Computes the RMSE/log-likelihood based on leave-one-out cross-validation.
a fitted object model, currently only
the criterion to use, given as a character string,
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
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.
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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))
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