ictab {ESTER} | R Documentation |
Returns a table with weights of a set of models, based on various
information criteria. Currently, ictab
supports the computation of
Akaike weights from the aic
or the bic
computed on lm
or merMod
models, as well as the computation of pseudo-BMA weights,
computed from the WAIC or LOOIC of brmsfit
models.
ictab(mods, ic, ...)
mods |
Should be a named list of models, of class |
ic |
Indicates which information criterion to use. Current supported
information criteria include |
... |
Additional parameters to be passed to |
An object of class data.frame
, which contains the value of the
information criterion (either AIC, BIC, WAIC or LOOIC), the number of parameters
(k for AIC and BIC or p for WAIC or LOOIC), the delta_IC (for AIC and BIC) or the
elpd for models compared with WAIC or LOOIC, and the weight of each
model (Akaike weights for AIC or BIC and pseudo-BMA weights for WAIC or LOOIC).
Ladislas Nalborczyk <ladislas.nalborczyk@gmail.com>
Burnham, K. P., \& Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretical Approach. 2d ed. New York: Springer-Verlag.
Burnham, K. P., \& Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods and Research, 33(2), 261-304.
Yao, Y. P., Vehtari, A., Simpson, D., \& Gelman, A. (2017). Using stacking to average Bayesian predictive distributions.
library(ESTER)
data(mtcars)
mod1 <- lm(mpg ~ cyl, mtcars)
mod2 <- lm(mpg ~ cyl + vs, mtcars)
mod3 <- lm(mpg ~ cyl + vs + I(vs^2), mtcars)
mod4 <- lm(mpg ~ cyl * vs, mtcars)
mods <- list(mod1 = mod1, mod2 = mod2, mod3 = mod3, mod4 = mod4)
ictab(mods, aic)
ictab(mods, bic)
## Not run:
library(brms)
mod1 <- brm(mpg ~ cyl, mtcars)
mod2 <- brm(mpg ~ cyl + vs, mtcars)
mods <- list(m1 = mod1, m2 = mod2)
ictab(mods, LOO, reloo = TRUE, k_threshold = 0.6, cores = 2)
## End(Not run)