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

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