emb <- embed(sdat, "Isomap", knn = 7, ndim = 2)
## 2018-02-28 09:16:19: Isomap START
## 2018-02-28 09:16:19: constructing knn graph
## 2018-02-28 09:16:19: calculating geodesic distances
## 2018-02-28 09:16:19: cmdscale
## 2018-02-28 09:16:20: post processing
edat <- emb@data@data
cols <- c("black", "magenta")[gabaID$gaba+1]
acols <- alpha(cols, 0.35)
pairs(emb@data@data, pch = 19, col = acols, cex = 0.2)
Here we are restricting hierarchical GMM to only go through on level. We are comparing the cluster results to the gaba
labels.
set.seed(3144)
h2 <- hmc(edat, maxDepth = 2, ccol = ccol)
h3 <- hmc(edat, maxDepth = 3, ccol = ccol)
h2lab <- viridis(max(h2$dat$labels$col))
h2col <- h2$dat$labels$col
h3lab <- viridis(max(h3$dat$labels$col))
h3col <- h3$dat$labels$col
p1 <- stackM(h2, ccol = "black", centered = TRUE, depth = 1)
p12 <- stackM(h3, ccol = "black", centered = TRUE, depth = 3)
L1 <- h3$dat$labels$L1
p2 <- stackMraw(as.data.frame(sdat), L1, ccol = ccol, depth = 3, centered = TRUE)
grid.arrange(p12, p2, nrow = 1)
cols <- c("black", "magenta")[gabaID$gaba+1]
acols <- alpha(cols, 0.35)
#pairs(h2$dat$data, pch = 19, cex = 0.7, col = acols)
plot(h2$dat$data, col = acols, pch = c(19,3)[gaba+1], cex = c(0.5,1)[gaba+1])
pairs(sdat, col = acols, pch = c(19,3)[gaba+1], cex = c(0.5,1)[gaba+1])
acols2 <- alpha(h2lab[h2$dat$labels$col], 0.45)
par(bg = "gray45")
plot(h2$dat$data, pch = c(3,20)[gaba + 1], cex = 1, col = acols2)
pairs(sdat, pch = 19, cex = 0.7, col = acols2)
dev.off()
## null device
## 1
permn <- 1.5e4
p0 <- mclust::adjustedRandIndex(pred, gaba)
perms <- foreach(i = 1:permn, .combine = c) %dopar% {
set.seed(i*2)
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 654 25
## TRUE 54 60
measurment | value |
---|---|
Misclassification Rate | 0.0996217 |
Accuracy | 0.9003783 |
Sensitivity | 0.7058824 |
Specificity | 0.9237288 |
Precision | 0.5263158 |
Recall | 0.7058824 |
ARI | 0.4773253 |
\(p\)-value for ARI | 0.000067 |
F1-score | 0.6030151 |
TP | 60 |
FP | 54 |
TN | 654 |
FN | 25 |