Using the user-written khb command by Ulrich Kohler and Kristian Karlson, I have decomposed the effect of a binary variable (x) on a categorical (unordered) outcome (y) using a categorical (ordered) mediating variable (m).
I would like to plot the indirect, direct and total effects (I have attached an example where ACME is the indirect effect, ADE is the direct followed by the total effect). I am trying to store the estimates from the khb output and then use coefplot to achieve what I need.
This is the decomposition for category 3 of my outcome with 0 as the base outcome. I attempt to store the coefficients and confidence intervals so that I can plot them:
Code:
khb mlogit y i.x || i.m, outcome(3) baseoutcome(0) c( goveducCH2 govempCH2 ) disentangle Decomposition using the KHB-Method Model-Type: mlogit Number of obs = 5567 Variables of Interest: i.x Pseudo R2 = 0.04 Z-variable(s): i.m Concomitant: goveducCH2 govempCH2 Results for outcome 3 and base outcome Consistently_unemployed ------------------------------------------------------------------------------ y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0.x | (base outcome) -------------+---------------------------------------------------------------- 1.x | Reduced | .893427 .1067596 8.37 0.000 .684182 1.102672 Full | .7119227 .1079495 6.59 0.000 .5003455 .9234999 Diff | .1815043 .0248776 7.30 0.000 .1327452 .2302635 ------------------------------------------------------------------------------ Components of Difference Z-Variable | Coef Std_Err P_Diff P_Reduced -------------+--------------------------------------------- 0b.x | _i_m2 | 0 0 . . _i_m3 | 0 0 . . _i_m4 | 0 0 . . 1.x | _i_m2 | .0499166 .0162951 27.50 5.59 _i_m3 | .0909348 .0190041 50.10 10.18 _i_m4 | .040653 .0195628 22.40 4.55 ----------------------------------------------------------- . . ereturn list scalars: e(N) = 5567 macros: e(title) : "Decomposition" e(mediator_vars) : "_i_m2 _i_m3 _i_m4" e(key_vars) : "i.x" e(depvar) : "y" e(method) : "KHB" e(model) : "mlogit" e(cmd) : "khb" e(properties) : "b V" matrices: e(b) : 1 x 6 e(V) : 6 x 6 e(Diff_V) : 2 x 2 e(SUR_b) : 1 x 15 e(SUR_V) : 15 x 15 e(Full_b) : 1 x 8 e(Full_V) : 8 x 8 e(Reduced_b) : 1 x 8 e(Reduced_V) : 8 x 8 e(disentangle) : 6 x 4 functions: e(sample) . . estimates store khb . . coefplot _est_khb estimation result _est_khb not found
(In the khb output, reduced = total effect, full = direct and diff = indirect effect)
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