similar to: GLM problem

I would like to do an R glm() with family = binomial(link="loglog") Right now, the cloglog link exists, which is nice when the data have a heavy tail to the left. I have the opposite case and the loglog link is what I need. Can someone suggest how to add the loglog link onto glm()? It would be lovely to have it there by default, and it certainly makes sense to have the two opposite

I am trying to figure out how to apply a loglog link to a binomial model (dichotomous response variable with far more zeros than ones). I am aware that there are several relevant posts on this list, but I am afraid I need a little more help. The two suggested approaches seem to be: 1) modify the make.link function in GLM, or 2) use the loglog or cloglog functions in the VGAM package.

Hello, I have different results from these two softwares for a simple binomial GLM problem. >From Genmod in SAS: LogLikelihood=-4.75, coeff(intercept)=-3.59, coeff(x)=0.95 >From glm in R: LogLikelihood=-0.94, coeff(intercept)=-3.99, coeff(x)=1.36 Is there anyone tell me what I did wrong? Here are the code and results, 1) SAS Genmod: % r: # of failure % k: size of a risk set data

Hello, I'm not a Stata user so I'm trying to reproduce Stata results that are given to me in R. I would like to use a GLM with a complementary log-log function. The stata code I have is: glm c IndA fia, family(binomial s) link(cloglog) offset(offset) The R code is: glmt <- glm(data=dataset, c ~ IndA + fia, offset = offset, family = binomial(link = cloglog)) Which yields

Hi, I would like apply different link functions from Aranda-Ordaz (1981) family to large binary dataset (n = 2000). The existing links in glm for binomial data (logit, probit, cloglog) are not adequate for my data, and I need to test some other transformations. Is it possible to do this in R? And how? Thank you for your help, /Sharon

Just a quick point which may be easy to correct. Whilst typing the wrong thing into R 2.2.1, I noticed the following error messages, which seem to have some stray quotation marks and commas in the list of available families. Perhaps they have been corrected in the latest version (sorry, I don't want to upgrade yet, but it should be easy to check)? > glm(1 ~ 2,

Dear list, I'm trying to mimic the analysis of Wedderburn (1974) as cited by McCullagh and Nelder (1989) on p.328-332. This is the leaf-blotch on barley example, and the data is available in the `faraway' package. Wedderburn suggested using the variance function mu^2(1-mu)^2. This variance function isn't readily available in R's `quasi' family object, but it seems to me

I am creating a model attempting to predict the probability someone will reoffend after being caught for a crime. There are seven total inputs and I planned on using a logistic regression. I started with a null deviance of 182.91 and ended up with a residual deviance of 83.40 after accounting for different interactions and such. However, I realized after that my code is different from that in

Hi Folks, I am interested in extending the repertoire of link functions in glm(Y~X, family=binomial(link=...)) to include a "tan" link: eta = (4/pi)*tan(mu) i.e. this link bears the same relation to the Cauchy distribution as the probit link bears to the Gaussian. I'm interested in sage advice about this from people who know their way aroung glm. >From the surface, it looks

Hi Folks, I'm seeking confirmation of something which is probably true but which I have not managed to find in the documentation. I have a binary response y={0.1} and a variable x and have fitted a probit response to the data with f <- glm( y~x, family=binomial(link=probit) ) and then, with a specified set of x-value X I have used the predict.glm function as p <- predict( f, X,

I am running 9 negative binomial regressions with count data. The nine models use 9 different dependent variables - items of a clinical screening instrument - and use the same set of 5 predictors. Goal is to find out whether these predictors have differential effects on the items. Due to various reasons, one being that I want to avoid overfitting models, I need to employ identical types of

-- Error in binomial(link = "identity") : identity link not available for binomial family, available links are "logit", "probbit", "cloglog" and "log" Hi, I have a question, dealing with this error response. I'm trying to make anova on percentages. The variablethat has a biological significance is actually the percentage itself. Is it

I'm attempting to do model selection with AIC, using a glm and a lognormal distribution, but: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian(link="log")) ## gives the same result as either of the following: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian) fit1<-lm(BA~Year,data=pdat.sp1.65.04) fit1 #Coefficients: #(Intercept) Year2004 # -1.6341

Hi everyone, I have a problem with running a glm using a subset of my data. Whenever I choose a subset, in the summary the factors arent shown (as if the variable was a continuous variable). If I dont use subsets then all the factors are shown. I have copied the output from summary for both cases. Thanks for the help, Muri > model<-glm(log(cpue)~year,family=gaussian) Call: glm(formula =

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

Full_Name: Finn Knudsen Version: 1.8.0 OS: windows 2000 Submission from: (NULL) (194.192.22.33) The problem seems to happen when running the GLM. When both multiplicative effects and an offset is present. I experienced this problem on my own dataset when using af Poisson familiy with log link function but the behaviour can be reproduced with the following code. I do not know if it is a bug, but

Hi, I'm a very new user of R and I hope not to be too "basic" (I tried to find the answer to my questions by other ways but I was not able to). I have 12 response variables (species growth rates) and two environmental factors that I want to test to find out a possible relation. The sample size is quite small: (7<n<12, depending on each species-case). I performed a

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

Hello, I am try to perform a modeling which is relevant in a strongly heteroscedastic context. So I perform a dual modeling (modeling of both mean and variance of a response) in using the following loop: jointmod <- function(formula, data, itercrit=10,devcrit=0.0001) { # # Init step # init <- glm(formula=formula,family=gaussian, data=data) response <-

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