create.random.matrix {OmicsMarkeR} | R Documentation |
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.
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 |
perturb |
The amount of perturbation to the normal distribution.
Default |
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)