similar to: glm

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] =

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

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

This came up on r-help but indicates a bug. dnbinom(x,n,p) calls dbinom_raw(n-1,...) which returns 0 for n<1. -thomas ---------- Forwarded message ---------- Date: Thu, 08 Feb 2001 17:10:23 +0000 From: Yudi Pawitan <yudi@stat.ucc.ie> To: Mark Myatt <mark@myatt.demon.co.uk> Cc: R-Help <r-help@stat.math.ethz.ch> Subject: Re: [R] Goodness of fit to Poisson / NegBinomial

Dear all, I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have successfully fitted a logistic regression model using the "glm" function as shown below. library(rms) gusto <-

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 All, I have just estimated this model: ----------------------------------------------------------- Logistic Regression Model lrm(formula = Y ~ X16, x = T, y = T) Model Likelihood Discrimination Rank Discrim. Ratio Test Indexes Indexes Obs 82 LR chi2 5.58 R2 0.088 C 0.607 0

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

I am learning glm function, but how do you fit data using exponential distribution with glm? In the help file, under "Family Objects for Models", no ready made option seems available for the distribution as well as for other distributions satisfying GLM requirements not listed there.

Hi, I am trying to run a simple logistic regression using lrm() to calculate a odds ratio. I found a confusing output when I use summary() on the fit object which gave some OR that is totally different from simply taking exp(coefficient), see below: > dat<-read.table("dat.txt",sep='\t',header=T,row.names=NULL) > d<-datadist(dat) > options(datadist='d')

> On Sep 14, 2017, at 12:30 AM, Bonnett, Laura <L.J.Bonnett at liverpool.ac.uk> wrote: > > Dear all, > > I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk > > I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have

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 -

I'm writing functions that need to behave differently for GLMs like binomial and Poisson with fixed deviance, and those like normal or gamma or quasi where the deviance is estimated from the data. Given a glm object, is there a simple way to tell this directly, or do I have to look at the name of the family? Duncan Murdoch

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 :

In glm() you can use the summary() function to recover the shape parameter (the reciprocal of the dispersion parameter). How do you recover the scale parameter? Also, in the given example, how I estimate and save the geometric mean of the predicted values? For a simple model you can use fitted() or predicted() functions. I will appreciate any help. ? ? ? #Call required R packages require(plyr)?

Dear R-List members, I want to check if a set of measurements follows better a gamma or a lognormal distribution (see data below). Using shapiro.test I can test for normality (shapiro.test(log (Lt)). To test for gamma (and normal) distribution I would use ks.test but I need to specify its shape and scale. How should I calculate these values in R? I tried > Lt.fit <- glm(Lt ~ 1,

Fixed 'maxiter' in the help file. Thanks. Please give the original source of that dataset. That dataset is a tiny sample of GUSTO-I and not large enough to fit this model very reliably. A nomogram using the full dataset (not publicly available to my knowledge) is already available in http://biostat.mc.vanderbilt.edu/tmp/bbr.pdf Use lrm, not lrm.fit for this. Adding maxit=20 will

Dear Sir or Madam? I am a doctor of urology,and I am engaged in developing a nomogram of bladder cancer. May I ask for your help on below issue? I set up a dataset which include 317 cases. I got the Binary Logistic Regression model by SPSS.And then I try to reconstruct the model ?lrm(RECU~Complication+T.Num+T.Grade+Year+TS)? by R-Project,and try to internal validate the model through

Hi all, How is dispersion calculated within the glm function in R ? Cheers _________________________________________________________________ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body",

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