我正在对Stata中的类别变量进行回归:
regress y i.age i.birth
部分回归结果输出如下:
coef
age
28 .1
29 -.2
birth
1958 .2
1959 .5
我希望上述结果以相反的顺序显示,以便可以使用putexcel
命令将它们导出到Excel:
coef
age
29 -.2
28 .1
birth
1959 .5
1958 .2
我尝试在回归之前对birth
和age
变量进行排序,但这不起作用。
有人可以帮忙吗?
答案 0 :(得分:1)
您不能在回归输出中直接反转变量的因子水平。
但是,如果最终目标是在Microsoft Excel中创建表,则执行以下操作的一种方法是:
sysuse auto.dta, clear
estimates clear
keep if !missing(rep78)
tabulate rep78, generate(rep)
regress price mpg weight rep2-rep5
estimates store r1
regress price mpg weight rep5 rep4 rep3 rep2
estimates store r2
正常结果:
esttab r1 using results.csv, label refcat(rep2 "Repair record", nolabel)
------------------------------------
(1)
Price
------------------------------------
Mileage (mpg) -63.10
(-0.72)
Weight (lbs.) 2.093**
(3.29)
Repair record
rep78== 2.0000 753.7
(0.39)
rep78== 3.0000 1349.4
(0.76)
rep78== 4.0000 2030.5
(1.12)
rep78== 5.0000 3376.9
(1.78)
Constant -599.0
(-0.15)
------------------------------------
Observations 69
------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
相反的结果:
esttab r2 using results.csv, label refcat(rep5 "Repair record", nolabel)
------------------------------------
(1)
Price
------------------------------------
Mileage (mpg) -63.10
(-0.72)
Weight (lbs.) 2.093**
(3.29)
Repair record
rep78== 5.0000 3376.9
(1.78)
rep78== 4.0000 2030.5
(1.12)
rep78== 3.0000 1349.4
(0.76)
rep78== 2.0000 753.7
(0.39)
Constant -599.0
(-0.15)
------------------------------------
Observations 69
------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
请注意,这里我使用的是社区贡献的命令esttab
来导出结果。
如果弄弄它的选项,可以做进一步的调整。
编辑:
此解决方案手动为esttab
创建虚拟变量,但您也可以使用反向编码创建新变量,并使用与@NickCox在其解决方案中演示的相反的基本级别。
答案 1 :(得分:1)
您可以反转编码并应用值标签以坚持您将看到的内容:
sysuse auto, clear
generate rep78_2 = 6 - rep78
label define new 1 "5" 2 "4" 3 "3" 4 "2" 5 "1"
label values rep78_2 new
regress mpg i.rep78_2
Source | SS df MS Number of obs = 69
-------------+---------------------------------- F(4, 64) = 4.91
Model | 549.415777 4 137.353944 Prob > F = 0.0016
Residual | 1790.78712 64 27.9810488 R-squared = 0.2348
-------------+---------------------------------- Adj R-squared = 0.1869
Total | 2340.2029 68 34.4147485 Root MSE = 5.2897
------------------------------------------------------------------------------
mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
rep78_2 |
4 | -5.69697 2.02441 -2.81 0.006 -9.741193 -1.652747
3 | -7.930303 1.86452 -4.25 0.000 -11.65511 -4.205497
2 | -8.238636 2.457918 -3.35 0.001 -13.14889 -3.32838
1 | -6.363636 4.066234 -1.56 0.123 -14.48687 1.759599
|
_cons | 27.36364 1.594908 17.16 0.000 24.17744 30.54983
------------------------------------------------------------------------------
regress mpg ib5.rep78_2
Source | SS df MS Number of obs = 69
-------------+---------------------------------- F(4, 64) = 4.91
Model | 549.415777 4 137.353944 Prob > F = 0.0016
Residual | 1790.78712 64 27.9810488 R-squared = 0.2348
-------------+---------------------------------- Adj R-squared = 0.1869
Total | 2340.2029 68 34.4147485 Root MSE = 5.2897
------------------------------------------------------------------------------
mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
rep78_2 |
5 | 6.363636 4.066234 1.56 0.123 -1.759599 14.48687
4 | .6666667 3.942718 0.17 0.866 -7.209818 8.543152
3 | -1.566667 3.863059 -0.41 0.686 -9.284014 6.150681
2 | -1.875 4.181884 -0.45 0.655 -10.22927 6.479274
|
_cons | 21 3.740391 5.61 0.000 13.52771 28.47229
------------------------------------------------------------------------------
如果您希望看到与以前相同的变量名,则还可以执行以下操作:
drop rep78
rename rep78_2