The ESTER package implements sequential testing based on evidence ratios computed from the weights of a set of models. These weights correspond to Akaike weights when based on either the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC), and to pseudo-BMA weights when computed from the Widely Applicable Information Criterion (WAIC).
You can install the latest published version from CRAN using:
install.packages("ESTER")Or the development version (recommended) from Github with:
if (!require("devtools") ) install.packages("devtools")
devtools::install_github("lnalborczyk/ESTER", dependencies = TRUE)The ictab function takes as input a named list of models to be compared, and returns a dataframe with the given information criterion and the weight of each model.
library(ESTER)
data(mtcars)
mod1 <- lm(mpg ~ cyl, mtcars)
mod2 <- lm(mpg ~ cyl + vs, mtcars)
mod3 <- lm(mpg ~ cyl * vs, mtcars)
mods <- list(mod1 = mod1, mod2 = mod2, mod3 = mod3)
ictab(mods, aic)
#> modnames ic k delta_ic ic_wt
#> 1 mod1 170.1636 3 0.0000 0.7029
#> 2 mod2 172.5400 4 2.3765 0.2142
#> 3 mod3 174.4389 5 4.2753 0.0829You can study the evolution of sequential ERs using the seqtest function.
data(mtcars)
mod1 <- lm(mpg ~ cyl, mtcars)
mod2 <- lm(mpg ~ cyl + disp, mtcars)
seqtest(ic = aic, mod1, mod2, nmin = 10)
#> ERi ppt ER
#> 1 er 10 0.05008926
#> 2 er 11 0.07979700
#> 3 er 12 0.10363095
#> 4 er 13 0.11969544
#> 5 er 14 0.14678899
#> 6 er 15 1.10881485
#> 7 er 16 8.47867299
#> 8 er 17 6.75795190
#> 9 er 18 2.08641975
#> 10 er 19 2.19488818
#> 11 er 20 1.86450874
#> 12 er 21 2.38524035
#> 13 er 22 2.90472472
#> 14 er 23 3.39367311
#> 15 er 24 3.90918017
#> 16 er 25 1.67451190
#> 17 er 26 1.87769784
#> 18 er 27 2.10269935
#> 19 er 28 2.03674461
#> 20 er 29 2.20410125
#> 21 er 30 1.65463233
#> 22 er 31 1.73448182
#> 23 er 32 2.15556958More detailed information can be found in the main vignette, available online here, or by typing vignette("ESTER") in the console.