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Generating Correlated Random Variables Kriss Harris Senior Statistician

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Why? I was producing graphs for a SAS Graphics Training Course that will be rolled out soon, and I wanted to control the correlation between the variables. 2

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Previous Method 3 Use Excel to fill down and then generate another column that was fairly correlated

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Generating Correlated Random Variables using the SAS Datastep data bivariate_final; mean1=0; *mean for y1; mean2=10; *mean for y2; sig1=2; *SD for y1; sig2=5; *SD for y2; rho=0.90; *Correlation between y1 and y2; do i = 1 to 100; r1 = rannor(1245); r2 = rannor(2923); y1 = mean1 + sig1*r1; y2 = mean2 + rho*sig2*r1+sqrt(sig2**2- sig2**2*rho**2)*r2; output; end; run; 4

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Generating Correlated Random Variables using the SAS Datastep data bivariate_final; mean1=0; *mean for y1; mean2=10; *mean for y2; sig1=2; *SD for y1; sig2=5; *SD for y2; rho=0.90; *Correlation between y1 and y2; do i = 1 to 100; r1 = rannor(1245); r2 = rannor(2923); y1 = mean1 + sig1*r1; y2 = mean2 + rho*sig2*r1+sqrt(sig2**2- sig2**2*rho**2)*r2; output; end; run; 5

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Generating Correlated Random Variables using the SAS Datastep data bivariate_final; mean1=0; *mean for y1; mean2=10; *mean for y2; sig1=2; *SD for y1; sig2=5; *SD for y2; rho=0.90; *Correlation between y1 and y2; do i = 1 to 100; r1 = rannor(1245); r2 = rannor(2923); y1 = mean1 + sig1*r1; y2 = mean2 + rho*sig2*r1+sqrt(sig2**2- sig2**2*rho**2)*r2; output; end; run; 6

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Generating Correlated Random Variables using the SAS Datastep data bivariate_final; mean1=0; *mean for y1; mean2=10; *mean for y2; sig1=2; *SD for y1; sig2=5; *SD for y2; rho=0.90; *Correlation between y1 and y2; do i = 1 to 100; r1 = rannor(1245); r2 = rannor(2923); y1 = mean1 + sig1*r1; y2 = mean2 + rho*sig2*r1+sqrt(sig2**2- sig2**2*rho**2)*r2; output; end; run; 7

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Generating Correlated Random Variables using the SAS Datastep data bivariate_final; mean1=0; *mean for y1; mean2=10; *mean for y2; sig1=2; *SD for y1; sig2=5; *SD for y2; rho=0.90; *Correlation between y1 and y2; do i = 1 to 100; r1 = rannor(1245); r2 = rannor(2923); y1 = mean1 + sig1*r1; y2 = mean2 + rho*sig2*r1+sqrt(sig2**2- sig2**2*rho**2)*r2; output; end; run; 8

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Generating Correlated Random Variables using the SAS Datastep data bivariate_final; mean1=0; *mean for y1; mean2=10; *mean for y2; sig1=2; *SD for y1; sig2=5; *SD for y2; rho=0.90; *Correlation between y1 and y2; do i = 1 to 100; r1 = rannor(1245); r2 = rannor(2923); y1 = mean1 + sig1*r1; y2 = mean2 + rho*sig2*r1+sqrt(sig2**2- sig2**2*rho**2)*r2; output; end; run; 9

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Y and x for different correlation coefficients 10

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Generating Correlated Random Variables using Proc IML To generate more than 2 correlated random variables than it’s easier to use the Cholesky decomposition method in Proc IML. IML = Interactive Matrix Language 11

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Generating Correlated Random Variables using Proc IML proc iml; use bivariate_final; read all var {r1} into x3; read all var {r2} into x4; read all var {mean1} into mean1; read all var {mean2} into mean2; x={ 4 9, 9 25}; /* C */ mattrib x rowname=(rows [1:2 ]) colname=(cols [1:2]); Cholesky_decomp = root(x); /* U */ matrix_con = x3||x4; mean = mean1||mean2; final_simulated = mean + matrix_con * Cholesky_decomp; /*RC*/ varnames = {y3 y4}; create Cholesky_correlation from final_simulated (|colname = varnames|); append from final_simulated; quit; 12 Use is similar to set. Reading in the simulated data and the means

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Generating Correlated Random Variables using Proc IML proc iml; use bivariate_final; read all var {r1} into x3; read all var {r2} into x4; read all var {mean1} into mean1; read all var {mean2} into mean2; x={ 4 9, 9 25}; /* C */ mattrib x rowname=(rows [1:2 ]) colname=(cols [1:2]); Cholesky_decomp = root(x); /* U */ matrix_con = x3||x4; mean = mean1||mean2; final_simulated = mean + matrix_con * Cholesky_decomp; /*RC*/ varnames = {y3 y4}; create Cholesky_correlation from final_simulated (|colname = varnames|); append from final_simulated; quit; 13 Variance covariance matrix

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Generating Correlated Random Variables using Proc IML proc iml; use bivariate_final; read all var {r1} into x3; read all var {r2} into x4; read all var {mean1} into mean1; read all var {mean2} into mean2; x={ 4 9, 9 25}; /* C */ mattrib x rowname=(rows [1:2 ]) colname=(cols [1:2]); Cholesky_decomp = root(x); /* U */ matrix_con = x3||x4; mean = mean1||mean2; final_simulated = mean + matrix_con * Cholesky_decomp; /*RC*/ varnames = {y3 y4}; create Cholesky_correlation from final_simulated (|colname = varnames|); append from final_simulated; quit; 14 Applying Cholesky’s decompositon

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Generating Correlated Random Variables using Proc IML proc iml; use bivariate_final; read all var {r1} into x3; read all var {r2} into x4; read all var {mean1} into mean1; read all var {mean2} into mean2; x={ 4 9, 9 25}; /* C */ mattrib x rowname=(rows [1:2 ]) colname=(cols [1:2]); Cholesky_decomp = root(x); /* U */ matrix_con = x3||x4; mean = mean1||mean2; final_simulated = mean + matrix_con * Cholesky_decomp; /*RC*/ varnames = {y3 y4}; create Cholesky_correlation from final_simulated (|colname = varnames|); append from final_simulated; quit; 15 Concatenating the variables

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Generating Correlated Random Variables using Proc IML proc iml; use bivariate_final; read all var {r1} into x3; read all var {r2} into x4; read all var {mean1} into mean1; read all var {mean2} into mean2; x={ 4 9, 9 25}; /* C */ mattrib x rowname=(rows [1:2 ]) colname=(cols [1:2]); Cholesky_decomp = root(x); /* U */ matrix_con = x3||x4; mean = mean1||mean2; final_simulated = mean + matrix_con * Cholesky_decomp; /*RC*/ varnames = {y3 y4}; create Cholesky_correlation from final_simulated (|colname = varnames|); append from final_simulated; quit; 16 Correlated Variables

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Generating Correlated Random Variables using Proc IML proc iml; use bivariate_final; read all var {r1} into x3; read all var {r2} into x4; read all var {mean1} into mean1; read all var {mean2} into mean2; x={ 4 9, 9 25}; /* C */ mattrib x rowname=(rows [1:2 ]) colname=(cols [1:2]); Cholesky_decomp = root(x); /* U */ matrix_con = x3||x4; mean = mean1||mean2; final_simulated = mean + matrix_con * Cholesky_decomp; /*RC*/ varnames = {y3 y4}; create Cholesky_correlation from final_simulated (|colname = varnames|); append from final_simulated; quit; 17 Outputting the variables

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References Generating Multivariate Normal Data by using Proc IML Generating Multivariate Normal Data by using Proc IML Lingling Han, University of Georgia, Athens, GA 18

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Appendix Correlation Coefficient = 19

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R Code - Generating Correlated Random Variables mean1 = 0 mean2 = 10 sig1 = 2 sig2 = 5 rho = 0.9 r1 = rnorm(100, 0, 1) r2 = rnorm(100, 0, 1) y1 = mean1 + sig1*r1; y2 = mean2 + rho*sig2*r1+sqrt(sig2**2-sig2**2*rho**2)*r2; 20

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R Code - Generating Correlated Random Variables mean1 = 0 mean2 = 10 sig1 = 2 sig2 = 5 rho = 0.9 r1 = rnorm(100, 0, 1) r2 = rnorm(100, 0, 1) y1 = mean1 + sig1*r1 y2 = mean2 + rho*sig2*r1+sqrt(sig2**2-sig2**2*rho**2)*r2 21

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R Code - Generating Correlated Random Variables using Matrices C = matrix(c(4, 9, 9, 25), nrow = 2, ncol = 2) cholc = chol(C) R = matrix(c(r1,r2), nrow = 100, ncol = 2, byrow = F) mean = matrix(c(mean1,mean2), nrow = 100, ncol = 2, byrow = T) RC = mean + R %*% cholc 22 Use previous values of r1 and r2

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