我使用软件包memisc将我的回归表导出到LaTeX。我现在有了一个三个模型，每个模型中的自变量（IV）和控制变量（CV）都不同。
使用
[5]，我得到以下输出：
mtable("Modell 1"=lm(DV ~ IV1 + CV1 + CV2),
"Modell 2"=lm(DV ~ IV2 + CV1 + CV2),
"Modell 3"=lm(DV ~ IV3 + CV1 + CV2))
（我将摘要统计信息截短了）。我希望首先出现IV1，IV2和IV3，然后出现所有控制变量。有自动化的解决方案吗？
答案 0 :(得分：1)
这是一个有趣的问题。请考虑以下示例。
library(memisc)
# independent variables (IV) are dpi, ddpi, log(dpi) and log(ddpi)
# control variables (CV) are pop15 and pop75
mtable("Model 1" = lm(sr ~ dpi + pop15 + pop75, LifeCycleSavings),
"Model 2" = lm(sr ~ ddpi + pop15 + pop75, LifeCycleSavings),
"Model 3" = lm(sr ~ log(dpi) + pop15 + pop75, LifeCycleSavings),
"Model 4" = lm(sr ~ log(ddpi) + pop15 + pop75, LifeCycleSavings))
# looks like mtable appends new coefficients to the list of coefficients defined in the first model
# output
Calls:
Model 1: lm(formula = sr ~ dpi + pop15 + pop75, data = LifeCycleSavings)
Model 2: lm(formula = sr ~ ddpi + pop15 + pop75, data = LifeCycleSavings)
Model 3: lm(formula = sr ~ log(dpi) + pop15 + pop75, data = LifeCycleSavings)
Model 4: lm(formula = sr ~ log(ddpi) + pop15 + pop75, data = LifeCycleSavings)
=====================================================================
Model 1 Model 2 Model 3 Model 4
---------------------------------------------------------------------
(Intercept) 31.457*** 28.125*** 36.304** 26.118***
(7.482) (7.184) (10.511) (7.416)
dpi -0.001
(0.001)
pop15 -0.492** -0.452** -0.506** -0.408**
(0.149) (0.141) (0.154) (0.144)
pop75 -1.568 -1.835 -1.649 -1.663
(1.121) (0.998) (1.110) (1.009)
ddpi 0.428*
(0.188)
log(dpi) -0.779
(1.018)
log(ddpi) 1.584*
(0.722)
---------------------------------------------------------------------
R-squared 0.274 0.337 0.271 0.332
adj. R-squared 0.227 0.293 0.223 0.288
sigma 3.939 3.767 3.948 3.780
F 5.797 7.778 5.700 7.608
p 0.002 0.000 0.002 0.000
Log-likelihood -137.410 -135.171 -137.525 -135.355
Deviance 713.767 652.606 717.054 657.424
AIC 284.821 280.341 285.050 280.709
BIC 294.381 289.902 294.610 290.269
N 50 50 50 50
=====================================================================
我们现在希望IV（即dpi，ddpi，log(dpi)和log(ddpi)）首先出现。
选项1
# we will define a fake model which includes all the IVs
mtables <- mtable("Model fake" = lm(sr ~ dpi + ddpi + log(dpi) + log(ddpi), LifeCycleSavings),
"Model 1" = lm(sr ~ dpi + pop15 + pop75, LifeCycleSavings),
"Model 2" = lm(sr ~ ddpi + pop15 + pop75, LifeCycleSavings),
"Model 3" = lm(sr ~ log(dpi) + pop15 + pop75, LifeCycleSavings),
"Model 4" = lm(sr ~ log(ddpi) + pop15 + pop75, LifeCycleSavings))
# we will display mtables without the fake model
mtables[2:5]
# output
Calls:
Model 1: lm(formula = sr ~ dpi + pop15 + pop75, data = LifeCycleSavings)
Model 2: lm(formula = sr ~ ddpi + pop15 + pop75, data = LifeCycleSavings)
Model 3: lm(formula = sr ~ log(dpi) + pop15 + pop75, data = LifeCycleSavings)
Model 4: lm(formula = sr ~ log(ddpi) + pop15 + pop75, data = LifeCycleSavings)
=====================================================================
Model 1 Model 2 Model 3 Model 4
---------------------------------------------------------------------
(Intercept) 31.457*** 28.125*** 36.304** 26.118***
(7.482) (7.184) (10.511) (7.416)
dpi -0.001
(0.001)
ddpi 0.428*
(0.188)
log(dpi) -0.779
(1.018)
log(ddpi) 1.584*
(0.722)
pop15 -0.492** -0.452** -0.506** -0.408**
(0.149) (0.141) (0.154) (0.144)
pop75 -1.568 -1.835 -1.649 -1.663
(1.121) (0.998) (1.110) (1.009)
---------------------------------------------------------------------
R-squared 0.274 0.337 0.271 0.332
adj. R-squared 0.227 0.293 0.223 0.288
sigma 3.939 3.767 3.948 3.780
F 5.797 7.778 5.700 7.608
p 0.002 0.000 0.002 0.000
Log-likelihood -137.410 -135.171 -137.525 -135.355
Deviance 713.767 652.606 717.054 657.424
AIC 284.821 280.341 285.050 280.709
BIC 294.381 289.902 294.610 290.269
N 50 50 50 50
=====================================================================
选项2
# stargazer can be an option
library(stargazer)
stargazer(lm(sr ~ dpi + pop15 + pop75, LifeCycleSavings),
lm(sr ~ ddpi + pop15 + pop75, LifeCycleSavings),
lm(sr ~ log(dpi) + pop15 + pop75, LifeCycleSavings),
lm(sr ~ log(ddpi) + pop15 + pop75, LifeCycleSavings),
type = "text", column.labels = c("Model 1", "Model 2", "Model 3", "Model 4"),
model.numbers = FALSE)
# output
=====================================================================
Dependent variable:
---------------------------------------
sr
Model 1 Model 2 Model 3 Model 4
---------------------------------------------------------------------
dpi -0.001
(0.001)
ddpi 0.428**
(0.188)
log(dpi) -0.779
(1.018)
log(ddpi) 1.584**
(0.722)
pop15 -0.492*** -0.452*** -0.506*** -0.408***
(0.149) (0.141) (0.154) (0.144)
pop75 -1.568 -1.835* -1.649 -1.663
(1.121) (0.998) (1.110) (1.009)
Constant 31.457*** 28.125*** 36.304*** 26.118***
(7.482) (7.184) (10.511) (7.416)
---------------------------------------------------------------------
Observations 50 50 50 50
R2 0.274 0.337 0.271 0.332
Adjusted R2 0.227 0.293 0.223 0.288
Residual Std. Error (df = 46) 3.939 3.767 3.948 3.780
F Statistic (df = 3; 46) 5.797*** 7.778*** 5.700*** 7.608***
=====================================================================
Note: *p<0.1; **p<0.05; ***p<0.01
基于您的数据的解决方案
mtables <- mtable("Modell 0"=lm(DV ~ IV1 + IV2 + IV3),
"Modell 1"=lm(DV ~ IV1 + CV1 + CV2),
"Modell 2"=lm(DV ~ IV2 + CV1 + CV2),
"Modell 3"=lm(DV ~ IV3 + CV1 + CV2))
mtables[2:4]
# or use stargazer() as above