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i have a multilevel model at firm and local authority district level, with both firm level and LAD level predictors. It is a binary logistic model, the outcome is presence of women on the board of a firm (true/false). I scaled any variables that aren't percentages. Lots are binary -could this be a problem? My single level logit regression worked well.

multilevel_model1 <- glmer(at_least_1 ~ log_board_size + log_employees + scaled_company_age + self_employed + scaled_average_age +
                            industry + tech_employment + log_population_density + scaled_gpg +
                             level4_qual_f + economic_activity_f + private_sector_f +ft_median_pay_f +
                            (1 | ladcd.x),
                          data = multi_level_data,
                          family = binomial(link = "logit"),
                          control = glmerControl(optCtrl = list(maxfun = 10000)))
summary(multilevel_model1)

I tried changing the optimizer to bobyqua (?) but it didn't help. i get this error:

Model failed to converge with max|grad| = 0.207839 (tol = 0.002, component 1) Model is nearly unidentifiable: very large eigenvalue

  • Rescale variables? Model is nearly unidentifiable: large eigenvalue ratio
  • Rescale variables?
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  • It is impossible for us to know what the problem is without a reproducible example. That said, have you tried the advice given by the warning - rescale variables? Rescaling continuous variables to have mean = 0 and variance = 1 may help.
    – DaveArmstrong
    Commented Apr 11 at 14:54
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