Strange pattern of non-convergence when flattening in mixed effects logistic regression - logistic-regression

Question

I'm running a simulation study on the effects of adding fractional numbers of successes and failures, which I'll call C, to mixed effects logistic regressions. I've simulated 2000 datasets and modeled each with 5 logistic regressions (adding an C of either 1, .5, .25, .1 and .05). The models converge on the majority of the datasets, but ~200 fail to converge when I add an C of .25 and ~50 fail to converge when I add an C of .5 (Sometimes I get a warning message and sometimes I get implausible standard errors). I very rarely see any evidence of non-convergence with the other values (I've looked at warning messages, standard errors and the ratio of highest to lowest eigenvalues in the random effects matrix). Even in the datasets that fail to converge when C = .25, slightly changing C often solves the problem, such as in this example (data sets available here: https://www.dropbox.com/sh/ro92mtjkpqwlnws/AADSVzcNvl0nnnzCEF5QGM6qa?oref=e&n=19939135)
m7 <- glmer(cbind(Data + .25, (10+.5- (Data + .25))) ~ Group*Condition + (1 + Condition |ID), family="binomial", data=df2)
Warning messages:
1: In eval(expr, envir, enclos) : non-integer counts in a binomial glm!
2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?
summary(m7)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: cbind(Data + 0.25, (10 + 0.5 - (Data + 0.25))) ~ Group * Condition + (1 + Condition | ID)
Data: df2
AIC BIC logLik deviance df.resid
7001.1 7040.0 -3493.6 6987.1 1913
Scaled residuals:
Min 1Q Median 3Q Max
-3.5444 -0.6387 0.0143 0.6945 2.9802
Random effects:
Groups Name Variance Std.Dev. Corr
ID (Intercept) 0.26598 0.5157
Condition 0.06413 0.2532 0.66
Number of obs: 1920, groups: ID, 120
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.760461 0.001226 1436.5 <2e-16 ***
Group -1.816952 0.001225 -1483.0 <2e-16 ***
Condition -0.383383 0.001226 -312.7 <2e-16 ***
Group:Condition -0.567517 0.001225 -463.2 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) Group Condtn
Group 0.000
Condition 0.000 0.000
Group:Cndtn 0.000 0.000 0.000
m8 <- glmer(cbind(Data + .2, (10+.4- (Data + .2))) ~ Group*Condition + (1 + Condition |ID), family="binomial", data=df2)
Warning message:
In eval(expr, envir, enclos) : non-integer counts in a binomial glm!
summary(m8)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: cbind(Data + 0.2, (10 + 0.4 - (Data + 0.2))) ~ Group * Condition + (1 + Condition | ID)
Data: df2
AIC BIC logLik deviance df.resid
6929.3 6968.2 -3457.6 6915.3 1913
Scaled residuals:
Min 1Q Median 3Q Max
-3.5724 -0.6329 0.0158 0.6945 2.9976
Random effects:
Groups Name Variance Std.Dev. Corr
ID (Intercept) 0.2698 0.5194
Condition 0.0652 0.2553 0.66
Number of obs: 1920, groups: ID, 120
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.76065 0.07850 22.429 < 2e-16 ***
Group -1.81762 0.10734 -16.933 < 2e-16 ***
Condition -0.38111 0.06377 -5.977 2.28e-09 ***
Group:Condition -0.57033 0.08523 -6.692 2.21e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) Group Condtn
Group -0.732
Condition -0.033 0.025
Group:Cndtn 0.029 0.045 -0.758
As this is a simulation study, I'm not especially interested in making those models converge, but I'd like to understand why they're not converging. Does anybody have any ideas?