDC writes really do happen. I suspect that the patch for keeping track of the high-speed TMDS

Hi, My name is Tom Faulhaber. I am the network administrator at a small company in Providence,RI. I am running Samba 2.2.2 on a Redhat 6.1 machine on a Novell 4.21 network. The workstations are Win98SE. The Linux box is being used for the company intranet (Apache 1.3.22). Everything runs fine, however I am completely stumped on one issue (so far). That issue is: network connectivity. If I

Hi all, a problem I encounter again and again: I plot a zoo object using "plot" and then want to add lines or points to this plot. I usually circumvent this problem by adding artificial coloumns to the zoo object before plotting, but I am sure there's a better solution. To be specific: Assume I did x <-

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

Dear all, I am a new user in r and I am facing some problems with the quantile regression specification. I have two matrix (mresultb and mresultx) with nrow=1000 and ncol=nsim, where I specify (let's say) nsim=10. Hence, the columns in my matrix represents each simulation of a determined variable. I need to regress each column of mresultb on mresultx. My codes are the following:

Dear all, I have just sent an email with my problem, but I think no one can see the red part, beacuse it is black. So, i am writing again the codes: rm(list=ls()) #remove almost everything in the memory set.seed(180185) nsim <- 10 mresultx <- matrix(-99, nrow=1000, ncol=nsim) mresultb <- matrix(-99, nrow=1000, ncol=nsim) N <- 200 I <- 5 taus <- c(0.480:0.520) h <-

Running the function below, tested using the cardiff dataset from splancs generates the following error. What changes do I need to make to get the function to work? Thanks. --Dale > gen.rpoints(events, poly, 99) > rpoints Error: object "rpoints" not found # test spatial data library(splancs) data(cardiff) attach(cardiff) str(cardiff) events <- as.points(x,y) ###

Dear all, I am trying to run a loop in my codes, but the software returns an error: "subscript out of bounds" I dont understand exactly why this is happenning. My codes are the following: rm(list=ls()) #remove almost everything in the memory set.seed(180185) nsim <- 10 mresultx <- matrix(-99, nrow=1000, ncol=nsim) mresultb <- matrix(-99, nrow=1000, ncol=nsim) N

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

I?m working on an R-implementation of the simulation-based finite-sample null-distribution of (R)LR-Test in Mixed Models (i.e. testing for Var(RandomEffect)=0) derived by C. M. Crainiceanu and D. Ruppert. I'm in the beginning stages of this project and while comparing quick and dirty grid-search-methods and more exact optim()/optimize()-based methods to find the maximum of a part of the

Sorry, if this question is rather basic, but being a newbie I still have problems to think in the "R way". My problem is as follows: - I have a data frame X with stock prices X$Price and corresponding dates X$Date. - Now I want to get for each date x in X$Date the index z, such that z = min (a | X$Date(a)>x and X$Price(a)>Price(x) To put it simple, I am looking for the

Greetings, I am interested in using a generalized linear mixed model with data that best fits a beta distribution (i.e., the data is bounded between 0 and 1 but is not binomial). I noticed that the beta distribution is not listed as an option in the "family objects" for glmmPQL or lmer. I found a thread on this listserve from 2006 ("[R] lmer and a response that is a

## Package Needed library(fields) ## Assumptions set.seed(123) nsim<-5 p<-2 ## Generate Random Matrix G G <- matrix(runif(p*nsim),nsim,p) ## Set Empty Matraces dmax and dmin dmax<- matrix(data=NA,nrow=nsim,ncol=p) dmin<- matrix(data=NA,nrow=nsim,ncol=p) ## Loop to Fill dmax and dmin for(i in 1:nsim) { dmax[i]<- max(rdist(G[i,,drop=FALSE],G)) dmin[i]<-

Dear all, Does anyone have any idea on how to speed up the for() loop below. Currently it takes approximately 2 minutes and 30 seconds. Because of the size of Nsim and N, simulating a multivariate normal (instead of simulating Nsim times a vector of N normal distributions) would require too much memory space. Many thanks for your kind help, Simona N=3000 PD=runif(N,0,1) cutoff.=qnorm(PD)