| loo.cv {blmeco} | R Documentation |
Bayesian leave-one-out cross-validation
Description
Bayesian leave-one-out cross-validation based on the log pointwise predictive density
Usage
loo.cv(mod, nsim = 100, bias.corr = FALSE)
Arguments
mod |
an object obtained by the functions lm or glm |
nsim |
number of Monte Carlo simulations used to describe the posterior distributions. Computing time is large! |
bias.corr |
The leave-one-out cross-validation underestimates predictive fit because each prediction is conditioned n-1 data points. For large n this bias is negligible. For small n, a bias correction is recommended. |
Details
For details see Gelman et al. (2014) p 175
Value
LOO.CV |
leave-one-out cross-validation estimate of out-of-sample predictive fit, (log pointwise predictive density) |
bias.corrected.LOO.CV |
bias corrected leave-one-out cross-validation estimate of out-of-sample predictive fit, (log pointwise predictive density) |
minus2times_lppd |
-2*LOO.CV, transformed LOO.CV to scale of deviance |
est.peff |
estimate for the number of effective parameters |
Author(s)
F. Korner
References
Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A and Rubin DB (2014) Bayesian Data Analysis, Third edn. CRC Press.
See Also
Examples
## Not run:
x <- runif(20)
y <- 2+0.5*x+rnorm(20, 0, 1)
mod <- lm(y~x)
loo.cv(mod, bias.corr=TRUE) # increase nsim!!
## End(Not run)