从R中的for循环输出多个向量

Question

As someone relatively new to R I'm having an issue with creating a for loop.

I have a very large data set with 9000 observations and 25 categorical variables, which I've transformed into binary data and preformed hierarchical clustering. Now I want to try K-Modes clustering to produce an Elbow Plot using the "within-cluster simple-matching distance for each cluster", which is outputted from kmodes$withindiff . I can sum this for each of the k in 1:8 clusters to get the Elbow Plot.

library(klaR)
for(k in 1:8)
{
WCSM[k] <- sum(kmodes(data,k,iter.max=100)$withindiff)
}
plot(1:8,WCSM,type="b", xlab="Number of Clusters",ylab="Within-Cluster 
Simple-Matching Distance Summed", main="K-modes Elbow Plot")

My issue is that I want further output from k-modes. For each k in 1:8 I would like to get the vector of integers indicating the cluster to which each object is allocated to given by kmodes$cluster . I need to create a for loop that loops through each k in 1:8 and saves each of the outputs into 8 separate vectors. But I don't know how to do such a for loop. I could just run the 8 lines of code separately but they each take 15mins to run with iter.max=10 so increasing this to iter.max=100 will need to be left running overnight so a loop would be useful.

cl.kmodes2=kmodes(data, 2,iter.max=100)
cl.kmodes3=kmodes(data, 3,iter.max=100)
cl.kmodes4=kmodes(data, 4,iter.max=100)
cl.kmodes5=kmodes(data, 5,iter.max=100)
cl.kmodes6=kmodes(data, 6,iter.max=100)
cl.kmodes7=kmodes(data, 7,iter.max=100)
cl.kmodes8=kmodes(data, 8,iter.max=100)

Ultimately I want to compare the results from the hierarchical binary clustering to the k-modes clustering by getting the Adjusted Rand Index. For example, cutting the tree at k=4 for the hierarchical cluster and comparing this to a 4 cluster solution from k-modes:

dist.binary = dist(data, method="binary")
cl.binary = hclust(dist.binary, method="complete")
hcl.4 = cutree(cl.binary, k = 4)
tab = table(hcl.4, cl.kmodes4$cluster)
library(e1071)
classAgreement(tab)

Answer 1

The best method is to put the output from your clusters into a named list:

library(klaR)

myClusterList <- list()

for(k in 1:8) {
  myClusterList[[paste0("k.", i)]] <- kmodes(data, i,iter.max=100)
}

You can then pull out the any of the contents easily:

sum(myClusterList[["k.1"]]$withindiff)

or

sum(myClusterList[[1]]$withindiff)

You can also save the list to use in future R sessions, see ?save .

Answer 2

I agree with Imo, using a list is the best solution. If you don't want to do that, you could also use assign() to create a new vector in every iteration:

library(klaR)
for(k in 1:8) {
  assign(paste("cl.kmodes", k, sep = ""), kmodes(data, k, iter.max = 100))
}