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# Re: st: obtaining confidence interval from -lincom-

 From Federico Belotti To "statalist@hsphsun2.harvard.edu" Subject Re: st: obtaining confidence interval from -lincom- Date Sat, 25 May 2013 15:59:23 +0200

Tom,

you can easily put -lincom- confidence interval into scalars using

scalar ci_lb = r(estimate) + invnorm(0.025)*r(se)

scalar ci_ub = r(estimate) + invnorm(0.975)*r(se)

scalar list

if the sample is small you could substitute invnormal() with invttail()

Federico

Il giorno 25/mag/2013, alle ore 15:04, Tom <tommedema@gmail.com> ha scritto:

> Dear list,
>
> My research supervisor told me that I can generate a marginal effects
> plot much like the one on page 14 of Brambor et al. (2006). [1]
>
> Note that using marginsplot in Stata 12 does not create such plot.
> Therefore my supervisor told me to  do the following:
>
> ## code start ##
>
> * regress *
> reg g ipr dtflog dtflogipr i.cid, robust
>
> cap drop l_dtf
> cap drop l_est
> gen l_dtflog = .
> gen l_est = .
>
> * lincom *
> forval i = 1/61 {
>    local dtflog = (`i'-1) / 10
>
>    lincom _b[ipr] + `dtflog' * _b[dtflogipr], level(95)
>
>    * save *
>    replace l_dtflog = `dtflog' in `i'
>    replace l_est = r(estimate) in `i'
>
> }
>
> line l_est l_dtflog
>
> ## code end ##
>
> In short: the regression has two main independent variables ipr and
> dtflog and one interaction term of these two variables (dtflogipr).
> dtflog ranges from 0 to 6 and is looped through to see the interaction
> effect for certain values of dtflog.
>
> I want to make the same graph as Brambor et al. (2006), where both
> confidence interval borders being above or below the horizontal axis
> indicates a singificant interaction effect.
>
> The only problem so far seems to be that while lincom returns a
> confidence interval, when I do -return list- I only see the estimate
> in r(estimate), but I cannot retrieve the confidence interval from the
> list.
>
> So, how would I retrieve the confidence interval listed in the table
> after running -lincom- ?
>
> Tom
>
> [1]: Brambor, Thomas, William Roberts Clark, and Matt Golder.
> "Understanding interaction models: Improving empirical analyses."
> Political analysis 14.1 (2006): 63-82.
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