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* Run the "correl" module and display the results; call correl(C, CovG, VG1, VG2, CovP VP1, VP2, RG, RP, SERG, SEEP); print "Genotypic Correlation Between &TraitI and &TraitJ"; print RG serg; print "Phenotypic Correlation Between &TraitI and &TraitJ "; print RP serp; quit; run;
and their standard errors using the delta method; proc im1; use estmat; read all into e; use covmat; read all into cov; * Obtain the "C" matrix by removing the extra first column of the "cov" matrix; C = cov(|1:nrow(cov), 2:nco1(cov)|); * Obtain male covariance (CovM = 1/4 CovA) and variance components (VM1 and VM2 = 1/4 additive variances) from the elements of the "e" vector; CovM = e(|2,1|); VM1 = e(|1,1|); VM2 = e(|3,1|); * Obtain phenotypic covariance (CovP) and variance components (VP1 and VP2) from the elements of the
"e" vector; CovP = CovM + e(|5,1|) + e(|8,1|) + e(|1,1|) + e(|14,1|) + e (|17,1|); VP1 = VM1 + e(|4,1|) + e(|7,1|) + e(|10,1|) + e(|13,1|) + e (|16,1|); VP2 = VM2 + e(|6,1|) + e(|9,1|) + e(|12,1|) + e(|15,1|) + e(|18,1|); * Create a module called "correl" that will estimate genotypic and phenotypic correlations and their standard errors; start correl(C, CovM, VM1, VM2, Cove VP1, VP2, RG, RP, SERG, SERP); RG = CovM/sqrt(VM1xVM2); * Make the derivative vector for rg, note that the order of the rows and columns of the variance covariance matrix is VM1, CovM, VM2, VF(M)1, CovF(M), VF(M)2, VME1, CovME, VME2, VF(M)E1, CovF(M)E, VF(M)2E, VRME1, CovRME, VRME2, VRFME1,
and their standard errors using the delta method; proc iml; use estmat; read all into e; use covmat; read all into cov; * Obtain the "C" matrix by removing the extra first column of the "cov" matrix; C = cov(|1:nrow(cov), 2:ncol(cov)|); * Obtain male covariance (CovM = 1/4 CovA) and variance components (VM1 and VM2 = 1/4 additive variances) from the elements of the "e" vector; CovM = e(|2,1|); VM1 = e(|1,1|); VM2 = e(|3,1|); * Obtain phenotypic covariance (CovP) and variance components (VP1 and VP2) from the elements of the
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