similar to: subset selection for glm

I need to generate 100 I.I.D samples from an exponential distribution. I use rexp(100,parameter). Is there anyway to specify a seed to determine the first input for the uniform random number generator used to generate these exponentials? -Dhiren

I have a dataset with transactions and a timestamp at which they occoured during a day. The time stamp is in the format YYYY/MM/DD hh:mm:ss. I would like to plot a timeseries of the transactions to see if there is a particular time in the day when there is a spike in transactions. Ofcourse the YYYY/MM/DD can be dropped since I am monitoring activity for the day and the actual date is

Hello: I have a matrix 'dat' with 2 columns. I have the following code: for (i in 1:nrows(dat)) { if (dat[i,1] < dat[i,2]) { dat[i,2]<-0 } else { dat[i,2]<-1 } Is there a way to accomplish this without the for loop? Thank you. -Dhiren

Hi: I am extremely new to linux so bare with the questions. I am trying to install the SJava package for R on linux (debian). I issue the R CMD INSTALL SJava_0.68-0.tar.gz and get the following error Cannot find Java. Please set your path to include the directory in which the java executable resides, or set the environment variable JAVA_HOME before this configure script is run. I have

Hello, Suppose one is interested in fitting a GLM with a log link to binomial data. How does R choose starting values for the estimation procedure? Assuming I don't supply them. Thanks, Tyler

Hello R Users, I am having difficulty deleting the last warning message in a loop so that the only warning that is produced is that from the most recent line of code. I have tried options(warn=1), rm(last.warning), and resetting the last.warning using something like: > warning("Resetting warning message") This problem has been addressed in a previous listserve string,

Hi, Currently the `gaussian' family's initialization code signals an error if any response data are zero or negative and a log link is used. Given that zero or negative response data are perfectly legitimate under the GLM fitted using `gaussian("log")', this seems a bit unsatisfactory. Might it be worth changing it? The current offending code from `gaussian' is:

I have a string '982323.1' and would like to replace everything after the '.' with a '41'. So the string should look like '982323.41'. The code I use to do this is sub('\.$','41',982323.1) This works fine as long as there is only 1 digit after the decimal. If I have '982323.10', then the result of the code is '982323.141'

I encountered a segfault running glm() and wonder if it could have something to do with the way memory is handled in a dual core system (which I just set up). I'm running R-base-2.4.0-1, installed from the SuSE 10.1 x86_64 rpm (obtained from CRAN). (My processor is an AMD Athlon 64 x2 4800+). The error and traceback are *** caught segfault *** address 0x8001326f2b, cause 'memory not

Bug summary: glm() causes a segfault if the argument 'data' is a data frame with more than 16384 rows. Bug demonstration: -------input --------------- N <- 16400 df <- data.frame(x=runif(N, min=1,max=2),y=rpois(N, 2)) glm(y ~ x, family=poisson, data=df) ------ output --------------- *** caught segfault *** address (nil),

G'day all, I had a look at the GLM code of R (1.4.1) and I believe that there are problems with the function "glm.fit" that may bite in rare circumstances. Note, I have no data set with which I ran into trouble. This report is solely based on having a look at the code. Below I append a listing of the glm.fit function as produced by my system. I have added line numbers so that I

I'm fitting some small data sets using a binomial glm with complementary log-log link. In some ill-conditioned cases I am getting convergence failures. I know how to adjust maxit and epsilon, but that doesn't seem to help. In fact I know some very good starting values for my fits, but I can't see how to get them in to glm(). How may I do this? -- Dr Murray Jorgensen

I am trying to get summary statistics from WinBUGS/JAGS output in the form of mcmc.list objects, using the summary() function. However, I get odd warning messages: Warning messages: 1: In glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : algorithm did not converge 2: In glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : algorithm did

Hi everyone: I am just wondering how can I use weights with MCMClogit function (in MCMCpack package). For example, in case of glm function as given below, there is weights option in the arguments. Aparently there is no option of using weights in MCMClogit. glm(formula, family = gaussian, data, weights, subset, na.action, start = NULL, etastart, mustart, offset, control =

Dear R-list, I'm experiencing some problems while using "strptime", which I don't understand. > str(natver) 'data.frame': 154 obs. of 8 variables: $ ID : Factor w/ 14 levels "AC","ALS","FA",..: 10 11 9 1 4 8 13 3 14 12 ... $ CdBMin : int 22 22 26 26 28 23 27 23 25 26 ... $ CdBMax : int 22 24 27 26 32 26 29 30 29 28 ... $

Hello to all, I'm a biologist trying to tackle a "fish" (Poisson Regression) which is just too big for my modest understanding of stats!!! Here goes... I want to find good literature or proper mathematical procedure to calculate a confidence interval for an inverse prediction of a Poisson regression using R. I'm currently trying to analyse a "dose-response"

# Hello R Users, # # I would like to fit a glm model with quasi family and # logistical link function, but this does not seam to work # with binary data. # # Please don't suggest to use the quasibinomial family. This # works out, but when applied to the true data, the # variance function does not seams to be # appropriate. # # I couldn't see in the # theory why this does not work. # Is

This is not so much a bug as an infelicity in the code that can easily be fixed. The initialize expression in the quasi family function is, (uniformly for all links and all variance functions): initialize <- expression({ n <- rep.int(1, nobs) mustart <- y + 0.1 * (y == 0) }) This is inappropriate (and often fails) for variance function "mu(1-mu)".

How do you view the code for a built-in R command (i.e., if I want to see what R is doing when I run a glm() statement)? Regards, Stephen [[alternative HTML version deleted]]

Dear all, we are trying to model some data from rare plants so we always have less than 50 1x1 km presences, and the total area is about 550.000 square km. So we have a real problem, when we perform a GAM, if we consider only the same amount of absences than presences. We have thought to use a greater number of absences but in this case we shoud downweight them. Does anybody know how to use the