How can I adjust a coefplot for the constant value of categorical variable estimation?
I have a dataset in Stata that looks something like this
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
dv2 | 1,904 .5395645 .427109 -1.034977 1.071396
xvar | 1,904 3.074055 1.387308 1 5
with xvar being a categorical independent variable and dv2 a dependent variable of interest.
I am estimating a simple model with the categorical variable as a dummy:
reg dv2 ib4.xvar
eststo myest
Source | SS df MS Number of obs = 1,904
-------------+---------------------------------- F(4, 1899) = 13.51
Model | 9.60846364 4 2.40211591 Prob > F = 0.0000
Residual | 337.540713 1,899 .177746558 R-squared = 0.0277
-------------+---------------------------------- Adj R-squared = 0.0256
Total | 347.149177 1,903 .182422058 Root MSE = .4216
------------------------------------------------------------------------------
dv2 | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
xvar |
A | .015635 .0307356 0.51 0.611 -.044644 .075914
B | .1435987 .029325 4.90 0.000 .0860861 .2011113
C | .1711176 .0299331 5.72 0.000 .1124124 .2298228
E | .1337754 .0295877 4.52 0.000 .0757477 .1918032
|
_cons | .447794 .020191 22.18 0.000 .4081952 .4873928
------------------------------------------------------------------------------
These are the results. As you can see B, C and E have larger effect than D which is the excluded category.
However, coefplot does not account for the in categorical variable the coefficient is composite true_A=D+A.
coefplot myest, scheme(s1color) vert
As you can see the plot shows the constant to be the largest coefficient, while the other to be smaller.
Is there a systematic way I can adjust for this problem and plot the true coefficients and SEs of each category?
Thanks a lot for your help
In response to your second comment, here is an example of how you can use marginsplot to plot estimated effects from a linear regression.
sysuse auto, clear
replace price = price/100
reg price i.rep78, cformat(%9.2f)
------------------------------------------------------------------------------
price | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
rep78 |
2 | 14.03 23.56 0.60 0.554 -33.04 61.10
3 | 18.65 21.76 0.86 0.395 -24.83 62.13
4 | 15.07 22.21 0.68 0.500 -29.31 59.45
5 | 13.48 22.91 0.59 0.558 -32.28 59.25
|
_cons | 45.65 21.07 2.17 0.034 3.55 87.74
------------------------------------------------------------------------------
margins i.rep78, cformat(%9.2f)
------------------------------------------------------------------------------
| Delta-method
| Margin std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
rep78 |
1 | 45.65 21.07 2.17 0.034 3.55 87.74
2 | 59.68 10.54 5.66 0.000 38.63 80.73
3 | 64.29 5.44 11.82 0.000 53.42 75.16
4 | 60.72 7.02 8.64 0.000 46.68 74.75
5 | 59.13 8.99 6.58 0.000 41.18 77.08
------------------------------------------------------------------------------
marginsplot
Note that these values are the constant plus the appropriate coefficient.
And then using the marginsplot command we can produce the following plot, which includes the marginal estimates and confidence intervals:
