# no convergence using lme

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## no convergence using lme

 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 Gender.Age is Gender*Age Genotype.Age is Genotype*Age Gender.Genotype.Age is Gender*Genotype*Age   If I have the intercept (but not the slope) as a random effect the fit converges OK fit1 <- lme(Outcome~Age + Gender + Genotype  + Gender.Age + Genotype.Age + Gender.Genotype.Age,                   random=~1|Animal, data=VC)     If I have the slope (but not the intercept) as a random factor it converges OK fit2 <- lme(LVDD~Age + Gender + Genotype + Gender.Age + Genotype.Age +Gender.Genotype.Age,                   random=~Age-1|Animal, data=VC)     If I have both slope and intercept as random factors it won't converge fit3 <- lme(LVDD~Age + Gender + Genotype + Gender.Age + Genotype.Age + Gender.Genotype.Age,                   random=~ Age|Animal, data=VC) Gives error: Error in lme.formula(LVDD ~ Age + Gender + Genotype + Gender.Age + Genotype.Age +  :       iteration limit reached without convergence (9)       If I try to increase the number of iterations (even to 1000) by increasing maxIter it still doesn't converge   fit <- lme(LVDD~Age + Gender + Genotype + Gender.Age + Genotype.Age + Gender.Genotype.Age, +                   random=~ Age|Animal, data=VC, control=list(maxIter=1000, msMaxIter=1000, niterEM=1000))   NB.  I changed maxIter  value in isolation as well as together with two other controls with "iter" in their name (as shown above) just to be sure ( as I don't understand how the actual iterative  fitting of the model works mathematically)     I was wondering if anyone knew if there was anything else in the control values I should try changing.   Below are the defaults.. lmeControl function (maxIter = 50, msMaxIter = 50, tolerance = 1e-06, niterEM = 25,     msTol = 1e-07, msScale = lmeScale, msVerbose = FALSE, returnObject = FALSE,    gradHess = TRUE, apVar = TRUE, .relStep = (.Machine\$double.eps)^(1/3),    minAbsParApVar = 0.05, nlmStepMax = 100, optimMethod = "BFGS",     natural = TRUE)   I was reading on the R listserve that lmer from the lme4 package may be preferable to lme (for convergence problems) but lmer seems to need you to put in starting values and I'm not sure how to go about chosing them.  I was wondering if anyone had experience with lmer that might help me with this?   Thanks again for any advice you can provide.   Regards Marg     Dr Margaret Gardiner-Garden Garvan Institute of Medical Research 384 Victoria Street Darlinghurst Sydney NSW 2010 Australia   Phone: 61 2 9295 8348 Fax: 61 2 9295 8321             [[alternative HTML version deleted]] ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
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