[R-sig-ME] Nlme corAR1 error: Model won't converge

Igor Yakovenko iyakoven at ucalgary.ca
Mon Mar 14 03:37:42 CET 2016

Hi Everyone,


I'm absolutely stuck trying to figure this out, so I thought brighter minds
here may be able to shed some light on the situation. I'm relatively new to
mixed modeling in R, so it could be something obvious. I'm trying to fit a
longitudinal mixed model in Nlme, specifically to be able to model the
covariance structure. The data itself is based on an RCT intervention with
two groups followed over three time points (baseline, 3 and 6 months).
Before I even started adding the group predictor or any other factors, I
started building the basic model with just the fixed intercept, random
intercept, then a fixed time and random slopes. After building these first 4
models, which went fine, I modeled corAR1 on top of the previous model and
got a convergence error. Here is the syntax I used, including the resulting


model1 = gls(PGSI3months_Total ~1, data = vse, method = "ML", na.action =

model2 = lme(PGSI3months_Total ~1, data = vse, method = "ML", na.action =
"na.omit", random = ~1|id)

timeRI = update(model2, .~. + Time)

timeRS = update(timeRI, random = ~Time|id)

ARModel = update(timeRS, correlation = corAR1(0, form = ~Time|id))

Error in lme.formula(fixed = PGSI3months_Total ~ Time, data = vse, random =
~Time |  : 

  nlminb problem, convergence error code = 1

  message = iteration limit reached without convergence (10)


Time is my time variable, id is the subject number variable, distance
between time points is equal. Changing the maximum number of iterations or
the optimizer did not resolve this issue. 


Here is the summary for the timeRS model. The degrees of freedom and sample
size appear to be appropriate to model additional parameters, unless I
missed something, so I can't understand why trying to model autocorrelation
produces an error.


Linear mixed-effects model fit by maximum likelihood
 Data: vse 
       AIC      BIC    logLik
  3391.798 3417.239 -1689.899
Random effects:
 Formula: ~Time | id
 Structure: General positive-definite, Log-Cholesky parametrization
            StdDev      Corr  
(Intercept) 3.869742861 (Intr)
Time        0.005132771 -0.001
Residual    5.588313454       
Fixed effects: PGSI3months_Total ~ Time 
                Value Std.Error  DF   t-value p-value
(Intercept) 18.208305 0.6840868 307  26.61695       0
Time        -4.693759 0.3065688 307 -15.31062       0
Time -0.839
Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-2.26592087 -0.68602172 -0.06530166  0.60976438  2.47559836 
Number of Observations: 513
Number of Groups: 205 


Happy to provide any additional information. 


Thank you in advance for your help.

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