iter_matrix: Genetic algorithm for generating correlated binary data

In jkruppa/RepeatedHighDim: Global tests for expression data of high-dimensional sets of molecular features.

Description

Starts the genetic algorithm based on a start matrix with specified marginal probabilities.

Usage

iter_matrix(X0, R, T = 1000, e.min = 1e-04, plt = TRUE, perc = TRUE)

Arguments

X0	Start matrix with specified marginal probabilities. Can be generated by start_matrix.
R	Desired correlation matrix the data should have after running the genetic algorithm.
T	Maximum number of iterations after which the genetic algorithm stops.
e.min	Minimum error (RMSE) between the correlation of the iterated data matrix and R.
plt	Boolean parameter that indicates whether to plot e.min versus the iteration step.
perc	Boolean parameter that indicates whether to print the percentage of iteration steps relativ to T.

Details

In each step, the genetic algorithm swaps two randomly selected entries in each column of X0. Thus it can be guaranteed that the marginal probabilities do not change. If the correlation matrix is closer to R than that of x0(t-1), X0(t) replaces X0(t-1).

Value

A list with four entries:

Xt

Final representativ data matrix with specified marginal probabilities and a correlation as close as possible to R

t

Number of performed iteration steps (t <= T)

Rt

Empirical correlation matrix of Xt

RMSE

Final RSME error between desired and achieved correlation matrix

Author(s)

Jochen Kruppa, Klaus Jung

Examples

### Generation of the representive matrix Xt
X0 <- start_matrix(p = c(0.5, 0.6), k = 1000)
Xt <- iter_matrix(X0, R = diag(2), T = 10000, e.min = 0.00001)$Xt

### Drawing of a random sample S of size n = 10
S <- Xt[sample(1:1000, 10, replace = TRUE),]

jkruppa/RepeatedHighDim documentation built on May 19, 2019, 12:45 p.m.