
I have a very strange problem with my lmer function:  the model below (model2) gives me a "false convergence" warning, but ONLY for certain multiples of x (here time), ie. time was originally in days (15-60) and I need it to be in hours, so I multiplied by 24 (and called it time instead of timed) and it gives me the following output:


model2 <- lmer (y~time+instar + (1|blc/instar),verbose=TRUE, REML=FALSE,family=quasibinomial(link=probit))


> model2 <- lmer (y~time+instar + (1|blc/instar),verbose=TRUE, REML=FALSE,family=quasibinomial(link=probit))
  0:     170.25087: 0.816497 0.471405 -1.80929 0.00183953 0.409944 -0.120202
  1:     170.20463: 0.816496 0.471404 -1.80929 0.00186189 0.409944 -0.120202
  2:     170.20463: 0.816496 0.471404 -1.80929 0.00186189 0.409944 -0.120202
Warning message:
In mer_finalize(ans) : false convergence (8)


It looks like the iteration stops after a few steps, whereas when I run the model with days it completes 27 steps and reaches the following output (I cut out some steps for better overview):

 

> model2 <- lmer (y~timed+instar + (1|blc/instar),verbose=TRUE, REML=FALSE,family=quasibinomial(link=probit))
  0:     170.25087: 0.816497 0.471405 -1.80929 0.0441486 0.409944 -0.120202
  1:     170.22534: 0.816271 0.471329 -1.80929 0.0451200 0.409944 -0.120203
  2:     170.15753: 0.815436 0.471049 -1.80931 0.0446475 0.409939 -0.120212
  3:     170.11357: 0.813606 0.470435 -1.80935 0.0451684 0.409930 -0.120230
  4:     124.52440: 0.109642 0.234346 -1.82552 0.0448669 0.406283 -0.127214
  5:     121.10622:  0.00000 0.165132 -1.83089 0.0433875 0.407351 -0.129359
 27:     120.55699: 2.03203e-07 0.162016 -1.82157 0.0442344 0.415274 -0.120470
> summary(model2)
Generalized linear mixed model fit by the Laplace approximation 
Formula: y ~ timed + instar + (1 | blc/instar) 
   AIC   BIC logLik deviance
 134.6 151.6 -60.28    120.6
Random effects:
 Groups     Name        Variance   Std.Dev.  
 instar:blc (Intercept) 4.5491e-16 2.1329e-08
 blc        (Intercept) 2.8919e-04 1.7006e-02
 Residual               1.1017e-02 1.0496e-01
Number of obs: 84, groups: instar:blc, 21; blc, 7

Fixed effects:
              Estimate Std. Error t value
(Intercept) -1.8215677  0.0137412 -132.56
timed        0.0442344  0.0002751  160.77
instarforth  0.4152732  0.0087307   47.56
instarsixth -0.1204703  0.0086215  -13.97

Correlation of Fixed Effects:
            (Intr) timed  instrf
timed       -0.764              
instarforth -0.378  0.087       
instarsixth -0.305 -0.013  0.495
> 

The strange thing is, that if I multiply by 10 it works fine, multiply by anything between 15 and 1350 is a problem, then again 1400 is fine and 1450 but 1500 isn't... I tested different multiples, displaying standard deviation and the multiplication factor, to see which multiples work and which give me a convergence problem (standard deviation for the ones that work fine is about 120, the ones with problems stops at 170). Unfortunately, I see no pattern and don't understand this at all...

 

Can anybody help me with this? What am I doing wrong?

 

Here are the different multiples:

 
> iter <- seq(1,1600,25)
> dev <- numeric(length=length(iter))
> for (i in 1:length(iter)){
+ modelx <- lmer (y~I(timed*iter[i])+instar + (1|blc/instar),REML=FALSE,family=quasibinomial(link=probit))
+ dev[i] <- deviance(modelx)
+ }


> cbind(iter,dev)
      iter      dev
 [1,]    1 120.5570
 [2,]   26 170.2046
 [3,]   51 170.2046
 [4,]   76 170.2128
 [5,]  101 170.2046
 [6,]  126 170.2046
 [7,]  151 170.2046
 [8,]  176 170.2046
 [9,]  201 170.2046
[10,]  226 170.2046
[11,]  251 170.2046
[12,]  276 170.2055
[13,]  301 170.2088
[14,]  326 170.2147
[15,]  351 170.2235
[16,]  376 170.2477
[17,]  401 170.2046
[18,]  426 170.2046
[19,]  451 120.5570
[20,]  476 170.2046
[21,]  501 170.2046
[22,]  526 120.5570
[23,]  551 170.2046
[24,]  576 170.2046
[25,]  601 170.2046
[26,]  626 170.2046
[27,]  651 170.2046
[28,]  676 170.2046
[29,]  701 170.2046
[30,]  726 170.2046
[31,]  751 170.2046
[32,]  776 170.2049
[33,]  801 170.2056
[34,]  826 170.2068
[35,]  851 170.2085
[36,]  876 170.2108
[37,]  901 170.2137
[38,]  926 170.2172
[39,]  951 170.2213
[40,]  976 170.2272
[41,] 1001 170.2380
[42,] 1026 170.2046
[43,] 1051 170.2046
[44,] 1076 120.5857
[45,] 1101 170.2046
[46,] 1126 170.2046
[47,] 1151 170.2046
[48,] 1176 170.2046
[49,] 1201 120.5570
[50,] 1226 170.2046
[51,] 1251 170.2046
[52,] 1276 170.2046
[53,] 1301 170.2046
[54,] 1326 170.2046
[55,] 1351 170.2046
[56,] 1376 170.2046
[57,] 1401 120.5570
[58,] 1426 120.5570
[59,] 1451 120.5570
[60,] 1476 120.5570
[61,] 1501 170.2046
[62,] 1526 170.2046
[63,] 1551 170.2046
[64,] 1576 120.5859


 

Thank you!

 

Linda

 

Linda Buergi
Department of Environmental Science, Policy and Management
UC Berkeley, Berkeley, California

 		 	   		  
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