simCorrVars: [+] Simulate correlated continuous data from standard normal...
In GegznaV/spMisc: Infix Operators, Convenience and Other Kind of Miscellaneous Functions

Description Usage Arguments Details Value Author(s) Source See Also Examples

Simulate correlated continuaous data from standard normal distribution by using Choleski factorization of correlation matrix.

1	simCorrVars(corrMat, N = 100)

`corrMat`	A symmetricmatrix of correlation coefficients. Determinant of `corrMat` must be positive. If `corrMat` is a vector, it is passed to function `corr_vec2mat` to construct a correlation matrix. Default `corrMat = corr_vec2mat(c(.8,.2,.7))`.
`N`	A number of observations. Default is 100.

Number of variables is determined by the size of correlation matrix corrMat.

A dataframe with correlated variables.

Vilmantas Gegzna

http://www.r-bloggers.com/simulating-random-multivariate-correlated-data-continuous-variables/

corr_vec2mat, mvrnorm, chol

Other simmulation functions in spMisc: corr_vec2mat()

# ------------------------------------------------------------
simCorrVars()
simCorrVars(N = 10)

# ------------------------------------------------------------
# Generate 2 correlated variables with 10 observations:
# A. Imput is a matrix

corrMat <- matrix(c(1, .5, .5, 1), 2)
print(corrMat)
simCorrVars(corrMat, N = 10)

# B. Imput is a vector
simCorrVars(.5, N = 10)
simCorrVars(c(.1,.5,.8), N = 10)

# ------------------------------------------------------------