I am having trouble fitting a mixed effect zero inflated negative binomial model to my data using the GLMMadaptive package:
mixed_model(abun ~ TREATMENT + Size + R , random = ~1 | PLOT.TR, data = abunIP, family = zi.negative.binomial(), zi_fixed = ~grass.cut)
Error in chol.default(X[[i]], ...) : the leading minor of order 1 is not positive definite I can't find anything about this particular error message for these models.
My data has 978 rows. The resonse
abun is count data (flowers).
PLOT.TR are factors indicating plot type and a unique plot ID, respectively.
R are continuous variables.
grass.cut indicates whether weeding occurred before a survey, which could reduce the
abun value, often to 0, which is why I am trying to use a zero-inflated model.
Following Dimitris' answer below, I found that the combination of solutions 1 and 2 worked for me, though I made the change
list(betas = rep(0,7)) because
TREATMENT is a factor with 5 levels.
By only adding the
initial_values argument, I got the error message
Error in mixed_fit(y, X, Z, X_zi, Z_zi, id, offset, offset_zi, family, : A large coefficient value has been detected during the optimization. Please re-scale you covariates. Alternatively, this may due to a divergence of the optimization algorithm, indicating that an overly complex model is fitted to the data. For example, this could be caused when including random-effects terms (e.g., in the zero-inflated part) that you do not need. Otherwise, adjust the 'max_coef_value' control argument.
iter_EM = 0 in addition resulted in no error messages.
I have a follow-up question: Conceptually, what is
iter_EM = 0 doing, and is it addressing the previous error message I get by specifying