CODEWITHSUNDEEP
rcluster-analysisfactor-analysis
JK3301
JK3301

Reputation: 1

R - Clustering after factor analysis

Anybody knows how to recreate this data in R? Below is the cluster output that I want to have after doing factor analysis. 

Cluster centers   Value 1   Value 2   Value 3   Value 4  
FACTOR1            -0.049   -1.481    0.505     0.651    
FACTOR2            0.691    -0.161    -0.633    -0.547      
FACTOR3            0.251    -0.265    0.611     -1.522    
-------------------------------------------------------
No. of case         257       93       174       96

For my data I have 620 rows of observations and 20 columns of questions, 620x20. I first did factor analysis in R and factorized the 620 rows of observations into 3 factors producing the output as a 20x3 data frame shown below. 

 Matrix   Factor 1   Factor 2   Factor 3   
 Q1       0.646      -0.095     0.041   
 Q2       0.630      0.047      0.124     
 Q3       ...        ...        ...    
 Q4       ...        ...        ...
 ...
 Q20      0.419      0.181      0.337

Next I want to perform cluster analysis on 620 data, where the clusters consider the different factors scores as the output at the top. I am not sure how to do that in R.

Upvotes: 0

Views: 1356

Answers (1)

milan
milan

Reputation: 5000

This is an example. I generated a 30x3 matrix, used kmeans clustering specifying that 4 clusters are required. Note, you can use any other clustering algorithm. Then, I calculated the clusters centers (mean by cluster) using aggregate. These centers can now be used to apply your classification in a new dataset by finding out, for each sample, what center that sample is closest to (e.g., using Euclidean distance).

set.seed(1); d <- matrix(rnorm(90), ncol=3)
kd <- kmeans(d, centers=4)
cluster <- kd$cluster
dd <- as.data.frame(cbind(d, cluster))
t(aggregate(dd, by=list(dd$cluster), FUN=mean))[c(1,5)*-1,]

         [,1]        [,2]        [,3]       [,4]
V1  0.8321043 -0.01501747 -0.09144934 -1.8916013
V2  0.0121109 -0.51743551  0.85714652 -0.5389448
V3 -0.4478400  0.17132066  0.99685057 -0.9206161

Upvotes: 1

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