[R-sig-ME] Little variability in outcome; "pwrssUpdate did not converge"

[Ruben Arslan]

Dear list,

I have a dichotomous outcome (child mortality) with a very high mean
(0.9946) in a large dataset (3.5m).
The "Error: pwrssUpdate did not converge in (maxit) iterations" occurs in
most cases. I've tried using blme to combat complete separation with
fixef.priors with SDs from 1 to 10 without success. The variance explained
by the random family effect is numerically very small (0.000698) though I
suppose that still amounts to ca 7%. There's few members per family (~ 2 on
average). Fitting a glm without the family intercepts results in fairly
different results (which I expect), judging by the few models that ran.
Using less data sometimes leads to convergence, depending on the sample I
draw, I suppose. I'm using bobyqa.

I thought maybe the problem still is complete separation and I'm just being
too timid with the blme prior.

Oddly (maybe not), the only model where I do get convergence is one where I
accidentally mis-specified my sample, so my outcome was censored (hence the
mean but not the intercept was lower). I'm attaching the model.

Best regards,

Ruben Arslan

## Cov prior : idParents ~ wishart(df = 3.5, scale = Inf, posterior.scale =
cov, common.scale = TRUE) ## Fixef prior: normal(sd = c(9, 9, ...), corr =
c(0 ...), common.scale = FALSE) ## Prior dev : 143 ## ## Generalized linear
mixed model fit by maximum likelihood (Laplace ## Approximation)
[bglmerMod] ## Family: binomial ( logit ) ## Formula: surviveR ~
maternalage.factor + paternalloss + maternalloss + ## center(nr.siblings) +
birth.cohort + male + paternalage.mean + ## paternalage.factor + (1 |
idParents) ## Data: swed.2 ## Control: control_defaults ## Subset:
survive1y == TRUE & byear < 2000 ## ## AIC BIC logLik deviance df.resid ##
938507 938795 -469231 938463 3691460 ## ## Scaled residuals: ## Min 1Q
Median 3Q Max ## -134.50 0.04 0.05 0.06 3.07 ## ## Random effects: ##
Groups Name Variance Std.Dev. ## idParents (Intercept) 0.000698 0.0264 ##
Number of obs: 3691482, groups: idParents, 1907489 ## ## Fixed effects: ##
Estimate Std. Error z value Pr(>|z|) ## (Intercept) 6.84279 0.03471 197.2 <
2e-16 *** ## maternalage.factor(14,20] 0.16356 0.01600 10.2 < 2e-16 *** ##
maternalage.factor(35,61] -0.18822 0.00871 -21.6 < 2e-16 *** ##
paternallossTRUE -0.41957 0.04694 -8.9 < 2e-16 *** ## paternallossNA
-0.30693 0.01819 -16.9 < 2e-16 *** ## maternallossTRUE -0.67635 0.08228
-8.2 < 2e-16 *** ## maternallossNA -0.11658 0.02607 -4.5 7.8e-06 *** ##
center(nr.siblings) 0.27749 0.00288 96.2 < 2e-16 *** ##
birth.cohort(1970,1977] 0.35761 0.02833 12.6 < 2e-16 *** ##
birth.cohort(1977,1984] 0.72394 0.03203 22.6 < 2e-16 *** ##
birth.cohort(1984,1991] 0.86295 0.03173 27.2 < 2e-16 *** ##
birth.cohort(1991,1999] -5.95342 0.01933 -308.0 < 2e-16 *** ## male
-0.01946 0.00512 -3.8 0.00015 *** ## paternalage.mean 0.88269 0.01168 75.5
< 2e-16 *** ## paternalage.factor(25,30] -0.53984 0.01068 -50.5 < 2e-16 ***
## paternalage.factor(30,35] -1.18842 0.01360 -87.4 < 2e-16 *** ##
paternalage.factor(35,40] -1.59243 0.01815 -87.7 < 2e-16 *** ##
paternalage.factor(40,45] -2.02418 0.02429 -83.3 < 2e-16 *** ##
paternalage.factor(45,50] -2.46269 0.03266 -75.4 < 2e-16 *** ##
paternalage.factor(50,55] -3.11201 0.04679 -66.5 < 2e-16 *** ##
paternalage.factor(55,90] -3.67437 0.06747 -54.5 < 2e-16 ***

## R version 3.1.0 (2014-04-10) ## Platform: x86_64-redhat-linux-gnu
(64-bit) ## ## other attached packages: ## [1] mgcv_1.8-4 nlme_3.1-119
stringr_0.6.2 pander_0.5.1 ## [5] blme_1.0-2 formr_0.1.11 lme4_1.1-7
Rcpp_0.11.4 ## [9] Matrix_1.1-5 ggplot2_1.0.0 data.table_1.9.5 knitr_1.9

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