MMOC (version 0.1.1.0)

clustStruct: Generate multi-view data sets with simple cluster structures

Description

Generates multiple data sets from a multivariate normal distribution using the mvrnorm function from the MASS package.

Usage

clustStruct(n, p, k, noiseDat = "random", randNoise = 2)

Value

A list of n\(\times\)p data frames with the specified number of groups

Arguments

n

An integer, the sample size for all generated data sets

p

An integer, the number of columns (features) in each generated data set

k

An integer or vector, the number of distinct clusters in each generated data set. n/k must be an integer for all values of k

noiseDat

Either the character string 'random', indicating the covariance matrix is a diagonal matrix with randNoise along the diagonal, or a valid covariance matrix

randNoise

The value along the diagonal when noiseDat='random'

Details

The function accepts k as a vector. It splits data into k groups with means c(0, 2^( 1:(kk-1) ) ), e.g., when k=3 the data will be split into 3 groups with means 0, 2, and 4, respectively. The covariance matrix is either a diagonal matrix with randNoise (an integer) along the diagonal, or a given matrix.

Examples

## A single view with 30 variables and 3 groups
s1 <- clustStruct(n=120, p=30, k=3, noiseDat='random')[[1]]

## Multiple views with 30 variables
## View 1 has 2 groups and View 2 has 3 groups
s2 <- clustStruct(n=120, p=30, k=c(2,3), noiseDat='random')

## Multiple views with 30 variables
## View 1 has 2 groups, View 2 has 3, and View 3 has 3 groups
s3 <- clustStruct(n=120, p=30, k=c(2,3,3), noiseDat='random')

## Three view study.
# View 1: 2 groups, 30 variables, random noise = 5
# View 2: 3 groups, 60 variables, random noise = 2
# View 3: 4 groups, 45 variables, random noise = 4

s4 <- clustStruct(n=120, k=c(2,3,4), p=c(30,60,45), randNoise=c(5,2,4))