similar to: comparative rendering of modeling outputs

Hi all, In a simulation context, I'm applying some my function, "myfun" say, to a list of glm obj, "list.glm": >length(list.glm) #number of samples simulated [1] 1000 >class(list.glm[[324]]) #any component of the list [1] "glm" "lm" >length(list.glm[[290]]$y) #sample size [1] 1000 Because length(list.glm) and the sample size are rather large,

Dear R-List users, Can anyone explain exactly the difference between Weights options in lm glm and gls? I try the following codes, but the results are different. > lm1 Call: lm(formula = y ~ x) Coefficients: (Intercept) x 0.1183 7.3075 > lm2 Call: lm(formula = y ~ x, weights = W) Coefficients: (Intercept) x 0.04193 7.30660 > lm3 Call:

This is a question about removing the intercept in a binomial glm() model with categorical predictors. V&R (3rd Ed. Ch7) and Chambers & Hastie (1993) were very helpful but I wasn't sure I got all the answers. In a simplistic example suppose I want to explore how disability (3 levels, profound, severe, and mild) affects the dichotomized outcome. The glm1 model (see below) is

Hi, I am trying to fit a negative binomial model with a number of parasite tapeworms as response variable to geographical coordinates (actually preparing a trend surface before kriging). When I try an anova, I get warnings: > glm4.nb<-glm.nb(wb~X4+Y4+I(X4^2)+I(Y4^2)) > anova(glm4.nb) Analysis of Deviance Table Model: Negative Binomial(0.0463), link: log Response: wb Terms added

Hi all I'm getting a NaN returned on using dffits, as explained below. To me, there seems no obvious (or non-obvious reason for that matter) reason why a NaN appears. Before I start digging further, can anyone see why dffits might be failing? Is there a problem with the data? Consider: # Load data dep <-

Dear list, I have been working with the MatrixModels package quite a bit this week, and it is proving to be extremely valuable for my current work (I am working with several factors with many levels, leading to a sparse model matrix). However, as my knowledge of statistical theory leaves much to be desired, there are certain aspects of model evaluation etc that I am having trouble with. Has

Dear list, Can anyone tell me how to obtain the rank of a sparse Matrix, for example from package Matrix (class dgCMatrix)? Here is an example of QR decomposition of a sparse matrix (from the sparseQR class help). library(Matrix) data(KNex) mm <- KNex$mm str(mmQR <- qr(mm)) Similarly, using the functions/classes from the relatively new MatrixModels package: library(MatrixModels)

Hi, I've had a look the bug list and searched though the R documentation, email lists etc. but didn't see anything on this: when I do: summary(species.glm1, correlation = TRUE) I get a correlation matrix like this: Correlation of Coefficients: ( p I(H C pH * 1 I(pH^2) * B 1 Ca . . 1 I(Ca^2) . . B attr(,"legend") [1] 0 ` ' 0.3 `.'

Hi everyone, I'm looking to produce a side-by-side histogram of the number of trips taken by jays with a particular number of acorns after accounting for year (year "one" and year "two"). I know this involves indexing first then creating a histogram but I'm not sure how I'd do this. I want to explore the possibilities that jays are altering their strategies in

I ran an rsync test using the RDC program that comes with the Server 2008 SDK. The RDC program is a simple program to demonstrate Microsoft's Remote Differential Compression algorithm. It uses the API for RDC to copy a file across a WAN/LAN connection. The program, as-is, has little functionality and a lot of limitations but I thought it might be fun to test. I compiled the RDC code as-is

NESCent Hackathon on Comparative Methods in R Call for Participation and Input The R statistical analysis package has emerged as a popular platform for implementation of powerful comparative phylogenetic methods to understand the evolution of organismal traits. The National Evolutionary Synthesis Center (NESCent) is organizing a hackathon focused on the integration of

I've been comparing variables among objects (taxa) related by known trees, using phylogentically independent contrasts in the ape package, and want to move on to more complex models e.g. by using gls with appropriate correlation terms. My trees contain lots of (hard) polytomies and information about ancestors, which I've been including- creating fully dichotomous trees by using zero branch

I have two series of events over time and I want to construct a graph of the relative frequency/density of these events that allows their distributions to be sensibly compared. The events are the milestones items in my project on milestones in the history of data visualization [1], and I want to compare trends in Europe vs. North America. I decided to use a graph of two overlaid density

Thanks for the module, I think its a good idea to commit it to FreeBSD for a few reasons: 1) Some folks just prefer more static kernels. 2) Securelevel is a great thing, but can be a pain to do upgrades around remotely. [A lot of folks use FreeBSD simply because its a breeze to run remotely]. 3) Until someone writes code to add modules to a kernel via /dev/mem and releases it to the script

Dear R members, I am new in using frailty models in survival analyses and am getting some contrasting results when I compare the Wald and likelihood ratio tests provided by the r output. I am testing the survivorship of different sunflower interspecific crosses using cytoplasm (Cyt), Pollen and the interaction Cyt*Pollen as fixed effects, and sub-block as a random effect. I stratified

Hi, I have been trying unsuccessfully to plot data using different colors based on a variable within a subset of an imported file. The file I am reading is about 20000 lines long and has a column (in the example called FILE) that contains approximately 100 unique entries. I would like to plot a subset of the data from the file and key the color from the FILE column, This is what my file looks like