Description Usage Arguments Details Value Author(s) References Examples

Computes the RMSE/log-likelihood based on leave-one-out cross-validation.

1 |

`object` |
a fitted object model, currently only |

`type` |
the criterion to use, given as a character string,
either |

`...` |
other arguments are currently ignored. |

Leave-one-out cross validation is a `K`-fold cross validation, with `K`
equal to the number of data points in the set `N`. For all `i` from 1
to `N`, the model is fitted to all the data except for `i`-th row and
a prediction is made for that value. The average error is computed and used to
evaluate the model.

`loo`

returns a single numeric value of RMSE or
mean log-likelihood.

Kamil Bartoń, based on code by Carsten Dormann

Dormann, C. et al. (2018) Model averaging in ecology: a review of Bayesian,
information-theoretic, and tactical approaches for predictive inference.
*Ecological Monographs*, 88, 485–504.

1 2 3 4 5 6 7 8 9 10 | ```
fm <- lm(y ~ X1 + X2 + X3 + X4, Cement)
loo(fm, type = "l")
loo(fm, type = "r")
## Compare LOO_RMSE and AIC/c
options(na.action = na.fail)
dd <- dredge(fm, rank = loo, extra = list(AIC, AICc), type = "rmse")
plot(loo ~ AIC, dd, ylab = expression(LOO[RMSE]), xlab = "AIC/c")
points(loo ~ AICc, data = dd, pch = 19)
legend("topleft", legend = c("AIC", "AICc"), pch = c(1, 19))
``` |

```
[1] 1.79803
[1] 2.913451
Fixed term is "(Intercept)"
```

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