BookmarkSubscribeRSS Feed
thanoon
Calcite | Level 5

HI ALL

i need your help to correct this commands ( simulation of multivariate regression model).

regards\

&let N=1000;

proc iml;

/* specify the mean and covariance of the population */

Mean = {1, 2, 3 , 4, 5, 6, 7, 8, 9};

cov = {0.8838659 0.9738211 0.5075826 0.8869405 0.6893635 0.9432546 0.9287864 0.487664 0.7798851,

       0.9738211 0.8771385 0.5668343 0.6656078 0.8234433 0.9280248 0.7900982 0.1142881 0.8905852,

       0.5075826 0.5668343 0.5448972 0.2810451 0.9316785 0.6140648 0.8564316 0.6149666 0.4655727,

       0.8869405 0.6656078 0.2810451 0.5900954 0.7749225 0.4426628 0.0762391 0.9093871 0.5513743,

       0.6893635 0.8234433 0.9316785 0.7749225 0.6663087 0.236998 0.1419535 0.3321644 0.3306588,

       0.9432546 0.9280248 0.6140648 0.4426628 0.236998 0.7543832 0.6882105 0.6199138 0.7862424,

       0.9287864 0.7900982 0.8564316 0.0762391 0.1419535 0.6882105 0.9863298 0.1584623 0.3849384,

        0.487664 0.1142881 0.6149666 0.9093871 0.3321644 0.6199138 0.1584623 0.3205565 0.0744708,

       0.7798851 0.8905852 0.4655727 0.5513743 0.3306588 0.7862424 0.3849384 0.0744708 0.4806334}

call randseed(4321); 

X = RandNormal(&N, Mean, Cov);               /* 1000 x 9 matrix     */

/* check the sample mean and sample covariance */

SampleMean = mean(X);                        /* mean of each column */

SampleCov =  cov(X);                         /* sample covariance   */

/* print results */

c = "x1":"x9";

print (X[1000:9,])[label="First 5 Obs: MV Normal"];

print SampleMean[colname=c];

print SampleCov[colname=c rowname=c];

/* write SAS/IML matrix to SAS data set for plotting */

create MVN from X[colname=c];  append from X;  close MVN;

quit;

run;

1 REPLY 1
thanoon
Calcite | Level 5

i am sorry i forget the linear equation :

%let N=1000;

proc iml;

/* specify the mean and covariance of the population */

Mean = {1, 2, 3, 2, 1, 4, 5, 6, 1};

Cov = {0.8838659 0.9738211 0.5075826 0.8869405 0.6893635 0.9432546 0.9287864 0.487664 0.7798851,

       0.9738211 0.8771385 0.5668343 0.6656078 0.8234433 0.9280248 0.7900982 0.1142881 0.8905852,

       0.5075826 0.5668343 0.5448972 0.2810451 0.9316785 0.6140648 0.8564316 0.6149666 0.4655727,

       0.8869405 0.6656078 0.2810451 0.5900954 0.7749225 0.4426628 0.0762391 0.9093871 0.5513743,

       0.6893635 0.8234433 0.9316785 0.7749225 0.6663087 0.236998 0.1419535 0.3321644 0.3306588,

       0.9432546 0.9280248 0.6140648 0.4426628 0.236998 0.7543832 0.6882105 0.6199138 0.7862424,

       0.9287864 0.7900982 0.8564316 0.0762391 0.1419535 0.6882105 0.9863298 0.1584623 0.3849384,

        0.487664 0.1142881 0.6149666 0.9093871 0.3321644 0.6199138 0.1584623 0.3205565 0.0744708,

       0.7798851 0.8905852 0.4655727 0.5513743 0.3306588 0.7862424 0.3849384 0.0744708 0.4806334};

call randseed(4321); 

X = RandNormal(&N, Mean, Cov);               /* 1000 x 9 matrix     */

/* check the sample mean and sample covariance */

SampleMean = mean(X);                        /* mean of each column */

SampleCov =  cov(X);                         /* sample covariance   */

/* generate Y according to regression model */

beta = {2, 1, -1, 2, 1, 5, 6, 2, -2};               /* params, not including intercept */

Y = 1 + X*beta + eps;          

/* write SAS data set */

varNames = ('x1':'x9') || {"Y"};

output = X || Y;

run;

sas-innovate-2024.png

 

Time is running out to save with the early bird rate. Register by Friday, March 1 for just $695 - $100 off the standard rate.

 

Check out the agenda and get ready for a jam-packed event featuring workshops, super demos, breakout sessions, roundtables, inspiring keynotes and incredible networking events. 

 

Register now!

Multiple Linear Regression in SAS

Learn how to run multiple linear regression models with and without interactions, presented by SAS user Alex Chaplin.

Find more tutorials on the SAS Users YouTube channel.

From The DO Loop
Want more? Visit our blog for more articles like these.
Discussion stats
  • 1 reply
  • 761 views
  • 0 likes
  • 1 in conversation