similar to: Inverse Gaussian distribution not working in glm funciton

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

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

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.

I needed to generate some data from the Inverse Gaussian distribution but it is not in R. A google search took me to http://www.maths.uq.edu.au/~gks/s/invgauss.html which contains the documentation for the d, p, r, q functions with a download link of the functions. The functions work fine in R. I am curious why the facilities are not included in R. Is it for legal reasons or just the

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

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.

Dear all, I have the following dataset: each row corresponds to count of forest floor small mammal captured in a plot and vegetation characteristics measured at that plot > sotr plot cnt herbc herbht 1 1A1 0 37.08 53.54 2 1A3 1 36.27 26.67 3 1A5 0 32.50 30.62 4 1A7 0 56.54 45.63 5 1B2 0 41.66 38.13 6 1B4 0 32.08 37.79 7 1B6 0 33.71 30.62

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:

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?

Hello - I am fairly new to R, (i.e., ability to create functions/write programs insignificant) and was wondering if there might be a convenient way to model the following: I want to fit a gaussian glm to grouped data, while allowing for unequal variances in each of the groups. More specifically, my data set looks something like this: ---------------- data group 1 76 1 2 82 1 3

Dear all, I am using the book "Generalized Linera Models and Extension" by Hardin and Hilbe (second edition, 2007) at the moment. The authors suggest that instead of OLS models, "the log link is generally used for response data that take only positive values on the continuous scale". Of course they also suggest residual plots to check whether a "normal" linera model

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

Dear R team, I don't get what I think I should get when using anova.glm() with family="gaussian" -- please ignore this and forgive me if this turns out to be another example of a fundamental misunderstanding on my part (a highly likely event!) For example: S <- as.factor(rep(c(rep("m",2),rep("f",2)),2)) A <-

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

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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

Hi all, Has any of you implemented code for non-negative matrix factorization to solve Y=T P' +E; dim(Y)=n,p ; dim(T)=n,nc; dim (P)=(p,nc); dim(E)=n,p where T and P must be non-negative and E either Gaussian or Poisson noise. I'm looking for two variants: 1. Easy (I think), T is known (that is we just want to solve the general inverse problem) 2. Harder (?), T is unknown (under some

Is the following difference between S+ and R normal or is it a bug? Thanks. FC. PS: S+ 4.5 for Windows and R 0.63.1 for Windows. S-PLUS output: > inverse.gaussian()$deriv > function(mu) > -2/mu^3 R output: > inverse.gaussian()$deriv > NULL -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi, I have a sample of size 100 from a function in interval [0,1] which can be assumed to come from a scalar-valued stationary Gaussian process. There are about 500 observation points in the interval. I need an effective and fast way to simulate from the Gaussian process conditioned on the available data. I can of course estimate the mean and 500x500 covariance matrix from data. I have searched