I have a data frame with 2 variables and I want to use the clusGap function to find the number of clusters that would be optimal to use. This code has a similar result:
library(cluster)
x <- as.vector(runif(100, 0, 1))
y <- as.vector(runif(100, 0, 1))
df <- data.frame(x, y)
gap_stat <- clusGap(df, FUN = kmeans, nstart = n,
K.max = 10, B = 50)
gap_stat
Result:
Clustering Gap statistic ["clusGap"] from call:
clusGap(x = df, FUNcluster = kmeans, K.max = 10, B = 50, nstart = n)
B=50 simulated reference sets, k = 1..10; spaceH0="scaledPCA"
--> Number of clusters (method 'firstSEmax', SE.factor=1): 1
logW E.logW gap SE.sim
[1,] 2.569315 2.584217 0.0149021144 0.03210076
[2,] 2.285049 2.284537 -0.0005116382 0.03231529
[3,] 2.053193 2.033653 -0.0195399122 0.03282376
[4,] 1.839085 1.835590 -0.0034952935 0.03443303
[5,] 1.691219 1.708479 0.0172603348 0.03419994
[6,] 1.585084 1.597277 0.0121935992 0.03440672
[7,] 1.504763 1.496853 -0.0079104306 0.03422321
[8,] 1.416176 1.405903 -0.0102731340 0.03371149
[9,] 1.333721 1.323658 -0.0100626869 0.03245958
[10,] 1.253199 1.250366 -0.0028330498 0.03034140
As you can see in line 4, the optimal number of clusters is 1. I would like the function to have 1 as an output. I need the optimal number of outputs to be an object in the environment, such as n is 1.
Typically such information is somewhere directly inside the object, like gap_stat$nc. To look for it str(gap_stat) would typically suffice.
In this case, however, the above strategy isn't enough. But the fact that you can see your number of interest in the output, means that print.clusGap (because the class of gap_stat is clusGap) will show how to obtain this number. So, inspecting cluster:::print.clusGap leads to
maxSE(f = gap_stat$Tab[, "gap"], SE.f = gap_stat$Tab[, "SE.sim"])
# [1] 1