Create a data set with correlated variables [duplicate]

Question

I am trying to create a dataset where columns 2,3,4 are correlate (0.98,0.97,0.96, respectively) to column 1. Right now I have this code:

 library(MASS)

X<-mvrnorm(20,mu=c(5,6),Sigma=matrix(c(1,0.98,0.98,1),ncol=2),empirical=TRUE)
cor(X)

Y<-mvrnorm(20,mu=c(5,6),Sigma=matrix(c(1,0.97,0.97,1),ncol=2),empirical=TRUE)
cor(Y)
Y <- Y[,2]

Z<-mvrnorm(20,mu=c(5,6),Sigma=matrix(c(1,0.97,0.97,1),ncol=2),empirical=TRUE)
cor(Z)
Z <- Z[,2]

data <- cbind (X,Y,Z)
cor(data)

and it produces this matrix:

                              Y          Z
    1.00000000  0.9800000000 0.0826655886 -0.4293286
    0.98000000  1.0000000000 0.0009559618 -0.5221029
 Y  0.08266559  0.0009559618 1.0000000000  0.1847887
 Z -0.42932859 -0.5221029358 0.1847886713  1.0000000

I would like the final outcome to look like this. It doesn't matter how the column 2,3,4 are correlated with each other (i.e. the X), as long as they have the right correlation with the first column.

  1    0.98   0.97   0.96
 0.98    1      X     X
 0.97    X      1     X 
 0.96    X      X     1

Thanks for your help!

Answer 1

Method 1

Basically an abridged version of the one exposed by @whuber in this answer.

Sample from $X_1 \sim \mathcal N(0, 1)$, then have

$$ \require{cancel} \begin{cases} X_2=f_2X_1+\sqrt{1-f_2^2}\epsilon_2\\ X_3=f_3X_1+\sqrt{1-f_3^2}\epsilon_3\\ X_4=f_4X_1+\sqrt{1-f_4^2}\epsilon_4\\ \end{cases} $$

Where the fractions are given as $f=(f_2, f_3, f_4) = (0.98,0.97,0.96)$ and the $\epsilon_i$ are white noise samples from $\mathcal N(0,1)$.

It's easy to see that, for $i \in (2,3,4)$:

$$\operatorname{Var}(X_i)=f_i^2\cancelto{1}{\operatorname{Var}(X_1)}+(1-f_i^2)\cancelto{1}{\operatorname{Var}(\epsilon_i)}+f_i\sqrt{(1-f_i^2)}\cancelto{0}{\operatorname{Cov}(X_1,X_i)}=\\ f_i^2+(1-f_i^2)=1$$

$$ \operatorname{Cor}(X_1, X_i) = \operatorname{Cov}(X_1,X_i) = E[X_1X_i] - \cancelto{0}{ E[X_1]}\cancelto{0}{E[X_i]}= f_i\cancelto{1}{E[X_1^2]}+\sqrt{1-f_i^2}\cancelto{0}{E[X_1\epsilon_i]}\\ =f_i$$

Method 2 (requires specifying the whole correlation matrix):

as @whuber pointed in the comments, in your question, you only presented the 3 correlations pertaining to the first variable. You would need to specify the remaining 3 correlations in such a manner that $R$ is positive definite. A solution without this caveat is also possible, where you first sample the first variable in isolation.

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Given your correlation matrix $R$, you can simulate data from a multivariate normal distribution by:

$$X=Z \cdot R^{1/2},$$

where $Z \sim \text{MVN}(\mathbb 0, \mathbb I)$.

It's easy to see that the covariance of the $X$ is given by

$$\operatorname{Cov}(X)=E[X^\top X] - E[X]^\top \cdot E[X]\\ ={R^{1/2}}^\top E[Z^\top Z]R^{1/2}-R^{1/2}E[Z]^\top \cdot E[Z]R^{1/2}$$

But $E[Z] = \mathbb 0$, while $E[Z^\top Z] = \mathbb I$, so

$$\operatorname{Cov}(X) = {R^{1/2}}^\top\cdot R^{1/2} = R.$$

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$R^{1/2}$ is any decomposition of $R$ such that $R={R^{1/2}}^\top R^{1/2}$