How can I generate 2 sets of variables from different distributions with a correlation between them in r? [duplicate]

I am working in R and would like to generate 40 numbers from $$\mathrm{N}(0,1)$$ and another 40 from $$\mathrm{Uniform}(0,2)$$ with a negative correlation (for example: $$r = -0.45$$) between them. The correlation does not have to be exact.

I've done something similar for 2 normally distributed variables by specifying a co-variance matrix - see R code below:

sigma <- matrix(c(.33,-.23,-.23,1), ncol=2, byrow=T)

mu <- c(1,0)

ab <- mvrnorm(n=40, mu, sigma)


One thing I considered was using the above code and then transforming one of the normal variables to a uniform but I am not sure how to go about doing that.

marked as duplicate by Xi'an, kjetil b halvorsen, Michael Chernick, user158565, Peter Flom♦Jul 4 at 12:05

• Look into copulas – kjetil b halvorsen Jul 1 at 15:39
• @Xi'an Although that link is relevant, none of its answers appear to address the situation where the marginal distributions are specified. – whuber Jul 1 at 21:33

First, sample 40 values from a bivariate normal distribution $$(X_1, X_2)^T\sim \mathcal{N}(\mathbf{\mu}, \Sigma)$$ with a mean vector of $$\mathbf{\mu} = (0, 0)^T$$ and covariance matrix $$\Sigma = \begin{pmatrix} 1 & \rho\sqrt{\frac{\pi}{3}} \\ \rho\sqrt{\frac{\pi}{3}} & 1 \end{pmatrix}$$ where $$\rho$$ is the desired correlation coefficient between the standard normal and the uniform $$\mathrm{U}(0, 2)$$ distribution.

Secondly, apply the transformation $$Y_1 =\Phi(X_1)$$ in order to transform $$X_1$$ to a uniform $$Y_1\sim \mathrm{U}(0,1)$$ distribution. $$\Phi$$ denotes the CDF of the standard normal distribution.

Multiply $$Y_1$$ with 2 (i.e. $$Z_1=2Y_1$$) to get a uniform $$Z_1\sim \mathrm{U}(0,2)$$ distributed random variable. The variance of $$Z_1$$ is $$\frac{1}{12}(b-a)^2=\frac{1}{12}(2-0)^2=\frac{1}{3}$$.

The covariance between $$Y_1$$ and $$X_2$$ is $$\frac{\rho}{2\sqrt{3}}$$. Because $$\mathrm{Cov}(aX, Y) = a\mathrm{Cov}(X,Y)$$, the covariance between $$Z_1$$ and $$X_2$$ is $$2\frac{\rho}{2\sqrt{3}}=\frac{\rho}{\sqrt{3}}$$. Hence, the correlation between $$Z_1$$ and $$X_2$$ is

$$\mathrm{Corr}(Z_1,X_2)=\frac{\frac{\rho}{\sqrt{3}}}{\sqrt{\frac{1}{3}}\cdot 1} = \rho$$

In R:

library(MASS)

set.seed(142857)

n <- 40

rho <- -0.45 # setting the correlation

sigma <- matrix(c(1, rho*sqrt(pi/3), rho*sqrt(pi/3), 1), 2, 2, byrow = TRUE)

x <- mvrnorm(n, mu = c(0, 0), Sigma = sigma)

z1 <- 2*pnorm(x[, 1])
x2 <- x[, 2]

cor(z1, x2)
[1] -0.4343862


I've simulated $$10^5$$ repetitions of the above process. The distribution of correlation coefficients between $$Z_1$$ and $$X_2$$ is shown here (the vertical orange line denotes the correlation coefficient of $$\rho = -0.45$$).

• Is it really that case that rho is the correlation of the resulting variables or would it merely be the correlation parameter for the bivariate Normal distribution that underlies the copula? I suspect it would be the latter, but this question asks for the former. – whuber Jul 1 at 21:35
• @whuber Good question. I think you're right. My simulations show that the mean of the resulting correlations is not $-0.45$ which is troublesome. A the moment, I'm unable to fully grasp and explain why. I will leave the answer up for the moment but will delete it when a better one is posted. – COOLSerdash Jul 2 at 9:43
• The reason for the discrepancy is that the transformation of the marginal uniform distribution to a marginal Normal distribution is nonlinear and therefore is likely to change the correlation. However, it's not "strongly" nonlinear and therefore changes the correlation only a little, in a way that varies with $\rho.$ One could study this variation (in this particular case) and thereby produce the function that translates the value of $\rho$ into the correlation. The problem would then be solved by inverting that function. – whuber Jul 2 at 13:37
• @whuber I believe that the correlation after the transformation is $\sqrt{3/\pi}\rho$. Therefore, multiplying $\rho$ with $\sqrt{\pi/3}$ should give the desired calculations after transformation. – COOLSerdash Jul 2 at 16:26