View source: R/DIAG_diagnostics.R

diagnostics | R Documentation |

Calculates diagnostic metrics using output from the `spBFA`

model.

diagnostics( object, diags = c("dic", "dinf", "waic"), keepDeviance = FALSE, keepPPD = FALSE, Verbose = TRUE, seed = 54 )

`object` |
A |

`diags` |
A vector of character strings indicating the diagnostics to compute. Options include: Deviance Information Criterion ("dic"), d-infinity ("dinf") and Watanabe-Akaike information criterion ("waic"). At least one option must be included. Note: The probit model cannot compute the DIC or WAIC diagnostics due to computational issues with computing the multivariate normal CDF. |

`keepDeviance` |
A logical indicating whether the posterior deviance distribution is returned (default = FALSE). |

`keepPPD` |
A logical indicating whether the posterior predictive distribution at each observed location is returned (default = FALSE). |

`Verbose` |
A boolean logical indicating whether progress should be output (default = TRUE). |

`seed` |
An integer value used to set the seed for the random number generator (default = 54). |

To assess model fit, DIC, d-infinity and WAIC are used. DIC is based on the deviance statistic and penalizes for the complexity of a model with an effective number of parameters estimate pD (Spiegelhalter et al 2002). The d-infinity posterior predictive measure is an alternative diagnostic tool to DIC, where d-infinity=P+G. The G term decreases as goodness of fit increases, and P, the penalty term, inflates as the model becomes over-fit, so small values of both of these terms and, thus, small values of d-infinity are desirable (Gelfand and Ghosh 1998). WAIC is invariant to parametrization and is asymptotically equal to Bayesian cross-validation (Watanabe 2010). WAIC = -2 * (lppd - p_waic_2). Where lppd is the log pointwise predictive density and p_waic_2 is the estimated effective number of parameters based on the variance estimator from Vehtari et al. 2016. (p_waic_1 is the mean estimator).

`diagnostics`

returns a list containing the diagnostics requested and
possibly the deviance and/or posterior predictive distribution objects.

Samuel I. Berchuck

Gelfand, A. E., & Ghosh, S. K. (1998). Model choice: a minimum posterior predictive loss approach. Biometrika, 1-11.

Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583-639.

Vehtari, A., Gelman, A., & Gabry, J. (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 1-20.

Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11(Dec), 3571-3594.

###Load pre-computed regression results data(reg.bfa_sp) ###Compute and print diagnostics diags <- diagnostics(reg.bfa_sp) print(unlist(diags))

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