similar to: GLM Gamma Regression error message in R

Hi there Thank you for everyone's help in all my previous questions. By way of intro, I am a masters student in actuarial science at the University of Cape Town, and I am doing a project in R on some healthcare cost data. Just for clarity before I embark on further research may I please ask the following. I want to take the direction of modelling healh insurance claims data with Tweedie

Dear Sir/Madam I'm trying quite a long shot here... I have used R previously, and would just like to know if any software/model has been designed which will allow me to project the future risk profile of a group of insured lives (by factors such as age, gender etc.), based on past and exisiting risk profiles of the covered lives? If you could point me in the direction of a similar project or

Dear R users, I use the glm() function to fit a generalized linear model with gamma distribution function and log link. I have read in the help page that the default method used by R is "glm.fit" (iteratively reweighted least squares, IWLS). Is it possible to use maximum likelihood method? Thanks Silvia Narduzzi Dipartimento di Epidemiologia ASL RM E Via di S. Costanza, 53 00198

Hi All, I am running a generalized linear model with gamma distribution in R (glm, family=gamma ) for my data (gene expression as response variable and few predictors). I want to calculate r-squared for this model. I have been reading online about it and found there are multiple formulas for calculating R2 (psuedo) for glm (in R) with gaussian (r2 from linear model), logistic regression

Dear R users, Which test is most appropriate in glm when the family is Gamma? In the help page of anova.glm, I found the following ?For models with known dispersion (e.g., binomial and Poisson fits) the chi-squared test is most appropriate, and for those with dispersion estimated by moments (e.g., gaussian, quasibinomial and quasipoisson fits) the F test is most appropriate.? My questions :

Hello all, I was wondering if someone can enlighten me as to the difference between the logLik in R vis-a-vis Stata for a GLM model with the gamma family. Stata calculates the loglikelihood of the model as (in R notation) some equivalent function of -1/scale * sum(Y/mu+log(mu)+(scale-1)*log(Y)+log(scale)+scale*lgamma(1/scale)) where scale (or dispersion) = 1, Y = the response variable, and mu

Hi All, could someone shed some light on what the difference between the estimated dispersion parameter that is supplied with the GLM function and the one that the 'gamma.dispersion( )' function in the MASS library gives? And is there consensus for which estimated value to use? It seems that the dispersion parameter that comes with the summary command for a GLM with a Gamma dist. is

Hi, I couldn't find a package to fit a penalized (lasso/ridge) Gamma regression model. Does anybody know any? Thanks in advance, Lars. [[alternative HTML version deleted]]

I would like the option to specify alternative scale parameters when using the gamma family, log link glm. In particular I would like the option to specify any of the following: 1. maximum likelihood estimate 2. moment estimator/Pearson's 3. total deviance estimator Is this easy? Possible? In addition, I would like to know what estimation process (maximum likelihood?) R is using to

Dear list, I am looking for a way to fix the scale parameter when fitting a generalized linear model with gamma errors and log link. Is there something like "SCALE" such as in GLIM? As always thanks a lot. Peter

In glm, is there a way to specify the family to be exponential, or Gamma but with alpha=1? This is required by Dobson Exercise 4.3d, page 48. But without specifying alpha=1, the asnwer is the same as in the book. If reply to the list, please cc me a copy. Thanks. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

R 2.5 windows XP I am getting an error from glm() that I don't understand. Any help or suggestions would be appreciated. N.B. 1<=AAMTCAREJ<=327900 > summary(data$AAMTCAREJ) Min. 1st Qu. Median Mean 3rd Qu. Max. 1.0 404.3 1430.0 6567.0 5457.0 327900.0 > fitglm<-glm(AAMTCAREJ~sexcat+H_AGE+SmokeCat+InsuranceCat+MedicadeCat+ +

John, You have specified a model with E(y) = 1/eta where eta = X beta is the linear predictor and E(y) must be >0, since the family is Gamma and you have a lot of covariates in the model. glm now has to try to find a best linear predictor, but under the constraint that eta>0 for every single one of the observations (the log-likelihood involves a log(eta) term). The internal

Hi, we have this model glm(CPUE~Year+Month+Length+Year:Month, family=Gamma (link="inverse")) where, CPUE is a numerical variable >0, We use this model for standardization of CPUE and we need the new estimates for each year. How can we obtain it? Thank you

Hi All, could someone please shed some light on the proper goodness-of-fit analysis for the GLM output based on Gamma distributions with the log-link? My objective is to test the goodness-of-fit for the final model (and not the comparison of nested models). In particular, should the 'Residual Deviance' be compared with the Chi-Square distribution, or should the 'Scaled Residual

Hi, I have a set of data (total number of record = 144,122), and I would like to use gamma-glm with log link to set up a model. IC is number of records IL is paid amount The table below shows that I have 30.578% of the data in the level of "1 - 1000" paid amount 20.320% of the data in the level of "1001 - 2000" paid amount and so on My question is could i use the whole data

Dear users; I am doing GLMs with the Gamma distribution, and I always get errors ("no valid set of coefficients: please supply starting values") or warnings ("NaNs produced in log(x)") when I use the links identity or inverse, but I don´t get them if I use the log link. For example: >

Quick question about the usage of glht. I'm working with a data set from an experiment where the response is bounded at 0 whose variance increases with the mean, and is continuous. A Gamma error distribution with a log link seemed like the logical choice, and so I've modeled it as such. However, when I use glht to look for differences between groups, I get significant

Dear R-users! I would like to fit exponential, Weibull and log-logistic via glm() like functions. Does anyone know a way to do this? Bellow is a bit longer description of my problem. Hm, could family() be adjusted/improved/added to allow for these distributions? SAS procedure GENMOD alows to specify deviance and variance functions to help in such cases. I have not tried that option and I do not

R 2.8.1 windows XP I am getting an error message that I don't understand when I try to run GLM. The error only occurs when I have all independent variables in the model. When I drop one independent variable, the model runs fine. Can anyone help me understand what the error means and how I can correct it? Thank you, John > fit11<-glm(AAMTCARE~BMI+BMIsq+SEX+jPHI+jMEDICAID+factor(AgeCat)+