diagnose | R Documentation |

**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.

```
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
)
```

`fit` |
a |

`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? |

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.

a logical value based on whether anything questionable was found

glmmTMB documentation built on Oct. 7, 2023, 5:07 p.m.

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