similar to: Standard errors GLM

Hi userRs! I am trying to fit some GLM-poisson and neg.binomial. The neg. Binomial model is to account for over-dispersion. When I fit the poisson model i get: (Dispersion parameter for poisson family taken to be 1) However, if I estimate the dispersion coefficient by means of: sum(residuals(fit,type="pearson")^2)/fit$df.res I obtained 2.4. This is theory means over-dispersion since

Hi, I am trying to fi a glm-poisson model to 400.000 records. I have tried biglm and glmulti but i have problems... can it really be the case that 400.000 are too many records??? I am thinking of using random samples of my dataset..... Many thanks, -- View this message in context: http://r.789695.n4.nabble.com/glm-poisson-fitting-400-000-records-tp3925100p3925100.html Sent from the R help

I am trying to do logistic regression with a categorical predictor variable with the glm() function, family=binomial. Using glm() I would like to be able to calculate the confidence intervals of all three possible odds ratios for a factor (the factor has three categories). Three categories imply two columns of 0's and 1's in the design matrix, and two parameter estimates with their

Hi there, I need to do the same thing as cumsum but with the variance and skewness. I have tried to do a loop for like this: var.value <- vector(mode = "numeric", length = length(daily)) for (i in (1:length(daily))) { var.value[i] <- var(daily[1:i]) } But because my dataset is so huge, I run out of memory..... Any ideas?!?! Much appreciate

dear userR: I am trying to plot two dependent variables in the same plot in ggplot2. because these two variables have very different magnitude, I have to use a second Y axis. I hope one variable to be line and the other to be barchart. The x axis is continuous. Yet since I have to make barchart, I guess I have to treat it as discrete or categorical. I have been google searching for the whole

Hi, Suppose a binomial GLM with both continuous as well as categorical predictors (sometimes referred to as GLM-ANCOVA, if I remember correctly). For the categorical predictors = indicator variables, is then there a suggested minimum frequency of each level ? Would such a rule/ recommendation be dependent on the y-side too ? Example: N is quite large, a bit > 100. Observed however are

When including categorical variables in a regression, the default in R is to set the first level as the base. Is there an option to specify a different level as the base? Regards, Stephen Collins, MPP | Analyst Health & Benefits | Aon Consulting [[alternative HTML version deleted]]

Hi all, I am interested in obtaining R code related to geographically weighted regression. In particular, I am interested in building geographically weighted Poisson GLMs. The model will contain categorical and continuous x independent variables, with interaction effects between categorical and continuous variables. Anybody have anything I can look at? thanks, Mark. --

Dear userRs, Is there a way to explicitly set an argument to a function call as missing? E.g., histogram(foo, bar, endpoints=ifelse(!missing(limits),limits,NA/NULL/whatever))) In this call I want to set a value to the endpoints argument only if the `limits' variable has been set, and leave the defaults otherwise. The only way I could do it is thus: if

Dear UserRs, I draw filled contour plot with filled.contour function. By default, colors of filled.contour is terrain.colors which alters from green (the lowest level) to white (the highest level). I want the colors to alter from white (the lowest level) to green (the highest level). How can I set this color levels? Thanks in advance.

Dear UserRs, I am trying to use systematically ggplot2 for most of my plots, but I am fighting some lack of documentation, which I try to overcome. I want to build a scatterplot where the axes cross exactly at (0,0). I tried using scale_y_continuous(limits=c(0,10)), but I always get an extra space at the bottom of the axes. The code I used is below. > plotdata<-data.frame(x=1:10,

Dear list people Suppose we have a data.frame where variables are categorical and the response is categorical eg: my.df=NULL for(i in LETTERS[1:3]){my.df[[i]]=sample(letters, size=10)} my.df=data.frame(my.df) my.df$class=factor(rep(c("pos", "neg"), times=5)) my.glm=glm(class ~ ., data=my.df, family=binomial) summary(my.glm) .... Estimate Std. Error z

Dear Madam/Sir This may be quite a long shot... By way of intro, I am a masters student in actuarial science at the University of Cape Town, and I am doing a project in R on some healthcare cost data. During my coding in R I encountered an error message, which I then googled, but I am still unable to resolve the issue. I would like to please ask if and how it is possible to resolve the problem

Hi UserRs, I know that there has to be an easy way to do this in R (probably easy enough that once someone clues me in I'll smack myself on the forehead for not figuring it out myself), but my searches on my own have not yielded any hints. I have many fields in my dataset that participants entered as "free lists" - i.e., the field constitutes a varying number of names each

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Hello R-helpers, I would like to calculate least square means after having built a GLM with quasipoisson errors. In my model the dependent variable is continuous, I have one continuous independent variable and one categorical independent variable (that is the variable for which I would like to calculate the least square means). I've looked around for the command to calculate the least

I've a set of data to run a GLM on and am having trouble defining the categorical variables. For example, one of my variables is Year (ie. 1998, 1999). How do I define those years as factors/categorical variables? Cheers, Tim [[alternative HTML version deleted]]

Hello, I routinely use aov and and the Error term to perform analyses of variance of experiments with 'within-subject' factors. I wonder whether a notion like 'multistratum models' exists for glm models when performing a logit analysis (without being 100% sure whether this would make sense). I have data of an experiment where the outcome is a categorical variable: 20

We are wanting to use R instead of/in addition to our existing stats package because of it's huge assortment of stat functions. But, we routinely need to fit GLM models to files that are approximately 2-4GB (as SQL tables, un-indexed, w/tinyint-sized fields except for the response & weight variables). Is this feasible, does anybody know, given sufficient hardware, using R? It appears to

Hi, I have a problem concerning discrepances between R (which I use) and Statistica (which uses my supervisor). I can't say what is the origin of these differences but unfortunately my supervisor doesn't know that either. Our response variable is number (or presence/absence) of parasites in rodents and explanatory variables are presence/absence of several alleles. The rodents were