similar to: Specifying starting values in lme (nlme package) using msScale

I am modeling the effects of an environmental variable (X) on fish recruitment (Y) for several, ecologically related species (i) using a mixed-effects model. The linear relationship between X and Y includes a fixed effect that is common across all species and random effects that vary by species. In the lmer() notation from the package lme4: Model<- lmer(Y ~ X +(X|i)) Because the residuals of

When I run gnls I get the error: Error in nls(y ~ cbind(1, 1/(1 + exp((xmid - x)/exp(lscal)))), data = xy, : step factor 0.000488281 reduced below 'minFactor' of 0.000976563 My first thought was to decrease minFactor but gnlsControl does not contain minFactor nor nlsMinFactor (see below). It does however contain nlsMaxIter and nlsTol which I assume are the analogs of

Hi. I was wondering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor

R2.2 for WinXP, Splus 6.2.1 for HP 9000 Series, HP-UX 11.0. I am trying to get a handle on why the same lme( ) code gives such different answers. My output makes me wonder if the fact that the UNIX box is 64 bits is the reason. The estimated random effects are identical, but the fixed effects are very different. Here is my R code and output, with some columns and rows deleted for space

Hi: I am working with someone remotely to allow them access to our data. The follow command using "download.file" works perfectly on my Mac: > > download.file(url="http://oceanwatch.pfeg.noaa.gov:8081/thredds/ > wcs/satellite/AG/ssta/14day? > request=GetCoverage&version=1.0.0&service=WCS&format=NetCDF3&coverage= >

Dear Friends, I am writing to seek any help on "lme" error message. I am using lme to do Mixed-model linear regression. I use my simulated data. However, sometimes, I get the following error message, which I do not understand. "Error in solve.default(pdMatrix(a, fact=TRUE)): system is computationally sigular" I would appreciate any help about it. Thanks a lot Jingyuan Fu

Dear readers, I have a basic question about how to use lme for my design. I haven't been able to find an example in r-help that made it clear to me how to tackle this problem and unfortunately I also cannot get hold of Pinheiro & Bates 2000. I hope someone can help. Data for the response variable "foan" were collected in: · 60 plots · plots were re-sampled yearly

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 Users, >From previous analysis (semi-variograms using package gstat), I found spatial autocorrelation in my dataset. The best fitted model to this spatial correlation structure is the Gaussian model (Spherical, Exponential, Linear tested and comparison done by Sum of Square errors). So I used corGaus function to define this spatial autocorrelation in my lme model using the option

Dear R-helpers I am running a simple mixed effects model using lme(). The call looks like this: fit <- lme(Analyte~Sample, data=Data, random=~1 | Run) I am particularly interested in the estimated random effects. When I print the 'fit' object, it looks something like example below: (...) Random effects: Formula: ~1 | Run (Intercept) Residual StdDev: 3.483794 3.637523

Hello I am trying to analyse mortality data from fish larvae using lme from the package nlme as well as using lmer in the package lme4 Response is DeathDay Fixed factor is Treatment Random factors are Clucth, Cup Design: Cup nested in Clutch If I do this in lme, I use the syntax: model1 <- lme(DeathDay ~ Treatment, random=~ 1 | Clutch/Cup) summary(model1) I get the first part of the output,

Hi dears, I do > CHOL<-lme(chol~rt*cd4+sex+age+rf+nadir+pharmac+factor(hcv)+factor(hbs)+ haartd+hivdur+factor(arv), random= ~rt|id, na.action=na.omit) ...runs sweet,..then ....try a multicomparisons approach for the categorical rf > summary(glht(CHOL, linfct=mcp(rf="Tukey"))) * Error in model.frame.default(object, data, xlev = xlev) : l'oggetto non è una matrice

I am hoping that some one might be to tell me whether there are any functions that produce influence measures for lme /nlme objects (i.e those suggested by Lesaffre and Verbeke or Langford and Lewis for multilevel models). Thanks in advance. ----------------------------------------------- Anthony A. Pezzola apezzola@uc.cl (02) 354-7823 Profesor de Ciencia Política Instituto de Ciencia

Hi everybody, as you may be aware the function augPred.lme does not work as soon as the covariate is a factor. The problem lies in the line newprimary <- seq(from = minimum, to = maximum, length.out = length.out) which does not make sense for factors. I think augPred.lme can be useful for models with a factor covariate as well. Thus, I propose to change the code to: augPred.lme <-

I'm trying to run a linear mixed effects model using nlme. It appears that lme is not available for R, but looks like I should be able to run linear models using nlme. What I tryed looks something like this: fit <- nlme (Y ~ A + B + C + D, fixed=list(A,B,C,D),random=Z). This didn't work. I got this error: Error in reStruct(random, REML = REML, data = NULL) : Object must be

Using lmer in the lme4 package, you can compute the conditional variance-covariance matrix of the random effects using the bVar slot: bVar: A list of the diagonal inner blocks (upper triangles only) of the positive-definite matrices on the diagonal of the inverse of ZtZ+Omega. With the appropriate scale factor (and conversion to a symmetric matrix) these are the conditional variance-covariance

Hi all, is there a way to calculate standard error of random effect from the estimated model in lme? Best Marcus [[alternative HTML version deleted]]

Dear professor, I'm using package nlme, but I can't find a way to specify two uncorrelated random effects. For example, a random intercept and a random slope. In package lme4, we can specify&nbsp;x + (x ll g) to realize, but how in nlme? Thanks! ???????????????????????? Zhen Wang Graduate student, Department of Medical Statistics, School of Public Health, Sun Yat-sen

Hi, i am trying to learn something about multilevel analysis using a great "Discovering statistics using R". I constructed some sample data and then tried to fit a model. Generally model fits well, however when trying to fit the same model using z-score (standarizded) variables i got an error: Error in lme.formula(z_wyn ~ z_IQ + Kasa, data = la, random = ~z_IQ | : nlminb problem,

Hi all, I''ve been trying to fit a mixed effects model and I''ve been having problems. =>My aim: to know whether states atributes, political parties and individual atributes affect the electoral results of men and women candidates. I use candidates as replications for states and for political parties. =>Response: Percentage of valid votes casted to each