Repeating the same procedure twice with different
seeds the results of the regression command were
identical at least for betas, SEs 95%CIs and t-values
(see output below) thus I suppose that one can use
corr2data to obtain such estimates.
Thanks to Richard Williams for the suggestions and the
useful link.
Nikos Pantazis
. corr2data x,n(10) mean(5) sds(0.5) seed(3)
. gen gr=1
. save cor1
. drop _all
. corr2data x,n(10) mean(10) sds(0.7) seed(3)
. gen gr=2
. save cor2
. drop _all
. corr2data x,n(10) mean(20) sds(1.2) seed(3)
. gen gr=3
. append using cor2
. append using cor1
. tab gr
. xi:reg x i.gr
i.gr _Igr_1-3 (naturally
coded; _Igr_1 omitted)
Source | SS df MS
Number of obs = 30
-------------+------------------------------
F( 2, 27) = 802.75
Model | 1166.66667 2 583.333335
Prob > F = 0.0000
Residual | 19.6200016 27 .726666724
R-squared = 0.9835
-------------+------------------------------
Adj R-squared = 0.9822
Total | 1186.28667 29 40.9064369
Root MSE = .85245
------------------------------------------------------------------------------
x | Coef. Std. Err. t P>|t|
[95% Conf. Interval]
-------------+----------------------------------------------------------------
_Igr_2 | 5 .3812261 13.12 0.000
4.217789 5.782211
_Igr_3 | 15 .3812261 39.35 0.000
14.21779 15.78221
_cons | 5 .2695676 18.55 0.000
4.446893 5.553107
------------------------------------------------------------------------------
. drop _all
. corr2data x,n(10) mean(5) sds(0.5) seed(15)
. gen gr=1
. save cor1,replace
. drop _all
. corr2data x,n(10) mean(10) sds(0.7) seed(15)
. gen gr=2
. save cor2,replace
. drop _all
. corr2data x,n(10) mean(20) sds(1.2) seed(15)
. gen gr=3
. append using cor2
. append using cor1
. xi:reg x i.gr
i.gr _Igr_1-3 (naturally
coded; _Igr_1 omitted)
Source | SS df MS
Number of obs = 30
-------------+------------------------------
F( 2, 27) = 802.75
Model | 1166.66667 2 583.333337
Prob > F = 0.0000
Residual | 19.6200026 27 .726666764
R-squared = 0.9835
-------------+------------------------------
Adj R-squared = 0.9822
Total | 1186.28668 29 40.9064371
Root MSE = .85245
------------------------------------------------------------------------------
x | Coef. Std. Err. t P>|t|
[95% Conf. Interval]
-------------+----------------------------------------------------------------
_Igr_2 | 5 .3812261 13.12 0.000
4.217789 5.782212
_Igr_3 | 15 .3812261 39.35 0.000
14.21779 15.78221
_cons | 5 .2695676 18.55 0.000
4.446893 5.553107
------------------------------------------------------------------------------
.
--- Richard Williams <[email protected]>
wrote:
> At 01:48 AM 3/9/2004 -0800, n p wrote:
> >statistics etc.). The question is if it is right to
> >use the corr2data command to create three (or more)
> >independent samples for A, B and C arms with given
> >mean, SE and N to obtain via regression two betas
> and
> >two SEs (B vs. A and C vs. A). I know that I could
> use
>
> Also, I discuss the use of -corr2data- for purposes
> like this on pp. 7-9 of
> the following:
>
>
http://www.nd.edu/~rwilliam/xsoc593/lectures/OLS-Stata.pdf
>
> Next time I get around to revising that handout,
> I'll add the advice that,
> if in doubt, generate alternative data sets with
> different seeds and see if
> it makes a difference. If it does make a
> difference, then what you are
> trying to do is not ok.
>
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> FAX: (574)288-4373
> HOME: (574)289-5227
> EMAIL: [email protected]
> WWW (personal): http://www.nd.edu/~rwilliam
> WWW (department): http://www.nd.edu/~soc
>
> *
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