"Input datasets must be dataframes" error in kamila package in R

I have a mixed type data set, one continuous variable, and eight categorical variables, so I wanted to try kamila clustering. It gives me an error when I use one continuous variable, but when I use two continuous variables it is working.

library(kamila)

data <- read.csv("mixed.csv",header=FALSE,sep=";")

conInd <- 9
conVars <- data[,conInd]
conVars <- data.frame(scale(conVars))

catVarsFac <- data[,c(1,2,3,4,5,6,7,8)]
catVarsFac[] <- lapply(catVarsFac, factor)

kamRes <- kamila(conVars, catVarsFac, numClust=5, numInit=10,calcNumClust = "ps",numPredStrCvRun = 10, predStrThresh = 0.5)

Error in kamila(conVar = conVar[testInd, ], catFactor = catFactor[testInd, : Input datasets must be dataframes

Solution

I think the problem is that the function assumes that you have at least two of both data types (i.e. >= 2 continuous variables, and >= 2 categorical variables). It looks like you supplied a single column index (conInd = 9, just column 9), so you have only one continuous variable in your data. Try adding another continuous variable to your continuous data.

I had the same problem (with categoricals) and this approach fixed it for me.

I think the ultimate source of the error in the program is at around line 170 of the source code. Here's the relevant snippet...

numObs <- nrow(conVar)
numInTest <- floor(numObs/2)
for (cvRun in 1:numPredStrCvRun) {
  for (ithNcInd in 1:length(numClust)) {
    testInd <- sample(numObs, size = numInTest, replace = FALSE)
    testClust <- kamila(conVar = conVar[testInd,], 
                        catFactor = catFactor[testInd, ], 
                        numClust = numClust[ithNcInd], 
                        numInit = numInit, conWeights = conWeights, 
                        catWeights = catWeights, maxIter = maxIter, 
                        conInitMethod = conInitMethod, catBw = catBw, 
                        verbose = FALSE)

When the code partitions your data into a training set, it's selecting rows from a one-column data.frame, but that returns a vector by default in that case. So you end up with "not a data.frame" even though you did supply a data.frame. That's where the error comes from.

If you can't dig up another variable to add to your data, you could edit the code such that the calls to kamila in the cvRun for loop wrap the data.frame() function around any subsetted conVar or catFactor, e.g.

testClust <- kamila(conVar    = data.frame(conVar[testInd,]), 
                    catFactor = data.frame(catFactor[testInd,], ... )

and just save that as your own version of the function called say, my_kamila, which you could use instead.

Hope this helps.