[R-sig-ME] GEE and GLMM convergence issues

[Arianna Cecchetti]

Dear all,

I am investigating differences of activity budget of dolphins across different temporal scales (i.e. years, months and and day time) and in relation to group size. I attach a subset of the data as example. I tried different approaches, but got convergence issues that I am struggling to deal with:

1)       multinomial GEE (multgee package)
I first run an independence structure being this one faster. When I run the most complete models they fail the fitting and initial values are asked. So I provided some values with the bstart parameter including 3 and 4 initial values for the 3 and 4 predictors of the two models:

mod6 <- nomLORgee(ActivityState~GroupSize + GroupSize*as.factor(Year) + GroupSize*Month, data=test, id=NSequence, repeated=Rep, LORstr="independence", bstart=c(1,0,1))

mod7 <- nomLORgee(ActivityState~GroupSize + GroupSize*as.factor(Year) + GroupSize*Month + GroupSize*Day, data=test, id=NSequence, repeated=Rep, LORstr="independence", bstart=c(1,0,1,0))


but I get the following error message:

Error in nomLORgee(ActivityState ~ GroupSize + GroupSize * as.factor(Year) +  :
  'bstart' and 'beta' differ in length

I am wondering if I am providing the initial values in the correct way. When I try to run the same models with the time.exch structure even the most simple one including only one variable get stuck in a loop and won’t generate any output.

2)       Four separated binomial GEE models for each activity state.
In one case values for estimates and standard errors doesn’t seem reliable.

geeglm(formula = ActivityState ~ GroupSize + as.factor(Year) + Month + Day + GroupSize * as.factor(Year) + GroupSize * Month + GroupSize * Day, family = binomial(link = "logit"), data = testsoc, id = NSequence, waves = Rep, corstr = "exch", scale.fix = T)

Coefficients:
                                                                                                Estimate   Std.err   Wald Pr(>|W|)
(Intercept)                                                                            5.84e+15  1.09e+16   0.29   0.5933
GroupSize                                                                             -1.75e+15  5.55e+14   9.91   0.0016 **
as.factor(Year)2014                                                           9.45e+15  1.60e+16   0.35   0.5533
as.factor(Month)August                                                   -3.62e+16  6.52e+15  30.93  2.7e-08 ***
as.factor(Month)July                                                         -3.53e+16  7.47e+15  22.36  2.3e-06 ***
as.factor(Month)June                                                        1.78e+17  9.80e+16   3.31   0.0689 .
as.factor(Month)May                                                        -1.31e+15  1.32e+16   0.01   0.9207
as.factor(Month)September                                           -1.29e+15  2.36e+16   0.00   0.9566
…..

Estimated Correlation Parameters:
   Estimate             Std.err
alpha 1.44e+15     2.43e+31
Number of clusters:   183   Maximum cluster size: 50

3)       GLMM (package lme4, glmer function)
For the models which failed convergence I applied different optimizers, but some still report convergence failure although they provide an output (here is an example)
ActivityState ~ log(GroupSize) + (1 | NSequence) + as.factor(Year) + Month + Day + log(GroupSize) * as.factor(Year) + log(GroupSize) * Month + log(GroupSize) * Day
Data: testtrav
Control: glmerControl(optimizer = "bobyqa")
Scaled residuals:
   Min     1Q Median     3Q    Max
-2.565 -0.517 -0.150  0.552  5.757

Random effects:
Groups    Name        Variance Std.Dev.
NSequence (Intercept) 6.59     2.57
Number of obs: 2207, groups:  NSequence, 183

Fixed effects:
                                                                                                Estimate Std. Error z value Pr(>|z|)
(Intercept)                                                                            -5.0105     3.5342   -1.42  0.15628
log(GroupSize)                                                                     0.8871     1.2095    0.73  0.46331
as.factor(Year)2014                                                           1.4536     1.4187    1.02  0.30553
as.factor(Month)August                                                   2.4192     3.6134    0.67  0.50317
as.factor(Month)July                                                         1.5645     3.4415    0.45  0.64940
as.factor(Month)June                                                        9.4559     4.2818    2.21  0.02722 *

…..

Correlation matrix not shown by default, as p = 20 > 12.
Use print(x, correlation=TRUE)  or
                vcov(x)  if you need it

convergence code: 1
Model failed to converge with max|grad| = 0.00394719 (tol = 0.001, component 1)

Using max(abs(relgrad)) code all models failed to be reliable being the values well above 0.001.

So at this point I am a bit stuck in deciding which approach would be the best to deal with these convergence issues.
I greatly appreciate any suggestions at this regard.

Thank you!

Best,

Arianna