create.random.matrix: Random Multivariate Data Generator
In cdeterman/OmicsMarkeR: Classification and Feature Selection for 'Omics' Datasets

Description Usage Arguments Value Author(s) References See Also Examples

Generates a matrix of dimensions nvar by nsamp consisting of random numbers generated from a normal distriubtion. This normal distribution is then perturbed to more accurately reflect experimentally acquired multivariate data.

1	create.random.matrix(nvar, nsamp, st.dev = 1, perturb = 0.2)

`nvar`	Number of features (i.e. variables)
`nsamp`	Number of samples
`st.dev`	The variation (i.e. standard deviation) that is typical in datasets of interest to the user. Default `spread = 1`
`perturb`	The amount of perturbation to the normal distribution. Default `perturb = 0.2`

Matrix of dimension nvar by nsamp

Charles E. Determan Jr.

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

create.corr.matrix, create.discr.matrix

# 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)