Statistics 如何运行报告所有因子变量的回归?
我想运行一个Statistics 如何运行报告所有因子变量的回归?,statistics,regression,stata,categorical-data,economics,Statistics,Regression,Stata,Categorical Data,Economics,我想运行一个回归,计算因子变量的所有级别的估计值。默认情况下,Stata会将一个虚拟对象作为基本级别忽略 当我使用allbaselevels选项时,它只显示base级别的零值: regress adjusted_volume i.rounded_time, allbaselevels SAS显示删除常量后分类变量的所有估计值 如何在Stata中执行相同的操作?选项allbaselevels是多个显示选项之一,在报告诸如回归等估算命令的结果时,该选项非常有用。但将其指定为选项不会对计算产生任何影
回归
,计算因子
变量的所有级别的估计值。默认情况下,Stata会将一个虚拟对象作为基本
级别忽略
当我使用allbaselevels
选项时,它只显示base
级别的零值:
regress adjusted_volume i.rounded_time, allbaselevels
SAS显示删除常量后分类变量的所有估计值
如何在Stata中执行相同的操作?选项
allbaselevels
是多个显示选项之一,在报告诸如回归
等估算命令的结果时,该选项非常有用。但将其指定为选项不会对计算产生任何影响
正如国家统计局指出的那样:
“…allbaselevels选项与baselevels非常相似,只是allbaselevels列出了交互和主效果中的基本级别。指定
AllBaseLevel将使输出更易于理解……”
您实际要查找的是因子变量运算符:
. sysuse auto, clear
(1978 Automobile Data)
. regress mpg ibn.rep78
note: 5.rep78 omitted because of collinearity
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 |
1 | -6.363636 4.066234 -1.56 0.123 -14.48687 1.759599
2 | -8.238636 2.457918 -3.35 0.001 -13.14889 -3.32838
3 | -7.930303 1.86452 -4.25 0.000 -11.65511 -4.205497
4 | -5.69697 2.02441 -2.81 0.006 -9.741193 -1.652747
5 | 0 (omitted)
|
_cons | 27.36364 1.594908 17.16 0.000 24.17744 30.54983
------------------------------------------------------------------------------
当然,您还需要指定noconstant
选项:
. regress mpg ibn.rep78, noconstant
Source | SS df MS Number of obs = 69
-------------+---------------------------------- F(5, 64) = 227.47
Model | 31824.2129 5 6364.84258 Prob > F = 0.0000
Residual | 1790.78712 64 27.9810488 R-squared = 0.9467
-------------+---------------------------------- Adj R-squared = 0.9426
Total | 33615 69 487.173913 Root MSE = 5.2897
------------------------------------------------------------------------------
mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
rep78 |
1 | 21 3.740391 5.61 0.000 13.52771 28.47229
2 | 19.125 1.870195 10.23 0.000 15.38886 22.86114
3 | 19.43333 .9657648 20.12 0.000 17.504 21.36267
4 | 21.66667 1.246797 17.38 0.000 19.1759 24.15743
5 | 27.36364 1.594908 17.16 0.000 24.17744 30.54983
------------------------------------------------------------------------------
选项
allbaselevels
是几个显示选项之一,在报告诸如回归
等估算命令的结果时,该选项非常有用。但将其指定为选项不会对计算产生任何影响
正如国家统计局指出的那样:
“…allbaselevels选项与baselevels非常相似,只是allbaselevels列出了交互和主效果中的基本级别。指定
AllBaseLevel将使输出更易于理解……”
您实际要查找的是因子变量运算符:
. sysuse auto, clear
(1978 Automobile Data)
. regress mpg ibn.rep78
note: 5.rep78 omitted because of collinearity
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 |
1 | -6.363636 4.066234 -1.56 0.123 -14.48687 1.759599
2 | -8.238636 2.457918 -3.35 0.001 -13.14889 -3.32838
3 | -7.930303 1.86452 -4.25 0.000 -11.65511 -4.205497
4 | -5.69697 2.02441 -2.81 0.006 -9.741193 -1.652747
5 | 0 (omitted)
|
_cons | 27.36364 1.594908 17.16 0.000 24.17744 30.54983
------------------------------------------------------------------------------
当然,您还需要指定noconstant
选项:
. regress mpg ibn.rep78, noconstant
Source | SS df MS Number of obs = 69
-------------+---------------------------------- F(5, 64) = 227.47
Model | 31824.2129 5 6364.84258 Prob > F = 0.0000
Residual | 1790.78712 64 27.9810488 R-squared = 0.9467
-------------+---------------------------------- Adj R-squared = 0.9426
Total | 33615 69 487.173913 Root MSE = 5.2897
------------------------------------------------------------------------------
mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
rep78 |
1 | 21 3.740391 5.61 0.000 13.52771 28.47229
2 | 19.125 1.870195 10.23 0.000 15.38886 22.86114
3 | 19.43333 .9657648 20.12 0.000 17.504 21.36267
4 | 21.66667 1.246797 17.38 0.000 19.1759 24.15743
5 | 27.36364 1.594908 17.16 0.000 24.17744 30.54983
------------------------------------------------------------------------------
谢谢你,斯宾塞。使用稳健回归怎么样?因为
rreg
没有noconstant
选项。不幸的是,并非所有命令都支持所有选项。如果rreg
不支持noconstant
,则无法执行此操作。为什么不使用回归
中的vce(稳健)
选项呢?谢谢Spencer。使用稳健回归怎么样?因为rreg
没有noconstant
选项。不幸的是,并非所有命令都支持所有选项。如果rreg
不支持noconstant
,则无法执行此操作。为什么不使用回归
中的vce(稳健)
选项?