clustMDlist {clustMD} R Documentation

## Model Based Clustering for Mixed Data

### Description

A function that fits the clustMD model to a data set consisting of any combination of continuous, binary, ordinal and nominal variables. This function is a wrapper for clustMD that takes arguments as a list.

### Usage

clustMDlist(arglist)


### Arguments

 arglist a list of input arguments for clustMD. See clustMD.

### Value

A clustMD object. See clustMD.

### References

McParland, D. and Gormley, I.C. (2016). Model based clustering for mixed data: clustMD. Advances in Data Analysis and Classification, 10 (2):155-169.

clustMD

### Examples

    data(Byar)

# Transformation skewed variables
Byar$Size.of.primary.tumour <- sqrt(Byar$Size.of.primary.tumour)
Byar$Serum.prostatic.acid.phosphatase <- log(Byar$Serum.prostatic.acid.phosphatase)

# Order variables (Continuous, ordinal, nominal)
Y <- as.matrix(Byar[, c(1, 2, 5, 6, 8, 9, 10, 11, 3, 4, 12, 7)])

# Start categorical variables at 1 rather than 0
Y[, 9:12] <- Y[, 9:12] + 1

# Standardise continuous variables
Y[, 1:8] <- scale(Y[, 1:8])

# Merge categories of EKG variable for efficiency
Yekg <- rep(NA, nrow(Y))
Yekg[Y[,12]==1] <- 1
Yekg[(Y[,12]==2)|(Y[,12]==3)|(Y[,12]==4)] <- 2
Yekg[(Y[,12]==5)|(Y[,12]==6)|(Y[,12]==7)] <- 3
Y[, 12] <- Yekg

argList <- list(X=Y, G=3, CnsIndx=8, OrdIndx=11, Nnorms=20000,
MaxIter=500, model="EVI", store.params=FALSE, scale=TRUE,
startCL="kmeans", autoStop=FALSE, ma.band=50, stop.tol=NA)

## Not run:
res <- clustMDlist(argList)

## End(Not run)



[Package clustMD version 1.2.1 Index]