ProcMod (version 1.0.8)

simulate_correlation: Simulate n points of dimension p correlated to a reference matrix.

Description

Simulates a set of point correlated to another set according to the procrustean correlation definition. Points are simulated by drawing values of each dimension from a normal distribution of mean 0 and standard deviation equals to 1. The mean of each dimension is forced to 0 (data are centred). By default variable are also scaled to enforce a strandard deviation strictly equal to 1. Covariances between dimensions are not controled. Therefore they are expected to be equal to 0 and reflect only the random distribution of the covariance between two random vectors. The intensity of the correlation is determined by the r2 parameter.

Usage

simulate_correlation(reference, p, r2, equal_var = TRUE)

Arguments

reference

a numeric matrix to which the simulated data will be correlated

p

an int value indicating the number of dimensions (variables) simulated

r2

the fraction of variation shared between the reference and the simulated data

equal_var

a logical value indicating if the dimensions must be scaled to force sd=1. TRUE by default.

Value

a numeric matrix of nrow(reference) rows and p columns

Examples

# NOT RUN {
sim1 <- simulate_matrix(25,10)
class(sim1)
dim(sim1)
sim2 <- simulate_correlation(sim1,20,0.8)
corls(sim1, sim2)^2

# }