使用 memisc 包中的 mtable() 在回归 table 中更改自变量的出现顺序
Change order of appearance of independent variables in regression table using mtable() from the memisc package
我使用包 memisc 将我的回归表导出到 LaTeX。我现在有一个三个模型，每个模型都有不同的自变量 (IV) 和各种控制变量 (CV)。
正在使用
mtable("Modell 1"=lm(DV ~ IV1 + CV1 + CV2),
"Modell 2"=lm(DV ~ IV2 + CV1 + CV2),
"Modell 3"=lm(DV ~ IV3 + CV1 + CV2))
，我得到这样的输出：
===========================================================================
Modell 1 Modell 2 Modell 3 Modell 4
---------------------------------------------------------------------------
(Intercept) -0.122 -0.112 -0.122 -0.097
(0.116) (0.115) (0.116) (0.116)
IV1 0.026 -0.083
(0.030) (0.064)
CV1 0.017 0.015 0.018 0.012
(0.019) (0.019) (0.019) (0.019)
CV2 0.064*** 0.063*** 0.066*** 0.063***
(0.016) (0.015) (0.016) (0.016)
IV3 0.076 0.196*
(0.045) (0.099)
IV4 -0.016 -0.047
(0.076) (0.078)
---------------------------------------------------------------------------
（我截断了汇总统计信息）。我希望 IV1、IV2 和 IV3 先出现，然后是所有控制变量。是否有任何可自动化的解决方案？
这是一个中间问题。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
根据您的数据得出的解决方案
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
我使用包 memisc 将我的回归表导出到 LaTeX。我现在有一个三个模型，每个模型都有不同的自变量 (IV) 和各种控制变量 (CV)。
正在使用
mtable("Modell 1"=lm(DV ~ IV1 + CV1 + CV2),
"Modell 2"=lm(DV ~ IV2 + CV1 + CV2),
"Modell 3"=lm(DV ~ IV3 + CV1 + CV2))
，我得到这样的输出：
===========================================================================
Modell 1 Modell 2 Modell 3 Modell 4
---------------------------------------------------------------------------
(Intercept) -0.122 -0.112 -0.122 -0.097
(0.116) (0.115) (0.116) (0.116)
IV1 0.026 -0.083
(0.030) (0.064)
CV1 0.017 0.015 0.018 0.012
(0.019) (0.019) (0.019) (0.019)
CV2 0.064*** 0.063*** 0.066*** 0.063***
(0.016) (0.015) (0.016) (0.016)
IV3 0.076 0.196*
(0.045) (0.099)
IV4 -0.016 -0.047
(0.076) (0.078)
---------------------------------------------------------------------------
（我截断了汇总统计信息）。我希望 IV1、IV2 和 IV3 先出现，然后是所有控制变量。是否有任何可自动化的解决方案？
这是一个中间问题。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
根据您的数据得出的解决方案
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