Created
December 26, 2022 14:27
-
-
Save njudd/bfadf6afedb2c16c6f88241ea4d7c120 to your computer and use it in GitHub Desktop.
Extracting and plotting AIC weights
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# Nicholas Judd | Donders Institute | |
# 26-12-2022 | |
# njudd.com | |
# replicating https://twitter.com/rogierK/status/1004443199031607296/photo/2 | |
# Wagenmakers, E.-J., & Farrell, S. (2004). | |
# AIC model selection using Akaike weights. | |
# Psychonomic Bulletin & Review, 11(1), 192–196. | |
if (!require(pacman)){ | |
install.packages('pacman') | |
} | |
set.seed(42) # a seed where the noise is uninformative; also a magical number | |
pacman::p_load(qpcR, AICcmodavg) | |
df <- iris # using the iris dataset | |
df$noise <- rnorm(dim(iris)[1]) | |
m1 <- lm(Sepal.Width ~ Petal.Length, data = df) | |
m2 <- lm(Sepal.Width ~ Petal.Length + Petal.Width, data = df) | |
m3 <- lm(Sepal.Width ~ Petal.Length + Petal.Width + Species, data = df) | |
m4 <- lm(Sepal.Width ~ Petal.Length + Petal.Width + Species + noise, data = df) | |
mod_list <- list(m1, m2, m3, m4) | |
mod_list.names <- c('m1', 'm2', 'm3', 'm4') | |
# manually with qpcR::akaike.weights() | |
AICvec <- map_dbl(mod_list, AIC) # AICvec <- c(AIC(m1), AIC(m2), AIC(m3), AIC(m4)) | |
AICvec_weights <- akaike.weights(AICvec)$weights | |
# replicating Rogier's graph | |
plot_df <- data.frame(model = mod_list.names, | |
variable = rep("AIC weight", length(mod_list.names)), | |
values_qpcR = AICvec_weights) | |
ggplot(plot_df, aes(model, values_qpcR, fill = model)) + | |
geom_bar(stat = "identity", position = "dodge") + | |
ylim(0, 1) + | |
xlab("Akaike weights") + | |
theme_classic(base_size = 15) | |
# now with aictable | |
aictab(mod_list, modnames = mod_list.names) | |
# adding the aic weights from aictab to the df | |
plot_df$values_AICcmodavg <- aictab(mod_list, sort =F)$AICcWt | |
ggplot(plot_df, aes(model, values_AICcmodavg, fill = model)) + | |
geom_bar(stat = "identity", position = "dodge") + | |
ylim(0, 1) + | |
xlab("Akaike weights") + | |
theme_classic(base_size = 15) | |
# the values differ slightly... | |
# because aictab uses the second-order Akaike information criterion | |
# which is good when sample size is small | |
# (Burnham & Anderson 2002 recommend its use when n / K < 40) | |
aictab(mod_list, modnames = mod_list.names, second.ord = FALSE) | |
# now the AIC values and weights are identical to the akaike.weights function |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment