similar to: weight by obs # in spatial 'nest' in NLME?

Hi, I would like to specify a spherical correlation structure for spatially autocorrelated residuals in a model based upon the logistic function of a response that is a proportion (0 to 1) (so usual binary logistic regression is not an option). There is no need for a g-side random effect with grouping in this model. Am I correct that nlme requires this (meaning a correlated error structure only

Dear R-nlme expert We need two pieces of information about the fitting of a nlme model which we cannot extract from the R help files and would be most grateful if you could help us. We fit an energy allocation growth model with 4 parameters to individual growth curves using the nlme routine. We thus have repeated age and size measurements of individuals and therefore allow for random

Dear R-expert I'm fitting a non linear model (energy allocation model to individual growth data) using your nlme routine. For each individual I have thus a number of observations (age and size) to which I fit the nonlinear function, with random effects for the individuals on the estimated parameters (individual as the grouping factor). The sampling of these individuals was stratified (size

In the old pass manager a coarser-grain pass cannot depend on a fine-grain pass (or analysis). In this case, the coarser-grain is the Inliner (CallGraphSCCPass) and the finer a FunctionPass. The reason is that the finer-grain pass has its own "pass manager", i.e. it runs all the passes and analysis (in that pass manager) on one function, frees them, and continues with the next function

Hi, So, does this mean that in legacy pass manager, when I run: PM.add( someFunctionPass() ) // PM is modulePassManager it creates FunctionPassManager, runs the function pass,destroys the functionPassManager and returns control to modulePassManager? Also, you have said that this wasn't the primary reason for the introduction of the new pass manager. But then is this one of the reasons?

Dear all, First, I would like to thank you for your immense work. My question is about a frequent topic which I am not able to solve - even after hours of search in the mailing lisy. I would like to analyse random-effects (and fixed-effects)models of longitudinal / panel data with sampling weights. I have an unbalanced panel of different individuals in 5 years and income data as well as their

Greetings- I am in the process of constructing a nonlinear model using nlme. The model is attempting to fit a nested data structure from some public-opinion data (the data are from individuals nested within organizations). The question I have is fairly simple (I hope). The data were collected in two stages: a 15,000-subject randomly-sampled telephone interview (the SCREEN), with 2,517 subjects

Hi, I have a spatial data set with many observations (~50,000) and would like to keep as much data as possible. There is spatial dependence, so I am attempting a mixed model in R with a spherical variogram defining the correlation as a function of distance between points. I have tried nlme, lme, glmmML, and glmmPQL. In all case the matrix needed (seems to be (N^2)/2 - N) is too large for my

Dear R users, I'm estimating a mixed effects model in which the spatial correlation is controlled for by the "corGaus" structure. I'm wondering if there is a document or paper that explains how the spatial correlation structure (such as "corExp" or "corGaus") works. Let me use the example and data posted on UCLA's R FAQ webpage to explain my problems.

Hi. I'm trying to perform what should be a reasonably basic analysis of some spatial presence/absence data but am somewhat overwhelmed by the options available and could do with a helpful pointer. My researches so far indicate that if my data were normal, I would simply use gls() (in nlme) and one of the various corSpatial functions (eg. corSpher() to be analagous to similar analysis in SAS)

Hi Chris and llvm list Thanks for your answer. > So again, this is the problem I described earlier: you have a pass > (MParSchedule) which is a basic block pass. This pass is being required > by another pass (your C backend-based thing) which is not a basic block > pass. This is the problem, please see the "how to write a pass" doc. The > fix is to change

Hi R user, I have two dataframe with different variables and coordinates : X Y sp bio3 bio5 bio6 bio13 bio14 1 -70.91667 -45.08333 0 47 194 -27 47 12 2 -86.58333 66.25000 0 16 119 -345 42 3 3 -62.58333 -17.91667 0 68 334 152 144 28 4 -68.91667 -31.25000 0 54 235 -45 25 7 5 55.58333 48.41667 0 23 319 -172 23 14 6 66.25000

DeaR comRades: I have a 2D spatial binomial process as shown in the data and code below. I am plotting the number of trials and the number of successes in the spatial binomial experiments and would like to draw the spatial cells were the trials and successes were counted, i.e. a partial grid in the plot only for those cells where there is a number. The cells are 2x2 km cells. The count of Trials

Dear all, This is probably a stupid question for which I have a solution, which unfortunately is not as straighforward as I'd like. I wonder if there's a simple way to apply a weighting variable for the cases of a dataframe (well I'm sure there is, I just cannot find it). My toy example: > my.data <- data.frame(var1=c("c", "e", "a",

Hi all, I would like to do a weighted linear regression, when the error of the dependent variable is correlated. So I have a weighting (covariance) matrix instead of a vector. As I understood the „weights” argument in the lm function should be a vector and not a matrix. Can anyone suggest me a function (package) which would do the job? Thanks a lot! Emese [[alternative

Hi, I'm not sure how interested anybody here is in this, but I've been working lately on getting rid of the horror that is SunSSH for some distros of Illumos (mostly SmartOS). One of the patches we're carrying around at the moment is one that simply drops fine-grained privileges in sshd, ssh-agent and sftp-server. Since the privilege dropping here is roughly equivalent to a more

Hi, I've searched online, in a few books, and in the archives, but haven't seen this. I believe that xerror is scaled to rel error on the first split. After fitting an rpart object, is it possible with a little math to determine the percentage of true classifications represented by a xerror value? -seth -- View this message in context:

Hi, I have probability estimates from two predictive models. I have these estimates and also a binary outcome for a validation data set not used in calibrating either model. I would like to calculate the Brier score for both models on this binary outcome and test the hypothesis that the Brier scores are equal from the two models. I have not been able to find an R function to do this, can

Hello all, In lme4 if you want to model two non-nested random effects you code it like this: mod1 <- lmer(y~x + (1|randomvar1) + (1|randomvar2)) How would you go about to model something similar in nlme? In my database I have two variables for which I have repeated measures, lets call them "individual" and "year". But none of the "individuals" were measured in

I have spatial data on a sphere (the Earth) for which I would like to run an gls model assuming that the errors are autcorrelated, i.e. including a corSpatial correlation in the model specification. In this case the distance metric should be calculated on the sphere, therefore metric = "euclidean" in (for example) corSpher would be incorrect. I would be grateful for help on how to