similar to: GLM, how to get an R2 to explain how much of data explained by one variable?

Hi Celine, GLM outputs usually give the null deviance and residual deviance in the summary() term - so you can work out % deviance explained for a variable/model from this. Hope this helps. Best wishes, Clare ---------------------------- Dr Clare B Embling Visiting Research Fellow Marine Institute University of Plymouth Plymouth, UK.

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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:

I imitated predict.glm, my thing worked, now I need to revise. It would help me very much if someone would explain predict.glm line 28, which says object$na.action <- NULL # kill this for predict.lm calls I want to know 1) why does it set the object$na.action to NULL 2) what does the comment after mean? Maybe I need a pass by value lesson too, because I can't see how changing that

Dear all, I have question regarding GLMs: I have a discrete response variable and a continuous explaining variable. Like this: http://www.myimg.de/?img=example1db0f.jpg I want to use a GLM to investigate. I have to specify the "familiy of the distribution of the response variable" - or, maybe more precise, the "family of the distribution of the residuals of the response

I have used packages rpart, mvpart and tree for classification and regression trees. I want to calculate fraction of null deviance explained by each node and variable in the tree. For instance, at the first split, this would be (1 - (sum of residual deviance in each of the two leaves)/deviance at the root). In the subsequent splits, this formula is slightly different. There probably is a function

Folks, I am looking for a means for calculating the 1-R^2 ratio for variable selection to mimic the values of PROC VARCLUS in SAS. While there may be better methods for variable selection, we are trying to duplicate published results at this time. To date, I have been unable to find a way to obtain this value from Hmisc::varclus , stats::hclust and clustvarsel::clustvarsel. Can any of you give

Here is our configuration - we have a Windows 2003 domain I'll call 'corporate' and a Windows 2008 domain I'll call 'lab'. There is a one-way trust (users can log into 'lab' machines using 'corporate' creds). We have a linux box running samba that is a member of 'lab'. Users log into their Windows machines using a 'corporate' credential and

Hi! When I use the glm.cv function I get a value called "delta" which is explained as the "raw cross-validation estimate of prediction error". I recently found a formula for that term in literature where it is defined as: alpha = 1 / N * sum over( yi - yi,pred,CV) Well it is somehow similar to the RSS for R2 and the PRESS for Q2. But this delta value increases with

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It is actually model.matrix that crashes, not glm. Same crash occurs with e.g. lm. model.matrix(dob_mon ~ dob_day*dob_mon, data = tab) also crashes R. Jan On 06-10-17 12:08, Jan van der Laan wrote: > > The following code crashes R (I know I shouldn't try to estimate such > a model; this was a bug in some code of mine). I also tried with > R-devel; same result. > >

Hello everyone, I am confused about how the dependent variable should be specified, e.g. say S and F denote series of successes and failures. Is it share<-S/(S+F) glm(share~x,family=quasibinomial) or glm(cbind(S,F)~x,family=quasibinomial) The two variants produce very different dispersion parameter and deviances. The book by Crawley, the only one R-book a have, says the second variant is

Dear all, I am fitting an nonlinear glm() using optim() by first minimising glm(resp~ var1 + var2, family=binomial, data=data)$deviance where var1= exp(-a1*dist1), and var2= exp(-a2*dist2), where a1 and a2 are parameters and dist1 and dist2 are independent variables. Next, I calculate the value of var1 (and var2) by plugging in the value of al1 (and al2) that minimises deviance, and fit

Hi list, I've searched in R-help and found some related discussions but still could not understand this type of error. My own function is pretty complex, so I would not put it here, but the basic algorithm is like this: myfun<-function(k){ mydata<-...#by someway I create a data frame mymodel<-glm(y~.,family=binomial(),data=mydata) ...#some other stuff } as I execute this

Hello, I am currently working with the betareg package, which allows the fitting of a variable dispersion beta regression model (Simas et al. 2010, Computational Statistics & Data Analysis). I was wondering whether there is any package in R that allows me to fit variable dispersion parameters in the standard logistic regression model, that is to make the dispersion parameter contingent upon

I have gotten the this error before: "glm.fit: fitted probabilities numerically 0 or 1 occurred" and the problem was usually solved by combining one or more categories were there were no observations. I am now having this error show up for a variable that is continuous (not categorical). What could be the cause of this for a continuous variable?? Thanks, Nick -- View this message

Dear, I want to compute coefficient of determination (R-squared) to complement AIC for model selection of multivariable GLM. However, I found this is not a built-in function in glm. neither is it available through reviewing the question in the R-help archive. Please kindly help and thanks a lot. Elaine [[alternative HTML version deleted]]

I have a categorical variable with a nested structure. For example, region: a country is split into parts, which in turn contain provinces, which contain municipalities: Part -> Province -> Municipality North Province A Municipality 1 Municipality 2 Municipality 3 ... Province B Municipality 1 ... ... West Province A ...

Dear R-helpers, I'm using a GLM with poisson errors to model integer count data as a function of one non-integer covariate. The model formula is: log(DV) ~ glm(log(IV,10),family=poisson). I'm getting a warning because the logged DV is no longer an integer. I have three questions: 1) Can I ignore the warning, or is logging the DV (resulting in non-integers) a serious violation of the

Hi all, I would like to estimate power and necessary sample size for a GLM with a response variable that has a poisson distribution. Do you have any suggestions for how I can do this in R? Thank you for your help. Sincerely, Craig -- Craig A. Faulhaber Department of Forest, Range, and Wildlife Sciences Utah State University 5230 Old Main Hill Logan, UT 84322 (435)797-3892