使用R中的Stargazer进行几次回归的虚拟变量

我正尝试使用R中的Stargazer软件包创建回归表。我有几个只在虚拟变量中有所不同的回归。如果某些固定效应（即虚拟变量）包含在回归中，我希望它报告自变量的系数，常数等，并说“是”或“否”。这是我的回归：使用R中的Stargazer进行几次回归的虚拟变量

iv1 <- ivreg(data=merge1,log(total_units)~log(priceIndex)|log(taxIndex)) 
iv2 <- ivreg(data=merge1,log(total_units)~log(priceIndex)+factor(fips_state_code)|log(taxIndex)+factor(fips_state_code)) 
iv4 <- ivreg(data=merge1,log(total_units)~log(priceIndex)+factor(fips_state_code) +factor(year)|log(taxIndex)+factor(fips_state_code) +factor(year)) 
iv5 <- ivreg(data=merge1,log(total_units)~log(priceIndex)+factor(fips_state_code) +time*factor(fips_state_code)|log(taxIndex)+factor(fips_state_code) +time*factor(fips_state_code))

（数据帧代码是在底部，顺便）

正如你所看到的，IV1没有假人。 iv2有状态假人。 iv4有州和年假人。 iv5有状态假人和时间趋势假人。

而不是报告所有这些假人的贝塔，我想回归只是报告是否包括每个假人。出于某种原因，我能得到这个使用占星每个单独的回归工作，因为这样的：

> stargazer(iv1,type="text", 
+   omit = c("fips_state_code","year","time"), 
+   omit.labels = c("State FE?","Year FE?","State time trend?")) 

=============================================== 
         Dependent variable:  
        --------------------------- 
         log(total_units)  
----------------------------------------------- 
log(priceIndex)    1.146   
           (1.481)   

Constant      -0.283   
           (3.576)   

----------------------------------------------- 
State FE?      No    
Year FE?      No    
State time trend?    No    
----------------------------------------------- 
Observations     189    
R2       -1.347   
Adjusted R2     -1.359   
Residual Std. Error  1.297 (df = 187)  
=============================================== 
Note:    *p<0.1; **p<0.05; ***p<0.01 
> 
> stargazer(iv2,type="text", 
+   omit = c("fips_state_code","year","time"), 
+   omit.labels = c("State FE?","Year FE?","State time trend?")) 

=============================================== 
         Dependent variable:  
        --------------------------- 
         log(total_units)  
----------------------------------------------- 
log(priceIndex)    1.184   
           (1.561)   

Constant      -0.495   
           (3.767)   

----------------------------------------------- 
State FE?      Yes    
Year FE?      No    
State time trend?    No    
----------------------------------------------- 
Observations     189    
R2       -1.130   
Adjusted R2     -1.487   
Residual Std. Error  1.332 (df = 161)  
=============================================== 
Note:    *p<0.1; **p<0.05; ***p<0.01 
> 
> stargazer(iv4,type="text", 
+   omit = c("fips_state_code","year","time"), 
+   omit.labels = c("State FE?","Year FE?","State time trend?")) 

=============================================== 
         Dependent variable:  
        --------------------------- 
         log(total_units)  
----------------------------------------------- 
log(priceIndex)    0.845   
           (1.049)   

Constant      0.342   
           (2.619)   

----------------------------------------------- 
State FE?      Yes    
Year FE?      Yes    
State time trend?    No    
----------------------------------------------- 
Observations     189    
R2       -0.393   
Adjusted R2     -0.690   
Residual Std. Error  1.098 (df = 155)  
=============================================== 
Note:    *p<0.1; **p<0.05; ***p<0.01 
> 
> stargazer(iv5,type="text", 
+   omit = c("fips_state_code","year","time"), 
+   omit.labels = c("State FE?","Year FE?","State time trend?")) 

=============================================== 
         Dependent variable:  
        --------------------------- 
         log(total_units)  
----------------------------------------------- 
log(priceIndex)    0.554   
           (1.064)   

Constant      0.041   
           (2.393)   

----------------------------------------------- 
State FE?      Yes    
Year FE?      No    
State time trend?    Yes    
----------------------------------------------- 
Observations     189    
R2       -0.001   
Adjusted R2     -0.405   
Residual Std. Error  1.001 (df = 134)  
=============================================== 
Note:    *p<0.1; **p<0.05; ***p<0.01

然而，事情变得奇怪，当我试图同时做多回归：

> stargazer(iv1,iv2,iv4,iv5,type="text", 
+   omit = c("fips_state_code","year","time"), 
+   omit.labels = c("State FE?","Year FE?","State time trend?")) 

======================================================================================= 
              Dependent variable:       
        ------------------------------------------------------------------- 
              log(total_units)       
          (1)    (2)    (3)    (4)  
--------------------------------------------------------------------------------------- 
log(priceIndex)   1.146   1.184   0.845   0.554  
         (1.481)   (1.561)   (1.049)   (1.064)  

Constant     -0.283   -0.495   0.342   0.041  
         (3.576)   (3.767)   (2.619)   (2.393)  

--------------------------------------------------------------------------------------- 
State FE?     No    No    No    No  
Year FE?     No    No    No    No  
State time trend?   No    No    No    No  
--------------------------------------------------------------------------------------- 
Observations    189    189    189    189  
R2      -1.347   -1.130   -0.393   -0.001  
Adjusted R2    -1.359   -1.487   -0.690   -0.405  
Residual Std. Error 1.297 (df = 187) 1.332 (df = 161) 1.098 (df = 155) 1.001 (df = 134) 
======================================================================================= 
Note:              *p<0.1; **p<0.05; ***p<0.01

注意如何所有的假人现在都被报告为“不”。看起来像iv1的使用，没有假人，将Stargazer抛出。我不确定为什么会出现这种情况！

所以，我的问题是：我如何获得组合Stargazer输出看起来像这样？

======================================================================================= 
               Dependent variable:       
         ------------------------------------------------------------------- 
               log(total_units)       
           (1)    (2)    (3)    (4)  
    --------------------------------------------------------------------------------------- 
    log(priceIndex)   1.146   1.184   0.845   0.554  
          (1.481)   (1.561)   (1.049)   (1.064)  

    Constant     -0.283   -0.495   0.342   0.041  
          (3.576)   (3.767)   (2.619)   (2.393)  

    --------------------------------------------------------------------------------------- 
    State FE?     No    Yes    Yes    Yes  
    Year FE?     No    No    Yes    No  
    State time trend?   No    No    No    Yes  
    --------------------------------------------------------------------------------------- 
    Observations    189    189    189    189  
    R2      -1.347   -1.130   -0.393   -0.001  
    Adjusted R2    -1.359   -1.487   -0.690   -0.405  
    Residual Std. Error 1.297 (df = 187) 1.332 (df = 161) 1.098 (df = 155) 1.001 (df = 134) 
    ======================================================================================= 
    Note:              *p<0.1; **p<0.05; ***p<0.01

我知道这似乎是一个愚蠢的问题。但我试图做更多的回归，并且每次手动格式化都是巨大的痛苦。任何和所有的建议将会有所帮助！谢谢。

这是我的数据：

structure(list(year = c(2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 
2006L, 2006L, 2006L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 
2007L, 2007L, 2007L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 
2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 
2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 
2008L, 2008L, 2008L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 
2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 
2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 
2009L, 2009L, 2009L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 
2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 
2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 
2010L, 2010L, 2010L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 
2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 
2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 
2011L, 2011L, 2011L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L), fips_state_code = c(4, 5, 6, 8, 9, 10, 
11, 12, 13, 17, 18, 21, 22, 24, 25, 27, 29, 31, 32, 34, 35, 36, 
38, 45, 46, 48, 55, 4, 5, 6, 8, 9, 10, 11, 12, 13, 17, 18, 21, 
22, 24, 25, 27, 29, 31, 32, 34, 35, 36, 38, 45, 46, 48, 55, 4, 
5, 6, 8, 9, 10, 11, 12, 13, 17, 18, 21, 22, 24, 25, 27, 29, 31, 
32, 34, 35, 36, 38, 45, 46, 48, 55, 4, 5, 6, 8, 9, 10, 11, 12, 
13, 17, 18, 21, 22, 24, 25, 27, 29, 31, 32, 34, 35, 36, 38, 45, 
46, 48, 55, 4, 5, 6, 8, 9, 10, 11, 12, 13, 17, 18, 21, 22, 24, 
25, 27, 29, 31, 32, 34, 35, 36, 38, 45, 46, 48, 55, 4, 5, 6, 
8, 9, 10, 11, 12, 13, 17, 18, 21, 22, 24, 25, 27, 29, 31, 32, 
34, 35, 36, 38, 45, 46, 48, 55, 4, 5, 6, 8, 9, 10, 11, 12, 13, 
17, 18, 21, 22, 24, 25, 27, 29, 31, 32, 34, 35, 36, 38, 45, 46, 
48, 55), priceIndex = c(8L, 16L, 25L, 27L, 2L, 24L, 18L, 26L, 
26L, 26L, 20L, 15L, 1L, 10L, 30L, 11L, 12L, 18L, 17L, 23L, 23L, 
6L, 1L, 5L, 24L, 7L, 10L, 22L, 7L, 20L, 8L, 10L, 2L, 30L, 16L, 
27L, 21L, 14L, 21L, 13L, 16L, 11L, 11L, 7L, 22L, 21L, 30L, 2L, 
19L, 2L, 10L, 17L, 6L, 12L, 5L, 30L, 12L, 15L, 29L, 19L, 16L, 
16L, 22L, 9L, 10L, 9L, 10L, 19L, 22L, 6L, 16L, 24L, 25L, 24L, 
12L, 10L, 26L, 12L, 30L, 16L, 9L, 5L, 8L, 7L, 2L, 4L, 9L, 11L, 
16L, 10L, 13L, 23L, 1L, 10L, 9L, 10L, 2L, 17L, 6L, 15L, 5L, 18L, 
2L, 2L, 13L, 9L, 18L, 10L, 25L, 8L, 26L, 29L, 14L, 3L, 12L, 22L, 
15L, 22L, 14L, 13L, 27L, 4L, 16L, 20L, 12L, 19L, 12L, 20L, 12L, 
17L, 9L, 1L, 28L, 23L, 24L, 13L, 16L, 10L, 21L, 1L, 18L, 15L, 
1L, 15L, 23L, 5L, 16L, 27L, 8L, 7L, 5L, 20L, 3L, 3L, 7L, 3L, 
23L, 1L, 26L, 4L, 5L, 18L, 13L, 17L, 30L, 22L, 14L, 29L, 1L, 
1L, 23L, 12L, 14L, 21L, 29L, 2L, 2L, 16L, 21L, 15L, 11L, 29L, 
26L, 26L, 17L, 20L, 23L, 27L, 7L), totalWeight = c(0.964679717852504, 
0.910153114749701, 0.937533258307128, 0.908932907218257, 0.897870703904312, 
0.570664114467063, 0.793595725333603, 0.960149778439218, 0.702012263867207, 
0.959840103392019, 0.942220302688495, 0.964136166436202, 0.945368646478464, 
0.899686521142446, 0.874686707751765, 0.914447566897194, 0.952932668846809, 
0.960061052199137, 0.926259918197789, 0.885837510813906, 0.901475780845684, 
0.779591446248175, 0.604818428169235, 0.941410295398351, 0.908944873195851, 
0.940822410107144, 0.820433580971128, 0.955543163510268, 0.914685040312209, 
0.948635424851211, 0.946104114649245, 0.932230610899134, 0.558057546499175, 
0.750564479296488, 0.971764930983387, 0.68817373783927, 0.975097771312425, 
0.962368976746048, 0.970230629172812, 0.953507602894619, 0.892296298593537, 
0.930726885101312, 0.908546595974175, 0.962179609608759, 0.96839162884849, 
0.935106841280912, 0.897095564773418, 0.920053661608378, 0.820365371424697, 
0.646532974396383, 0.944743562870499, 0.911857926468439, 0.963635866793497, 
0.944584511990913, 0.973319999879543, 0.912794288563832, 0.950505538487169, 
0.947587097715066, 0.932230610899134, 0.585877063357753, 0.741854702451495, 
0.974829401211451, 0.691439730628336, 0.975813815364686, 0.960835846736876, 
0.961274083799183, 0.959334487143946, 0.89688427237274, 0.937723734431402, 
0.912751255497468, 0.971245010442592, 0.971456099076554, 0.941243932527261, 
0.898677051935661, 0.909199996904926, 0.904176820031607, 0.660962686468937, 
0.926016809434945, 0.927065572055749, 0.969462751042824, 0.887911658008384, 
0.974754164229651, 0.885875391195578, 0.958515313970186, 0.948823953012966, 
0.936466604521389, 0.613240721391053, 0.777793767761539, 0.981209274133896, 
0.706831562657967, 0.982459601639192, 0.969382100794866, 0.970450010303705, 
0.960978075054578, 0.902842393873445, 0.942890887235305, 0.905145032941613, 
0.985616404521002, 0.974335897510718, 0.94236227101429, 0.92257155375435, 
0.903566344156375, 0.905142965998554, 0.661175613077282, 0.948470597079574, 
0.937249077110803, 0.972342549476988, 0.966932959536049, 0.969719582376951, 
0.892634342170433, 0.964670562454497, 0.951929452222193, 0.93649537248916, 
0.612101928212217, 0.724332887315945, 0.980582527341166, 0.712928614791972, 
0.987189573702774, 0.974718254899991, 0.975852766090469, 0.96236303821044, 
0.899854848145425, 0.946343691677045, 0.911796075815032, 0.981805900102976, 
0.97572086066658, 0.940776475282425, 0.920956214063409, 0.918314213645145, 
0.909966039838214, 0.688692601749395, 0.939834970965504, 0.938634040266665, 
0.97372751263285, 0.96841594260187, 0.965125603615924, 0.872094653176646, 
0.974957711538891, 0.972050595493474, 0.933488903015909, 0.664724768281132, 
0.725532855017458, 0.982136493351554, 0.731583789519918, 0.986998917423862, 
0.985672785517343, 0.985359985268326, 0.96327016977471, 0.907456559706999, 
0.947841526350148, 0.924724066870382, 0.984805872685194, 0.974845207727776, 
0.956650623685199, 0.927323325078334, 0.928141500916387, 0.912472003821784, 
0.718170802590407, 0.935947208560755, 0.946217508856548, 0.975281478643238, 
0.969969908612259, 0.97439813803871, 0.849645214769615, 0.971427658757611, 
0.972050595493474, 0.927830874535962, 0.655478629719111, 0.734298949581601, 
0.984919482876493, 0.737396852851197, 0.988375665649713, 0.978252656267413, 
0.978204861100427, 0.961122141972513, 0.941660644201143, 0.953036993924037, 
0.925681643545421, 0.990001340259083, 0.969788001954067, 0.94817860131528, 
0.928318571162957, 0.927885380703944, 0.913542321320878, 0.825157348433747, 
0.948727363244703, 0.948225380163735, 0.975281478643238, 0.971354871768121 
), taxIndex = c(14L, 4L, 4L, 19L, 15L, 18L, 12L, 12L, 14L, 7L, 
10L, 28L, 29L, 30L, 14L, 3L, 23L, 10L, 26L, 15L, 26L, 21L, 29L, 
4L, 22L, 23L, 16L, 5L, 4L, 25L, 7L, 6L, 10L, 16L, 25L, 6L, 13L, 
25L, 18L, 7L, 14L, 27L, 27L, 17L, 6L, 4L, 18L, 10L, 19L, 18L, 
14L, 12L, 19L, 21L, 23L, 5L, 6L, 28L, 28L, 21L, 10L, 30L, 18L, 
23L, 24L, 25L, 19L, 13L, 22L, 14L, 11L, 2L, 13L, 24L, 8L, 30L, 
12L, 13L, 4L, 3L, 1L, 21L, 7L, 8L, 30L, 3L, 7L, 14L, 10L, 23L, 
24L, 17L, 11L, 27L, 18L, 4L, 9L, 14L, 29L, 25L, 4L, 8L, 16L, 
3L, 28L, 2L, 2L, 28L, 28L, 5L, 7L, 30L, 30L, 6L, 24L, 1L, 28L, 
19L, 3L, 2L, 5L, 14L, 23L, 13L, 14L, 23L, 21L, 23L, 14L, 20L, 
21L, 25L, 27L, 30L, 5L, 15L, 27L, 3L, 4L, 15L, 1L, 12L, 9L, 17L, 
24L, 26L, 1L, 25L, 6L, 13L, 11L, 18L, 28L, 30L, 3L, 28L, 8L, 
11L, 11L, 8L, 25L, 11L, 4L, 20L, 1L, 14L, 3L, 15L, 2L, 11L, 1L, 
17L, 30L, 15L, 21L, 14L, 29L, 26L, 1L, 27L, 18L, 12L, 7L, 17L, 
4L, 30L, 23L, 1L, 27L), total_units = c(30L, 12L, 16L, 10L, 30L, 
6L, 8L, 24L, 15L, 6L, 6L, 16L, 15L, 19L, 28L, 16L, 7L, 13L, 12L, 
21L, 9L, 9L, 10L, 4L, 12L, 21L, 30L, 1L, 26L, 7L, 2L, 7L, 1L, 
2L, 15L, 14L, 11L, 28L, 29L, 2L, 22L, 26L, 9L, 21L, 8L, 26L, 
4L, 14L, 18L, 15L, 18L, 11L, 9L, 20L, 3L, 20L, 20L, 24L, 1L, 
9L, 16L, 27L, 29L, 2L, 25L, 16L, 24L, 13L, 11L, 13L, 1L, 19L, 
5L, 5L, 11L, 22L, 16L, 20L, 21L, 2L, 9L, 13L, 15L, 6L, 12L, 28L, 
7L, 24L, 22L, 24L, 21L, 14L, 1L, 6L, 10L, 10L, 26L, 26L, 3L, 
9L, 16L, 30L, 16L, 23L, 20L, 11L, 17L, 16L, 15L, 8L, 20L, 21L, 
1L, 19L, 4L, 4L, 26L, 21L, 18L, 18L, 24L, 8L, 17L, 15L, 20L, 
19L, 10L, 19L, 23L, 4L, 17L, 1L, 20L, 29L, 28L, 26L, 2L, 17L, 
22L, 17L, 17L, 14L, 17L, 13L, 1L, 3L, 15L, 5L, 30L, 27L, 20L, 
10L, 3L, 24L, 28L, 22L, 28L, 20L, 15L, 16L, 10L, 11L, 28L, 27L, 
12L, 5L, 19L, 11L, 15L, 26L, 15L, 27L, 6L, 25L, 7L, 8L, 29L, 
26L, 16L, 25L, 28L, 22L, 20L, 13L, 3L, 8L, 4L, 29L, 10L), time = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7)), .Names = c("year", 
"fips_state_code", "priceIndex", "totalWeight", "taxIndex", "total_units", 
"time"), row.names = c(NA, -189L), vars = list(year), drop = TRUE, indices = list(
    0:26, 27:53, 54:80, 81:107, 108:134, 135:161, 162:188), group_sizes = c(27L, 
27L, 27L, 27L, 27L, 27L, 27L), biggest_group_size = 27L, labels = structure(list(
    year = 2006:2012), class = "data.frame", row.names = c(NA, 
-7L), vars = list(year), drop = TRUE, .Names = "year"), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"))

来源

2016-03-15 ejn

我没有使用Stargazer，但看看你是否可以利用'model.matrix'莫名其妙。 –

我对'model.matrix'不熟悉......你究竟是什么意思？谢谢。 – ejn

我曾与其他模型类型类似的问题和事情是为了固定效应出现的问题。

如果你只是简单的反转型号的顺序：

stargazer(iv5,iv4,iv2,iv1,type="text", 
     omit = c("fips_state_code","year","time"), 
     omit.labels = c("State FE?","Year FE?","State time trend?"))

你得到正确的输出：

======================================================================================= 
              Dependent variable:       
        ------------------------------------------------------------------- 
              log(total_units)       
          (1)    (2)    (3)    (4)  
--------------------------------------------------------------------------------------- 
log(priceIndex)   0.554   0.845   1.184   1.146  
         (1.064)   (1.049)   (1.561)   (1.481)  

Constant     0.041   0.342   -0.495   -0.283  
         (2.393)   (2.619)   (3.767)   (3.576)  

--------------------------------------------------------------------------------------- 
State FE?     Yes    Yes    Yes    No  
Year FE?     No    Yes    No    No  
State time trend?   Yes    No    No    No  
--------------------------------------------------------------------------------------- 
Observations    189    189    189    189  
R2      -0.001   -0.393   -1.130   -1.347  
Adjusted R2    -0.405   -0.690   -1.487   -1.359  
Residual Std. Error 1.001 (df = 134) 1.098 (df = 155) 1.332 (df = 161) 1.297 (df = 187) 
======================================================================================= 
Note:              *p<0.1; **p<0.05; ***p<0.01

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2016-04-08 04:47:29 Florian

我没有意识到这是有效的。但不幸的是，这不是很有用，因为我仍然必须手动将每个表逐一更改为正确的设置。 – ejn

我只是不希望打印出我的回归使用的都是假和这个问题困扰了3个多小时，在这里找到它是令人惊讶的。

我试了一下弗洛里安建议和这样的作品，实际上，固定效应顺序出现在回归于我而言并不重要，我在这里运行的PLM，以下是我的占星代码：

stargazer(cluster.matched.fixed.7,cluster.matched.fixed.2,cluster.matched.fixed.3,cluster.matched.fixed.4, 
      cluster.matched.fixed.5,cluster.matched.fixed.6,cluster.matched.fixed.1,title="Matched sample regression DID results", 
      omit.stat = c("f"),covariate.labels=c("D","D1","D2","log(ROA)","log(totalasset)","log(sales)", 
          "log(GM)","log(Export)","log(Leverage)"),omit = c("year"),omit.labels = c("Year FE?"))

回归7没有固定的效果，结果是正确的。更重要的是让我感兴趣的是，在那里你找到“观星”的省略=

c("fips_state_code","year","time"), 
     omit.labels = c("State FE?","Year FE?","State time trend?")

论点，我打印出来，从R-CRAN的文件，但有一点也不像。

来源

2017-01-15 02:26:53

使用R中的Stargazer进行几次回归的虚拟变量

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