Dear All,
I am running a fixed effect neg binomial regression and want to plot some of the regression coefficients with 95% CI using coefplot.
Since the CI on int3 is so large, we cannot see the CI of remaining coefficients. Is there a way I could plot int3 on yaxis(1) and the others on yaxis(2)? Will be grateful for any guidance to modify the coefplot code to utilize both y-axes.
Many thanks.
Sumedha.
I am running a fixed effect neg binomial regression and want to plot some of the regression coefficients with 95% CI using coefplot.
Code:
. eststo raw11: xtnbreg sumnalaxone int3-int4 int6-int9 i.year i.treat age if year>2012, fe exposure(exposure) /* WORKS*/ note: 2281 groups (2281 obs) dropped because of only one obs per group note: 2529 groups (6872 obs) dropped because of all zero outcomes Iteration 0: log likelihood = -394.85682 Iteration 1: log likelihood = -378.89183 Iteration 2: log likelihood = -373.71977 Iteration 3: log likelihood = -373.29432 Iteration 4: log likelihood = -373.28541 Iteration 5: log likelihood = -373.28387 Iteration 6: log likelihood = -373.28352 Iteration 7: log likelihood = -373.28343 Iteration 8: log likelihood = -373.28342 Conditional FE negative binomial regression Number of obs = 685 Group variable: studypersonid Number of groups = 220 Obs per group: min = 2 avg = 3.1 max = 7 Wald chi2(14) = 86.89 Log likelihood = -373.28342 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ sumnalaxone | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- int3 | 1.150222 1434.497 0.00 0.999 -2810.411 2812.712 int4 | -.650432 1.213165 -0.54 0.592 -3.028191 1.727327 int6 | 2.006028 .5589152 3.59 0.000 .9105741 3.101481 int7 | 2.109025 .5748433 3.67 0.000 .9823525 3.235697 int8 | 2.130793 .5859116 3.64 0.000 .9824274 3.279159 int9 | 2.272671 .7126006 3.19 0.001 .8759997 3.669343 | year | 2014 | 14.12338 638.2786 0.02 0.982 -1236.88 1265.126 2015 | 14.04019 638.2786 0.02 0.982 -1236.963 1265.043 2016 | 14.69761 638.2786 0.02 0.982 -1236.305 1265.701 2017 | 14.98768 638.2786 0.02 0.981 -1236.015 1265.991 2018 | 15.10221 638.2786 0.02 0.981 -1235.901 1266.105 2019 | 14.59542 638.2787 0.02 0.982 -1236.408 1265.599 | 1.treat | -.3597424 .6071373 -0.59 0.554 -1.54971 .8302249 age | -.041732 .0211664 -1.97 0.049 -.0832173 -.0002467 _cons | -22.66602 638.2789 -0.04 0.972 -1273.67 1228.338 ln(exposure) | 1 (exposure) ------------------------------------------------------------------------------ . coefplot, keep(int3 int4 int6 int7 int8 int9) mcol(cranberry) msym(o) ciopts(lcolor(gs8)) /// > coeflabels( int3="2013" int4="2014" int6="2016" int7="2017" int8="2018" int9="2019" , angle(45)) mlabsize(huge) /// > title("", color(black) size(large)) ysc(r(-4 4) noext) /// > yline(0, lcol(black)) xline(2.5, lwidth(2.2) lcolor(gs10)) /// > ytitle(Estimated coefficient, size(medium) margin(small)) vertical omitted
Many thanks.
Sumedha.
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