similar to: different results with plot.glm vs. plot.glm(which=c(2))

I am running GLM models using the gamma family. For example: model <-glm(y ~ x, family=Gamma(link="identity")) I am getting different results for the normal Q-Q plot and the Scale-Location plot if I run the diagnostic plots without specifying the plot vs. if I specify the plot ... e.g., "plot(model)" gives me a different Normal Q-Q graph than "plot(model,

Dear friends, I used R and SAS to analyze my data through generalized linear model, and there is some difference between them. Results from R: glm(formula = snail ~ grass + gheight + humidity + altitude + soiltemr + airtemr, family = Gamma) Deviance Residuals: Min 1Q Median 3Q Max -1.23873 -0.41123 -0.08703 0.24339 1.21435 Coefficients:

Greetings, everybody. Can I ask some glm questions? 1. How do you find out -2*lnL(saturated model)? In the output from glm, I find: Null deviance: which I think is -2[lnL(null) - lnL(saturated)] Residual deviance: -2[lnL(fitted) - lnL(saturated)] The Null model is the one that includes the constant only (plus offset if specified). Right? I can use the Null and Residual deviance to

Hi - I am fitting a probit model using glm(), and the deviance and residual degrees of freedom are different depending on whether I use a binary response vector of length 80 or a two-column matrix response (10 rows) with the number of success and failures in each column. I would think that these would be just two different ways of specifying the same model, but this does not appear to be the case.

Dear all, I am using the bigglm package to fit a few GLM's to a large dataset (3 million rows, 6 columns). While trying to fit a Poisson GLM I noticed that the coefficient estimates were very different from what I obtained when estimating the model on a smaller dataset using glm(), I wrote a very basic toy example to compare the results of bigglm() against a glm() call. Consider the

I'm confused as to the trustworthiness of the dispersion parameters reported by glm. Any help or advice would be greatly appreciated. Context: I'm interested in using a fitted GLM to make some predictions. Along with the predicted values, I'd also like to have estimates of variance for each of those predictions. For a Gamma-family model, I believe this can be done as Var[y] =

Hi Sir, Thanks for making package available to us. I am facing few problems if you can give some hints: Problem-1: The model summary and residual deviance matched (in the mail below) but I didn't understand why AIC is still different. > AIC(m1) [1] 532965 > AIC(m1big_longer) [1] 101442.9 Problem-2: chunksize argument is there in bigglm but not in biglm, consequently,

Dear R-Help I 'm using GLM function to Modelling. But when I used Gamma Family in GLM, then I can't run. It was error > glm(DamageRatio~MinTEMP+MaxTEMP+DayRain+Group1+Group2+Group3+Year,family=Gamma()) Error in eval(expr, envir, enclos) : Non-positive values not allowed for the gamma family Can Gamma Distribution use data begin 0 ? and then when I used GLM in S-Plus Program then

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, I have found a problem with using the glm function with a gamma family. I have a vector of data, assumed to be generated by a gamma distribution. The parameters of this gamma distribution are estimated in two ways (i) using the glm() function, (ii) "by hand", using the optim() function. I find that the -2*likelihood at the maximum found by (i) is substantially larger than that

I've downloaded R for windows (9.0.1) and it is great! I've converted all my lecture notes for my GLM course to run on R (they are available on my web page below). I must admit I particularly like the default contrast options, which are identical to GLIM. Also I like the gl function - very useful! I have a couple of questions/bugs: 1. predict.glm doesn't work, but predict.lm does -

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

Members of this mailing list may be interested in the following new book: Beginner's Guide to GLM and GLMM with R. - A frequentist and Bayesian perspective for ecologists - Zuur AF, Hilbe JM and Ieno EN This book is only available from: http://www.highstat.com/BGGLM.htm This book presents Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) based on both

Hi, I am fitting two models, a generalized linear model and a generalized additive model, to the same data. The R-Help tells that "A generalized additive model (GAM) is a generalized linear model (GLM) in which the linear predictor is given by a user specified sum of smooth functions of the covariates plus a conventional parametric component of the linear predictor." I am fitting the GAM

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)+

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

Hello, My response variable seems to be distributed according to Student t with df=4. I have 320 observations and about 20 variables. I am wondering whether there is a way to fit glm with Student t for error distribution. Student t is not one of the family choices in glm function. How should I proceed to fit glm with Student t? I know that Student t is the Inverse Gamma with shape parameter

On 27/04/2018 9:25 AM, Hadley Wickham wrote: > Hi all, > > Very surprising (to me!) and mystifying result from predict.glm(): the > predictions vary depending on whether or not I use ns() or > splines::ns(). Reprex follows: > > library(splines) > > set.seed(12345) > dat <- data.frame(claim = rbinom(1000, 1, 0.5)) > mns <- c(3.4, 3.6) > sds <- c(0.24,

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

I've read something about this problem, but I don't know how can i avoid this problem. Why the order of the factors give different results? I suppose it's because the order of the factors, i've just changed "lcc" from the first position to the last in the model, and the significance change completely >