## @knitr eeg-tables
library(dplyr)
prefix = "eeg"
vecmeans <- readRDS(cache_file("vecmeans", prefix))
ABmeans <- readRDS(cache_file("ABmeans", prefix))
ABacc <- ABmeans %>% lapply(., function(x) x["Balanced.Accuracy", ]) %>%
bind_rows(.) %>%
data.frame
vecacc <- vecmeans %>% lapply(., function(x) x["Balanced.Accuracy", ]) %>%
bind_rows(.) %>%
data.frame
combacc <- rbind(ABacc, vecacc)
names(combacc) <- paste0("ch", 1:6)
methods <- c("A", "B", "A+B", "8", "15", "30")
row.names(combacc) <- methods
combacc <- apply(combacc, 2, round, digits = 2)
names(combacc) <- paste0("Channel ", 1:6)
knitr::kable(combacc, caption = "Balanced accuracy of each models and channel combination.")
## @knitr baseline
basedf <- readRDS(cache_file("basestats", prefix))
names(basedf) <- c("Sensitivity", "Specificity", "Accuracy")
stargazer::stargazer(basedf, summary = FALSE, label = "fig:example",
title = "Prediction performance of baseline classifier")
## @knitr comb-sd
# sd
res_vec <- readRDS(cache_file("res_vec", prefix))
resAB <- readRDS(cache_file("resAB", prefix))
#
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