Here we are restricting hierarchical GMM to only go through on level. We are comparing the cluster results to the gaba
labels.
set.seed(314)
h2 <- hmc(sdat, maxDepth = 2, ccol = ccol, maxDim = 12)
h2lab <- viridis(max(h2$dat$labels$col))
stackM(h2, ccol = ccol, centered = TRUE, depth = 1)
cols <- c("black", "magenta")[gabaID$gaba+1]
acols <- alpha(cols, 0.35)
pairs(h2$dat$data, pch = 19, cex = 0.4, col = acols)
acols2 <- alpha(h2lab[h2$dat$labels$col], 0.5)
pairs(h2$dat$data, pch = 19, cex = 0.4, col = acols2)
p0 <- mclust::adjustedRandIndex(pred, gaba)
perms <- foreach(i = 1:1.5e4, .combine = c) %dopar% {
set.seed(i*5)
mclust::adjustedRandIndex(sample(pred), gaba)
}
pv0 <- sum(c(perms,p0) >= p0)/length(perms)
hist(perms, xlim = c(min(perms), p0 + 0.25*p0),
main = "permutation test of ARI values", probability = TRUE)
#hist(perms, probability = TRUE)
abline(v = p0, col = 'red')
t1
## truth
## pred FALSE TRUE
## FALSE 639 27
## TRUE 69 58
measurment | value |
---|---|
Misclassification Rate | 0.1210593 |
Accuracy | 0.8789407 |
Sensitivity | 0.6823529 |
Specificity | 0.9025424 |
Precision | 0.4566929 |
Recall | 0.6823529 |
ARI | 0.4043986 |
\(p\)-value for ARI | 0.000067 |
F1-score | 0.5471698 |
TP | 58 |
FP | 69 |
TN | 639 |
FN | 27 |