View source: R/performance_score.R

performance_score | R Documentation |

Calculates the logarithmic, quadratic/Brier and spherical score from a model with binary or count outcome.

performance_score(model, verbose = TRUE, ...)

`model` |
Model with binary or count outcome. |

`verbose` |
Toggle off warnings. |

`...` |
Arguments from other functions, usually only used internally. |

Proper scoring rules can be used to evaluate the quality of model
predictions and model fit. `performance_score()`

calculates the logarithmic,
quadratic/Brier and spherical scoring rules. The spherical rule takes values
in the interval `[0, 1]`

, with values closer to 1 indicating a more
accurate model, and the logarithmic rule in the interval `[-Inf, 0]`

,
with values closer to 0 indicating a more accurate model.

For `stan_lmer()`

and `stan_glmer()`

models, the predicted values
are based on `posterior_predict()`

, instead of `predict()`

. Thus,
results may differ more than expected from their non-Bayesian counterparts
in **lme4**.

A list with three elements, the logarithmic, quadratic/Brier and spherical score.

Code is partially based on GLMMadaptive::scoring_rules().

Carvalho, A. (2016). An overview of applications of proper scoring rules. Decision Analysis 13, 223–242. doi: 10.1287/deca.2016.0337

`performance_logloss()`

## Dobson (1990) Page 93: Randomized Controlled Trial : counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12) outcome <- gl(3, 1, 9) treatment <- gl(3, 3) model <- glm(counts ~ outcome + treatment, family = poisson()) performance_score(model) ## Not run: if (require("glmmTMB")) { data(Salamanders) model <- glmmTMB( count ~ spp + mined + (1 | site), zi = ~ spp + mined, family = nbinom2(), data = Salamanders ) performance_score(model) } ## End(Not run)

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