diagnose {glmmTMB}

diagnose model problems

Description

EXPERIMENTAL. For a given model, this function attempts to isolate potential causes of convergence problems. It checks (1) whether there are any unusually large coefficients; (2) whether there are any unusually scaled predictor variables; (3) if the Hessian (curvature of the negative log-likelihood surface at the MLE) is positive definite (i.e., whether the MLE really represents an optimum). For each case it tries to isolate the particular parameters that are problematic.

Usage

diagnose(
  fit,
  eval_eps = 1e-05,
  evec_eps = 0.01,
  big_coef = 10,
  big_sd_log10 = 3,
  big_zstat = 5,
  check_coefs = TRUE,
  check_zstats = TRUE,
  check_hessian = TRUE,
  check_scales = TRUE,
  explain = TRUE
)

Arguments

fit

a glmmTMB fit

eval_eps

numeric tolerance for 'bad' eigenvalues

evec_eps

numeric tolerance for 'bad' eigenvector elements

big_coef

numeric tolerance for large coefficients

big_sd_log10

numeric tolerance for badly scaled parameters (log10 scale), i.e. for default value of 3, predictor variables with sd less than 1e-3 or greater than 1e3 will be flagged)

big_zstat

numeric tolerance for Z-statistic

check_coefs

identify large-magnitude coefficients? (Only checks conditional-model parameters if a (log, logit, cloglog, probit) link is used. Always checks zero-inflation, dispersion, and random-effects parameters. May produce false positives if predictor variables have extremely large scales.)

check_zstats

identify parameters with unusually large Z-statistics (ratio of standard error to mean)? Identifies likely failures of Wald confidence intervals/p-values.

check_hessian

identify non-positive-definite Hessian components?

check_scales

identify predictors with unusually small or large scales?

explain

provide detailed explanation of each test?

Details

Problems in one category (e.g. complete separation) will generally also appear in "downstream" categories (e.g. non-positive-definite Hessians). Therefore, it is generally advisable to try to deal with problems in order, e.g. address problems with complete separation first, then re-run the diagnostics to see whether Hessian problems persist.

Value

a logical value based on whether anything questionable was found