OmicsMarkeR (version 1.4.2)

create.corr.matrix: Correlated Multivariate Data Generator

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

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)