OmicsMarkeR (version 1.4.2)

create.discr.matrix: Discriminatory Multivariate Data Generator

Description

Generates a matrix of dimensions dim(U) with induced correlations. D variables are randomly selected as discriminatory. If num.groups = 2 then discrimination is induced by adding and subtracting values derived from the level of of discrimination, l, for the classes respectively. Multi-class datasets have a few further levels of randomization. For each variable, a random number of the groups are selected as discriminating while the remaining groups are not altered. For each discriminatory group, a unique change is provided by randomly assigning addition or subtraction of the discrimination factor. For example, if 3 groups are selected and two groups are assigned as addition and the third subtraction, the second addition is multiplied by its number of replicates. E.g. (1,1,-1) -> (1,2,-1). These values are randomized and then multiplied by the respective discrimination factor. The resulting values are then added/subtracted from the respective groups. A noise matrix is applied to the final matrix to perturb 'perfect' discrimination.

Usage

create.discr.matrix(V, D = 20, l = 1.5, num.groups = 2, k = 4)

Arguments

V

Numeric matrix

D

Number of discriminatory variables induced. Default D = 20

l

Level of discrimination, higher = greater separation. Default l = 1.5

num.groups

Number of groups in the dataset

k

Correlation Perturbation - The higher k, the more the data is perturbed. Default k = 4

Value

List of the following elements

discr.mat

Matrix of dimension dim(V)+1 with discriminatory variables induced and the .classes added to the end of the matrix.

features

Vector of features that were induced to be discriminatory.

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

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)