K-Centroids Diagnostics Error: object 'std.param2' not found

[JBR]

I've used k-centroids diagnostics many times in the past and I've never encountered this error. I was using a 2018 version when I got the error, so I said what the heck, I'll try the 2019 version. Same issue.

I'm attaching two snipped images: one of the error I receive; and one of my configuration.

I've also Googled the error, but I can't even find anything about the object in question (std.param2) in the R-related documents.

I'm not doing anything I haven't done before with this tool. I'm completely baffled as to what's the problem. Any ideas about what this error is referring to would be much appreciated. Thanks.

[AndrewKramer · Accepted solution]

Hi,

I was able to replicate this on my side. Inspecting the K-Centroids Diagnostic Macro, the following code handles the standardization.

# Handle a requested standardization
if (standardize == "True") {
  z.score <- '%Question.z score%'
  if (z.score == "True") {
    std.type <- "z-score"
    std.param1 <- apply(the.matrix, 2, mean)
    std.param2 <- apply(the.matrix, 2, sd)
    the.matrix <- scale(the.matrix)
	matrix.str <- paste("scale(", matrix.str, ")", sep = "")
  } else {
    std.type <- "Unit interval"
    std.param1 <- apply(the.matrix, 2, min)
    std.param2 <- apply(the.matrix, 2, max) - std.param2
    the.matrix <- unitScale(the.matrix)
	matrix.str <- paste("unitScale(", matrix.str, ")", sep = "")
  }
}

When using unit standardization, the error lies with the following line of code. std.param2 is just being defined, so we cannot subtract by it as it does not exist

 std.param2 <- apply(the.matrix, 2, max) - std.param2

This line of code should be as follows. This would calculate the range, which is necessary for unit standardization.

 std.param2 <- apply(the.matrix, 2, max) - std.param1

Thanks for bringing this to our attention. I will cascade internally. In the meantime, I would recommend you use the Z Score standardization, as that runs without error.

[JBR]

Thank you for the quick response. I kicked myself when reading it b/c I didn't play with the input parameters... but the reason I didn't do so was b/c (in the k-centroids cluster analysis) the unit interval nearly always yields a lower sum of within cluster distances than the z-score, controlling for algorithm (e.g., k-means) and number of clusters in the solution -- thus I usually just use unit interval.

Thanks again.

Joe

[AndrewKramer]

I spoke with our development team, and this issue has been resolved. The fix is currently in QA, and should be included in the next release, 2019.2.

Thanks,

Andrew