R - 'princomp' can only be used with more units than variables

10
votes

I am using R software (R commander) to cluster my data. I have a smaller subset of my data containing 200 rows and about 800 columns. I am getting the following error when trying kmeans cluster and plot on a graph. "'princomp' can only be used with more units than variables"

I then created a test doc of 10 row and 10 columns whch plots fine but when I add an extra column I get te error again. Why is this? I need to be able to plot my cluster. When I view my data set after performing kmeans on it I can see the extra results column which shows which clusters they belong to.

IS there anything I am doing wrong, can I ger rid of this error and plot my larger sample??? Please help, been wrecking my head for a week now. Thanks guys.

3
rcluster-analysisk-meanspcar-commander
Can you, please, put a reproducible example? – aL3xa
@aL3xa a sample of my data would be - THis is my test table below. 3 rows and 11 cols. Is there any way i can use kmeans and plot these using R. 'free ipod apple tech google cat great mouse hello dog ball, 0.174292915 0.232990001 0.174292915 0 0 0 0 0 0 0 0, 0 0.349485002 0.261439373 0 0 0 0 0 0 0 0, 0.174292915 0 0 0.232990001 0.232990001 0 0 0 0 0 0 ' – CoolSteve
Please edit your question and put the data there. Not in the comments – JohnP

3 Answers

24
votes

The problem is that you have more variables than sample points and the principal component analysis that is being done is failing.

In the help file for princomp it explains (read ?princomp):

 ‘princomp’ only handles so-called R-mode PCA, that is feature
 extraction of variables.  If a data matrix is supplied (possibly
 via a formula) it is required that there are at least as many
 units as variables.  For Q-mode PCA use ‘prcomp’.
4
votes

Principal component analysis is underspecified if you have fewer samples than data point. Every data point will be it's own principal component. For PCA to work, the number of instances should be significantly larger than the number of dimensions.

Simply speaking you can look at the problems like this: If you have n dimensions, you can encode up to n+1 instances using vectors that are all 0 or that have at most one 1. And this is optimal, so PCA will do this! But it is not very helpful.

1
votes

you can use prcomp instead of princomp