similar to: lme function

I tried to use LME (on a fairly large dataset, so I am not including it), and I got this error message: Error in lme.formula(formula(paste(c(toString(TargetName), "as.factor(nodeInd)"), : nlminb problem, convergence error code = 1 message = false convergence (8) Is there any way to get more information or to get the potentially wrong estimates from LME? (Also, the page in the

I have 2 questions on ANOVA with 1 between subjects factor and 2 within factors. 1. I am confused on how to do the analysis with aov because I have seen two examples on the web with different solutions. a) Jon Baron (http://www.psych.upenn.edu/~baron/rpsych/rpsych.html) does 6.8.5 Example 5: Stevens pp. 468 - 474 (one between, two within) between: gp within: drug, dose aov(effect ~ gp * drug *

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Still having that problem with samba seemingly corrupting files, it seems to be the same files that are corrupted again and again, yet they are corrupted differently and sometimes not. I ran my md5 summer against one file ch02.pdf, and it almost contineously corrupts, I checked in a hex editor and there are some differences. Yet the problem is that

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)

Greetings all The help file for GAMM in mgcv indicates that the log likelihood for a GAMM reported using summary(my.gamm$lme) (as an example) is not correct. However, in a past R-help post (included below), there is some indication that the likelihood ratio test in anova.lme(mygamm$lme, mygamm1$lme) is valid. How can I tell if anova.lme results are meaningful (are AIC, BIC, and logLik

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

I've written a package to assist with using R in Hadoop Streaming. The main point of the package is to help make command-line runnable RScript files. I'd like to provide a demo RScript file, a demo data file (e.g. a plaintext file, not something already processed by R) , as well as demo bash shell scripts that demonstrate how to run the job from the command line and in a Hadoop

Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN

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

Dear List, I am new to TS-Modeling in R. I would like to fit an ARMA(1,1) model for a balanced panel, running Y on a full set of unit and year dummies using an arma(1,1) for the disturbance: y_it=unit.dummies+yeardummies+e_it where: e_it=d*e_it-1+u_it+q*u_it-1 How can I fit this model in R? arma() does not seem to take covariates (or I don't understand how to specify the function so that

Dear R People: Sorry for the off-topic. Could someone recommend a good reference for using the EM algorithm on mixed models, please? I've been looking and there are so many of them. Perhaps someone here can narrow things down a bit. Thanks in advance, Sincerely, Erin -- Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown

Hello, I have an irregular time series of the form : Time Data Time1 Data1 .... 1 b 1 e 7 g 4 i NA NA 5 k NA NA NA NA ... (the columns have varying length of NAs after a certain point) Converting this to regular time series with Pastecs does not seem to work, when I see the entire data as a single series. So I remove

Thanks a lot for the quick answerd I received and that helped me to solve my first (simple) problem with the plm package. Unfortunatley, here's another one: I do not know why, but I'm unable to estimate a simple model in the form y ~ 1 + x When I try > zz <- plm(y ~ 1 + x , data=mydata) I obtain Errore in X.m[, coef.within, drop = F] : numero di dimensioni errato which means

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

It looks like the Subversion migration is working. The CVS repository is still there, but in read-only mode. However, the SVN repository preserves the version history of the CVS repository, albeit organized slightly differently. For an introduction to Subversion, this online book is essential: http://svnbook.red-bean.com/ It is written by some of the Subversion authors. The only drawback I

Hello! In the book Modern Applied Statistics with S (4th ed), section 5.6 the concept of the "average shifted histogram" or ASH is mentionend. Also it is mentioned in the same section "The code used is in the scripts for this chapter" (from figure caption 5.8, analysis of the geyser duration data). *However*, I have trouble finding the code for that function! Admittedly, I am a

Does anyone know of a way of dealing with unbalanced mixed effects (fixed and random factors) for fully factorial designs. An example of such data is given below; The response variable is SQRTRECRUITS SEASON is a random factor DENSITY is a fixed factor Thus DENSITY:SEASON is a fixed factor. Therefore, whereas the effects of SEASON and DENSITY:SEASON should be tested against the overall

Dear R community, I have trouble implementing a nested variance-covariance matrix in the lme function. The model has two fixed effects called End and logpgc, the response variable is the logarithm to base 2 of Intensity ( log2(Intensity) ) and the random effects are called Probe and ProbeNo. The model has the following nesting structure: A Pixel is nested within the ProbeNo,the ProbeNo is

Hello, How do I specify the formula and random effects without a startup object ? I thought it would be a mixture of nls and lme. after trying very hard, I ask for help on using nlme. Can someone hint me to some examples? I constructed a try using the example from nls: #variables are density, conc and Run #all works fine with nls DNase1 <- subset(DNase, Run == 1 ) fm2DNase1 <- nls(

Hi, I was very much hoping someone could help me with the following. I am trying to convert some SAS NLMIXED code to NLME in R (v.2.1), but I get an error message. Does anyone have any suggestions? I think my error is with the random effect "u" which seems to be parametrized differently in the SAS code. In case it's helpful, what I am essentially trying to do is estimate parameters