create.corr.matrix: Correlated Multivariate Data Generator

In OmicsMarkeR: Classification and Feature Selection for 'Omics' Datasets

Description

Generates a matrix of dimensions dim(U) with induced correlations. Blocks of variables are randomly assigned and correlations are induced. A noise matrix is applied to the final matrix to perturb 'perfect' correlations.

Usage

create.corr.matrix(U, k = 4, min.block.size = 2, max.block.size = 5)

Arguments

U	Numeric matrix
k	Correlation Perturbation - The higher k, the more the data is perturbed. Default k = 4
min.block.size	minimum number of variables to correlate Default min.block.size = 2
max.block.size	maximum number of variables to correlate Default max.block.size = 5

Value

A numberic matrix of dimension dim(U) with correlations induced between variables

Note

Output does not contain classes, may provide externally as classes are irrelevant in this function.

Author(s)

Charles E. Determan Jr.

References

Wongravee, K., Lloyd, G R., Hall, J., Holmboe, M. E., & Schaefer, M. L. (2009). Monte-Carlo methods for determining optimal number of significant variables. Application to mouse urinary profiles. Metabolomics, 5(4), 387-406. http://dx.doi.org/10.1007/s11306-009-0164-4

See Also

create.random.matrix, create.discr.matrix

Examples

# Create Multivariate Matrices

# Random Multivariate Matrix

# 50 variables, 100 samples, 1 standard devation, 0.2 noise factor

rand.mat <- create.random.matrix(nvar = 50, 
                                 nsamp = 100, 
                                 st.dev = 1, 
                                 perturb = 0.2)


# Induce correlations in a numeric matrix

# Default settings
# minimum and maximum block sizes (min.block.size = 2, max.block.size = 5)
# default correlation purturbation (k=4)
# see ?create.corr.matrix for citation for methods

corr.mat <- create.corr.matrix(rand.mat)


# Induce Discriminatory Variables

# 10 discriminatory variables (D = 10)
# default discrimination level (l = 1.5)
# default number of groups (num.groups=2)
# default correlation purturbation (k = 4)

dat.discr <- create.discr.matrix(corr.mat, D=10)

Example output

solo last variable

OmicsMarkeR documentation built on April 28, 2020, 6:54 p.m.