nlnet (version 1.4)

data.gen: Simulated Data Generation

Description

Generating gene matrix as a example of input.

Usage

data.gen(n.genes=100, n.samples=100, n.grps=10, aver.grp.size=10, 
n.fun.types=6, epsilon=0.1, n.depend=0)

Arguments

n.genes

the number of rows of the matrix.

n.samples

the number of columns of the matrix.

n.grps

the number of hidden clusters.

aver.grp.size

averge number of genes in a cluster.

n.fun.types

number of function types to use.

epsilon

noise level.

n.depend

data generation dependence structure. can be 0, 1, 2.

Value

return the data including gene and clustering.

data

the gene matrix

grps

the predicted clustering

Details

The data generation scheme is described in detail in IEEE ACM Trans. Comput. Biol. Bioinform. 10(4):1080-85.

Examples

# NOT RUN {
##generating a gene matrix with 100 genes, some in 5 clusters, and 100 samples per gene.
output<-data.gen(n.genes=100, n.samples=10, n.grps=5)
##get the gene matrix from the source of data.
matrix<-output$data
##get the hiden clusters from the source of data.
grps<-output$grp
# }