1 Unsupervised RandomForest

rf1 <- randomForest(sdat, proximity = TRUE)
kern <- rf1$prox

pc1 <- prcomp(kern)
el1 <- getElbows(pc1$sdev, plot = TRUE)[1]

el2 <- getElbows(pc1$sdev, plot = FALSE)[2]

#edat <- scale(pc1$x[,1:el1], center = TRUE, scale = TRUE)
edat <- pc1$x[, 1:el1]
pairs(edat, col = gaba + 1, pch = 20, cex = 0.5)

1.1 Kernel/Proximity Matrix in heatmap

image(kern, col = viridis(255))

2 Results

2.1 1-d Heatmap

2.2 Location meda_plots

2.3 Outliers as given by randomForest

2.4 Correlation Matrix

2.5 Cumulative Variance with Elbows

2.6 Paired Hex-binned plot

2.7 Hierarchical GMM Classifications

2.8 Hierarchical GMM Dendrogram

2.9 Stacked Means

2.10 Cluster Means

3 Restricting hGMM to \(K = 2\)

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)
h2lab <- viridis(max(h2$dat$labels$col))
h2col <- h2$dat$labels$col

3.1 K = 2 stacked means plot

p1 <- stackM(h2, ccol = "black", centered = TRUE, depth = 1)
p2 <- stackMraw(sdat, h2$dat$labels$L1, centered = TRUE, depth = 2, ccol = ccol)
grid.arrange(p1,p2, nrow=1)

3.2 Pairs plot colored by true gaba classification

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])

3.3 Pairs plot colored by hGMM cluster classification

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

4 Permutation test for ARI

p0 <- mclust::adjustedRandIndex(pred, gaba)
perms <- foreach(i = 1:1.5e4, .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   375   33
##   TRUE    333   52

5 Summary Table

measurment value
Misclassification Rate 0.4615385
Accuracy 0.5384615
Sensitivity 0.6117647
Specificity 0.529661
Precision 0.1350649
Recall 0.6117647
ARI 0.00492
\(p\)-value for ARI 0.010333
F1-score 0.2212766
TP 52
FP 333
TN 375
FN 33