Re: st: defining a covariance matrix

[John Antonakis <John.Antonakis@unil.ch>]

Hi Ariel:

Try:

matrix C = (2,1,-1\1,1,-.5\-1,-.5,1)
corr2data x1 x2 x3, cov(C) n(1000)

You can also do this with -ssd-, but only if you estimate the model with 
-sem-:

ssd init x1 x2 x3
ssd set obs 1000
ssd set cov 2 \ 1 1 \ -1 -.5 1

Best,
J.

__________________________________________

John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management

Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis

Associate Editor
The Leadership Quarterly
__________________________________________

On 01.05.2013 20:21, Ariel Linden, DrPH wrote:
> Hi All,
>
> I am trying to replicate some analyses from a paper, and I came across the following sentence:
>
> "X1, X2, and X3 are multivariate normal with means zero, variances of (2, 1, 1) and covariances of (1,−1,−0.5) respectively."
>
> Can someone tell me how to define the covariance matrix  as described above? This rest is easy enough:
>
> *** code****
> matrix m = (0,0,0)
> matrix sd = (sqrt(2),1,1)
> matrix C = ?
> drawnorm X1 X2 X3, n(1000) means(m) sds(sd) cov(C)
>
> Thanks in advance
>
> Ariel
>
>
>
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