search for: varidation

Hi everyone, I am a newbie in R and in this discussion list. I am trying to use R package "ncdf" to read values of temperature from a NCDF file. I did it before to another file using the function "get.var.ncdf", but now there is an error that I can not solve, and I would really appreciate if you could help me. I am using R version 2.11.1 (2010-05-31) in a machine with Linux

Hello, As I am relatively new to the R environment this question may be either a) Really simple to answer b) Or I am overlooking something relatively simple. I am trying to add a VarIdent structure to my gam model which is fitting smoothing functions to the time variables year and month for a particular species. When I try to add the varIdent weights to variable Month I get this error returned.

Hi, I would like to fit a model for a factorial design that allows for unequal variances in all groups. If I am not mistaken, this can be done in lm by specifying weights. A function intended to specify weights for unequal variance structures is provided in the nlme library with the varIdent function. Is it apropriate to use these weights with lm? If not, is there another possibility to do

Hello, 1. It appears that "coef<-.varIdent" method does not work properly in some instances. Execution error: Error in `coef<-.varIdent`(`*tmp*`, value = c(11, 12)) : Cannot change the length of the varIdent parameter after initialization occurs when "coef<-.varIdent" is applied to an initialized object of class varIdent with some of the coefficients

I was reading the help but just did not get how to specify starting values for varIdent() of the lme() function, although I managed to do it for corSymm(). Do I specify the values just as they are printed out in an output, like c(1, 1.3473, 1.0195). Or do I need to take the residual and multiply it with these like c(0.2235, 0.2235*1.3473, 0.2235*1.0195) or any other form that I dont know of?

Dear All, Can anyone please let me know how exactly ncdf deals with NaN. I am trying to pass in a vector of data that has some NaN in it, into a variable in NetCDF. dimX <- dim.def.ncdf("X","count",(1:6)) dimY <- dim.def.ncdf("Y","count",(1:3)) var1 <-

Dear All, I'm trying to model heteroscedasticity using a multilevel model. To do so, I make use of the nlme package and the weigths-parameter. Let's say that I hypothesize that the exam score of students (normexam) is influenced by their score on a standardized LR test (standLRT). Students are of course nested in "schools". These variables are contained in the

summary: I can successfully ncvar_put(...) data to a file, but when I try to ncvar_get(...) the same data, I get > Error in if (nc$var[[li]]$hasAddOffset) addOffset = nc$var[[li]]$addOffset else addOffset = 0 : > argument is of length zero How to fix or debug? details: R code @ https://github.com/TomRoche/GEIA_to_NetCDF successfully (if crudely) uses R packages={ncdf4, maps, fields}

I am using lme from the nlme package to fit a mixed model. We have observations nested in patients(encounters) and patients nested in groups (2 different treatments). We are interested in the differences between the 2 groups, both the means and the standard deviations (are patients in group A less variable than those in group B? both within patient and between patient within group). Here is

Hi, I am running R2.8.1 under Linux, and I am having trouble using the variance functions in nlme My basic model was something like: model0 <- lme( log(growth) ~ light * species.group , data=data, random=~light|species ) # with 20 odd species divided in 2 groups Following the methods in Pinheiro&Bates I tried to put a variance function in the model: model1 <- update(model0,

Greetings R users! I am working with the ENSEMBLE climate data (10 min resolution daily temperatures images for all of Europe 1950-2006). The data comes packaged in a single netCDF file. I would like to read the data in and export a subset (2002-2006) as geotiffs (one image per day). So far, I can successfully read in the data and view the images within an R display window. However, I have yet to

Hello R users- I am new to R, and tried searching the archives and literature for an answer to this - please be patient if I missed something obvious. I am fitting a logistic regression model, and would like to include variance functions (specifically the varIdent function). I cannot figure out how to do this either in glmmPQL (or something similar) for the model with random effects, or in glm

I would appreciate * guidance regarding translation of IDL routines to R, generally * assistance translating two IDL routines to R, specifically Why I ask: I'm slowly learning how to do atmospheric modeling. One language that has been been popular in this space is IDL http://en.wikipedia.org/wiki/IDL_(programming_language) which unfortunately is proprietary (not to mention butt-ugly, IMHO

Dear R Users, I am encountering a problem when reading nc files into R using the ncdf and ncdf4 libraries. The nc files are too large to attach an example (but if someone is interested in helping out I could send a file privately via an online drive), but the code is basic: for(i in 1:length(thesenames[,1])){ data <- nc_open(paste(INDIR, thesenames[i,c("wholename")],

Dear R-help, I am trying to set up linear mixed effects models in R using the (recommended) nlme package (R version 2.3.1 on a Linux platform). When trying to reproduce an example from Jose Pinheiro & Douglas Bates (2000, p 210) I get the following error message (code to produce message pasted as well): library("nlme") data("Orthodont") vf1Ident <- varIdent(

I've got a gls formula that's a mix of continuous and ordered variables. I wanted to use gls because I wanted to use the varIdent structure. Anyway, attempts to use "predict.gls" choke with the error that the levels I use are not allowed for one of them -- the first one alphabetically, so I'd guess the second would have the same problem. So I have three linked questions --

Greetings, I have a meta-analysis problem in which I have fixed effects regression coefficients (and estimated standard errors) from identical models fit to different data sets. I would like to use these results to create pooled estimated regression coefficients and estimated standard errors for these pooled coefficients. In particular, I would like to estimate the model \beta_{i} = \mu +

How does one implement a likelihood-ratio test, to test whether the variances of the random effects differ between two groups of subjects? Suppose your data consist of repeated measures on subjects belonging to two groups, say boys and girls, and you are fitting a linear mixed-effects model for the response as a function of time. The within-subject errors (residuals) have the same variance in

Dear Colleagues: Through your help an R related installation issue was resolved, but I now have the following usage issue. On any "get.var.ncdf" usage I am seeing: Error: in routine alloca() there is a stack overflow: thread 0, max 535822282KB, used 0KB, request 24B The same error is posted on a file as small as 50MB and as large as 500GB, with ulimit set to unlimited. I

Hi, I was reading the book on "Mixed Effects Models and Extensions in Ecology with R" by Zuur et al. In Section 6.2, an example is discussed where a gamm-model is fitted, with a smoother for time, which differs for each value of ID (4 different bird species). In earlier versions of R, the following code was used BM2<-gamm(Birds~Rain+ID+