similar to: glm error message when using family Gamma(link=inverse)

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+ +

Dear all, I am new in R and would like to ask for someone's help in understanding where I go wrong with the following code: rm(list=ls()) # Required packages library(MCMCpack) # Simulated data set.seed(1) data = rinvgamma(n=250, shape = 5, scale = 2) + 2 hist(data) # log-likelihood ll = function(par){ if(par[1]>0 & par[2]>0 & par[3]<min(data)) return(

FWIW p. 206 of the White Book gives the following for names(binomial()): family, names, link, inverse, deriv, initialize, variance, deviance, weight. So $aic wasn't there In The Beginning. I haven't done any more archaeology to try to figure out when/by whom it was first introduced ... Section 6.3.3, on extending families, doesn't give any other relevant info. A patch for

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: >

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 :

Dear R-Users, I wish to create a new family object based on the Binomial family. The only difference will be with the link function. Thus instead if using the 'logit(u)' link function, i plan to use '-log(i-u)'. So far, i have tried to write the function following that of the Binomial and Negative Binomial families. The major problem i have here is with the definition of the

In the iterative weighted least squares loop of the glm.fit function, a QR decomposition of the matrix X*sqrt(w) is obtained at each iteration, using fitted values of the mean response mu from the previous interation. Here "X" is the design matrix, w = wt*(d(mu)/d(eta))/variance(mu), "wt" is the vector of weights, and "d(mu)/d(eta)" is the derivative of mu wrt the

In R, family has aic component since version 0.62. There is no aic component in family in R 0.61.3. Looking at blame, https://github.com/wch/r-source/blame/tags/R-0-62/src/library/base/R/family.R , aic component in family is introduced in svn revision 640 (https://github.com/wch/r-source/commit/ac666741679b50bb1dfb5ce631717b375119f6ab): using aic(.) [Jim Lindsey]; use switch() rather than many if

Hi all I am converting an S-Plus function into R. The S-Plus code uses some of the glm families, and family objects. The family objects in S-Plus and R have many different features, for example: In R: > names(Gamma()) [1] "family" "link" "linkfun" "linkinv" "variance" [6] "dev.resids" "aic"

Hello, I've been attempting to fit the data below with an inverse gamma distribution. The reason for this is outside proprietary software (@Risk) kicked back a Pearson5 (inverse gamma) as the best fitting distribution with a Chi-Sqr goodness-of-fit roughly 40% better than with a log-normal fit. Looking up "Inverse gamma" on this forum led me the following post:

Hi Folks! Just started using the gamlss package and I tried a simple code example (see below). Why the negative sigma? John > y <- rt(100, df=1)> m1<-fitDist(y, type="realline")Warning messages:1: In MLE(ll3, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma, : possible convergence problem: optim gave code=1 false convergence (8)2: In MLE(ll4, start = list(eta.mu =

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

Is it generally known/has it been previously discussed here that the $aic() component in GLM-family objects (e.g. results of binomial(), poisson(), etc.) does not as implemented actually return the AIC, but rather -2*log-likelihood + 2*(model_has_scale_parameter) ? Can anyone in this forum gauge how a documentation patch would be received? This behaviour does not seem to be documented in ?family

Hi, All: assume I need to generate X from inverse gamma with parameter (k, beta). should I generate from Y from gamma(-k, beta), then take X=1/Y? Thanks pat

Hello I am trying to fit a model using glm function with family=Gamma(link="inverse"). Is it possible to give initial values, and how? I tried to search for info but not managed to get any answer. Kind regards and thanks in advance. Rosario [[alternative HTML version deleted]]

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

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 How can generate a sample from truncated inverse gamma distribution in R? thanks

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, I have a quick question regarding estimation of a truncation regression model (truncated above at 1) using MLE in R. I will be most grateful to you if you can help me out. The model is linear and the relationship is "dhat = bhat0+Z*bhat+e", where dhat is the dependent variable >0 and upper truncated at 1; bhat0 is the intercept; Z is the independent variable and is a uniform