similar to: Inverse Gaussian distribution

Two questions: 1. inverse.gaussian is up there as one of the glm families, but do people ever use it? There is no inverse.gaussian in the R distribution family, and when I checked McCullagh & Nelder, it only appeared twice in the book (according to subject index), once in the table on p. 30 and once on p. 38 in a passing sentence. Is there a good reference on this distribution? 2. When I

Dear R-people Does someone have a routine to ngenerate inverse-gaussian random numbers. I am thinking of something similar to rinvgauss, pinvgauss etc. in S-plus. best regards Helgi -- Helgi Tomasson FAX: 354-552-6806 University of Iceland PHONE:354-525-4571 Faculty of Economics and Business Administration

Hello, I'm Emanuela, I'm implemented a survival analysis and I'm trying to use a frailty model with inverse gaussian distribution, but I'm not able to find the right code, because it seems to be only a gamma and a gaussian distribution. Is there also the inverse gaussian distribution? Thanks a lot Emanuela Rossi

Hello everybody, I have encountered a problem with the inverse Gaussian distribution. It is very likely that it will not work regardless of the data input. I have programmed this regression and it works fine no matter which distribution the response comes from. If you run this example (first tried and already got the error) set.seed(1234) y = abs( rnorm(150) ) glm( y ~ ., iris, family =

Dear, I want to fit an inverse gaussion distribution to a data set. The predictor variables are gender, area and agecategory. For each of these variables I've defined a baseline e.g. #agecat: baseline is 3 data<-transform(data, agecat=C(factor(agecat,ordered=TRUE), contr.treatment(n=6,base=3))) The variable 'area' goes from A to F (6 areas: A,B,C,D,E,F) How can i

Dear R Help Please forgive my lack of knowledge,.I would be very thankful for some help. Here is my problem: I was using optim to estimate parameters of a model and I get this error message "Error in optim(x0, fn = riskll, method = "L-BFGS-B", lower = lbs, upper = ubs, : L-BFGS-B needs finite values of 'fn'" Below is the R code I have written.

I have a one-variable data set in R. The plot of histogram of my numerical variable suggests an inverse gaussian distribution. How can I obtain best estimation for the two parameters of inverse gaussian based on my data? Thanks. -- View this message in context: http://n4.nabble.com/estimate-inverse-gaussian-in-R-tp949692p949692.html Sent from the R help mailing list archive at Nabble.com.

For a simulation study, I need to fit a GEE with a IG distribution and using a log link function. As far as I know, there are two GEE packages available ('gee' and 'geepack') but none of them supports IG. I've also tried using family=quasi("log","mu^3") (without luck!).' Any guidance is highly appreciated Cheers, Ren?

Hi, sorry for this lengthy post. I am using R-2.0.0 on Unix, compiled. Overview: A) R CMD check: Unaccounted top-level text B) In which sections of *.Rd is LaTeX notation allowed, where *not*? C) Codoc mismatches and polyvalent parameters D) Successful R CMD INSTALL and R CMD build E) cp ../Rd.sty . is copy necessary? F) latex ./CWHstat-manual.tex runs into trouble When running >R CMD

Hi, sorry for this lengthy post. I am using R-2.0.0 on Unix, compiled. Overview: A) R CMD check: Unaccounted top-level text B) In which sections of *.Rd is LaTeX notation allowed, where *not*? C) Codoc mismatches and polyvalent parameters D) Successful R CMD INSTALL and R CMD build E) cp ../Rd.sty . is copy necessary? F) latex ./CWHstat-manual.tex runs into trouble When running >R CMD

Hi all, I am seeing a strange problem with Asterisk queue. I am not sure if it's my configuration which is wrong or there's something with Asterisk. I am using Asterisk 1.4.2 and i have a queue with one MGCP member. When i tried to call the extension number directing to the queue, the MGCP phone is not ringing. However, it is fine to call the MGCP phone directly. The strange thing is

http://r.789695.n4.nabble.com/file/n3216865/Inverse_Gamma.png Hello, I am seeking help in estimating the parameters of an inverse gamma distribution (from the 'actuar' package) using a function like 'fitdistr'. Unfortunately I haven't found such a package using findFn('fit Inverse Gamma') from the 'sos' package and was therefore hoping someone might be aware

I'm trying to configure Chan_H323 to register with GnuGK... without success... i've failed finding sample configurations. I'd greatly appreciate anyone who can provide sample config of H323.conf and gnugk.ini I am tyring to configure Asterisk as a neighbor in GnuGK. I'm always getting this error on Asterisk. ERROR[16384]: chan_h323.c:1987 load_module: Gatekeeper registration

No functional modification involved. drivers/gpu/drm/nouveau/nvkm/subdev/gsp/r535.c:2174: warning: expecting prototype for create_debufgs(). Prototype was for create_debugfs() instead. Reported-by: Abaci Robot <abaci at linux.alibaba.com> Closes: https://bugzilla.openanolis.cn/show_bug.cgi?id=12292 Signed-off-by: Jiapeng Chong <jiapeng.chong at linux.alibaba.com> ---

Hi, I followed the example in "Writing R Extensions" to create a shared object in Windows, using the command R CMD SHLIB X.cc X_main.cc This was encountered: ../src/gnuwin32/MkRules:155: warning: overriding commands for target '.c.d' ../src/gnuwin32/MkRules:143: warning: ignoring old commands for target '.c.d' ../src/gnuwin32/MkRules:171: warning: overriding commands

It was a real surprise, but a student in my class found that the function eigen is buggy. He traced to the problem from his inability of getting principal component analysis to work on his data. Chong Gu Here is a matrix I generated through X'X, where X is 2x3. > jj [,1] [,2] [,3] [1,] 0.8288469 -1.269783 -0.7533517 [2,] -1.2697829 2.162132 2.0262917 [3,]

Dear R-Helpers, I am hoping to perform survival analyses using the "ex-Gaussian" distribution. I understand that the ex-Gaussian is a convolution of exponential and Gaussian distributions for survival data. I checked the "survreg.distributions" help and saw that it is possible to mix pre-defined distributions. Am I correct to think that the following code makes the

Hi, I have a data set (weight) that does not follow the Gaussian (Normal) distribution. However, I have to transform the data before applying the Gaussian distribution. I used this syntax and used log(weight) as: posJy.model<-glm(log(weight) ~ factor(pos), family=gaussian(link='identity'), subset=Soil=="Jy"). This syntax COULD NOT transform the data. But if I transform the

Hello, I know that Mclust does the fitting on its own but I am trying to implement an optimization with the aim to generate a the mixture gaussian with the combine moments as closed as possible to the moment of my return distribution. The objective is to Min Abs((Mean Ret - MeanFit)/Mean Fit) + Abs((Std Ret -Stdev Fit)/Stdev) + Abs((Sk Ret-Sk fit)/Sk Fit) + Abs((Kurt Ret- Kurt Fit)) Taking

I am using R to teach, and here are a couple of things that I thought would work didn't work. 1. I noticed the utility data(***,package=***) recently and like it very much, but unless I type in the whole word "package" I'll get an error in 1.4.0. For example, data(cats,package=MASS) works fine but data(cats,pac=MASS) doesn't. 2. drop1 doesn't seem to be as smart as