Change order of appearance of independent variables in regression table using mtable() from the memisc...
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I use the package memisc for exporting my regression tables to LaTeX. I now have a three model with different independent variables (IV) and various control variables (CV) that are the same in every model.
Using
mtable("Modell 1"=lm(DV ~ IV1 + CV1 + CV2),
"Modell 2"=lm(DV ~ IV2 + CV1 + CV2),
"Modell 3"=lm(DV ~ IV3 + CV1 + CV2))
, I get a this output:
===========================================================================
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)
---------------------------------------------------------------------------
(I truncated the summary statistics). I would like IV1, IV2 and IV3 to appear first, and afterwards all the control variables. Is there any automatable solution?
r lm
edited Nov 9 at 16:46
ANG
4,0472620
asked Nov 9 at 14:05
Lukas
5810
add a comment |
up vote
1
down vote
favorite
I use the package memisc for exporting my regression tables to LaTeX. I now have a three model with different independent variables (IV) and various control variables (CV) that are the same in every model.
Using
mtable("Modell 1"=lm(DV ~ IV1 + CV1 + CV2),
"Modell 2"=lm(DV ~ IV2 + CV1 + CV2),
"Modell 3"=lm(DV ~ IV3 + CV1 + CV2))
, I get a this output:
===========================================================================
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)
---------------------------------------------------------------------------
(I truncated the summary statistics). I would like IV1, IV2 and IV3 to appear first, and afterwards all the control variables. Is there any automatable solution?
r lm
edited Nov 9 at 16:46
ANG
4,0472620
asked Nov 9 at 14:05
Lukas
5810
stargazercan be a good option. Do you absolutely want/need to usemtable?
– ANG
Nov 9 at 16:01
I did all the other table inmtableand like a consistent look (e.g. mtable aligns values by commata), so I want to continue using mtable.
– Lukas
Nov 11 at 10:16
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I use the package memisc for exporting my regression tables to LaTeX. I now have a three model with different independent variables (IV) and various control variables (CV) that are the same in every model.
Using
mtable("Modell 1"=lm(DV ~ IV1 + CV1 + CV2),
"Modell 2"=lm(DV ~ IV2 + CV1 + CV2),
"Modell 3"=lm(DV ~ IV3 + CV1 + CV2))
, I get a this output:
===========================================================================
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)
---------------------------------------------------------------------------
(I truncated the summary statistics). I would like IV1, IV2 and IV3 to appear first, and afterwards all the control variables. Is there any automatable solution?
r lm
edited Nov 9 at 16:46
ANG
4,0472620
asked Nov 9 at 14:05
Lukas
5810
I use the package memisc for exporting my regression tables to LaTeX. I now have a three model with different independent variables (IV) and various control variables (CV) that are the same in every model.
Using
mtable("Modell 1"=lm(DV ~ IV1 + CV1 + CV2),
"Modell 2"=lm(DV ~ IV2 + CV1 + CV2),
"Modell 3"=lm(DV ~ IV3 + CV1 + CV2))
, I get a this output:
===========================================================================
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)
---------------------------------------------------------------------------
(I truncated the summary statistics). I would like IV1, IV2 and IV3 to appear first, and afterwards all the control variables. Is there any automatable solution?
r lm
r lm
edited Nov 9 at 16:46
ANG
4,0472620
asked Nov 9 at 14:05
Lukas
5810
edited Nov 9 at 16:46
ANG
4,0472620
asked Nov 9 at 14:05
Lukas
5810
edited Nov 9 at 16:46
ANG
4,0472620
edited Nov 9 at 16:46
ANG
4,0472620
edited Nov 9 at 16:46
ANG
4,0472620
4,0472620
asked Nov 9 at 14:05
Lukas
5810
asked Nov 9 at 14:05
Lukas
5810
asked Nov 9 at 14:05
Lukas
5810
5810
stargazercan be a good option. Do you absolutely want/need to usemtable?
– ANG
Nov 9 at 16:01
I did all the other table inmtableand like a consistent look (e.g. mtable aligns values by commata), so I want to continue using mtable.
– Lukas
Nov 11 at 10:16
add a comment |
stargazercan be a good option. Do you absolutely want/need to usemtable?
– ANG
Nov 9 at 16:01
I did all the other table inmtableand like a consistent look (e.g. mtable aligns values by commata), so I want to continue using mtable.
– Lukas
Nov 11 at 10:16
stargazer can be a good option. Do you absolutely want/need to use mtable?– ANG
Nov 9 at 16:01
stargazer can be a good option. Do you absolutely want/need to use mtable?– ANG
Nov 9 at 16:01
I did all the other table in
mtable and like a consistent look (e.g. mtable aligns values by commata), so I want to continue using mtable.– Lukas
Nov 11 at 10:16
I did all the other table in
mtable and like a consistent look (e.g. mtable aligns values by commata), so I want to continue using mtable.– Lukas
Nov 11 at 10:16
add a comment |
1 Answer
1
active
oldest
votes
up vote
1
down vote
accepted
This is an intersiting question. Please, consider the following example.
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
=====================================================================
We now want the IV (i.e. dpi, ddpi, log(dpi) and log(ddpi)) to appear first.
Option 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
=====================================================================
Option 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
Solution based on your data
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
answered Nov 9 at 16:44
ANG
4,0472620
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
This is an intersiting question. Please, consider the following example.
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
=====================================================================
We now want the IV (i.e. dpi, ddpi, log(dpi) and log(ddpi)) to appear first.
Option 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
=====================================================================
Option 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
Solution based on your data
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
answered Nov 9 at 16:44
ANG
4,0472620
add a comment |
up vote
1
down vote
accepted
This is an intersiting question. Please, consider the following example.
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
=====================================================================
We now want the IV (i.e. dpi, ddpi, log(dpi) and log(ddpi)) to appear first.
Option 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
=====================================================================
Option 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
Solution based on your data
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
answered Nov 9 at 16:44
ANG
4,0472620
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This is an intersiting question. Please, consider the following example.
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
=====================================================================
We now want the IV (i.e. dpi, ddpi, log(dpi) and log(ddpi)) to appear first.
Option 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
=====================================================================
Option 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
Solution based on your data
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
answered Nov 9 at 16:44
ANG
4,0472620
This is an intersiting question. Please, consider the following example.
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
=====================================================================
We now want the IV (i.e. dpi, ddpi, log(dpi) and log(ddpi)) to appear first.
Option 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
=====================================================================
Option 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
Solution based on your data
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
answered Nov 9 at 16:44
ANG
4,0472620
edited Nov 10 at 18:43
answered Nov 9 at 16:44
ANG
4,0472620
answered Nov 9 at 16:44
ANG
4,0472620
answered Nov 9 at 16:44
ANG
4,0472620
4,0472620
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stargazercan be a good option. Do you absolutely want/need to usemtable?– ANG
Nov 9 at 16:01
I did all the other table in
mtableand like a consistent look (e.g. mtable aligns values by commata), so I want to continue using mtable.– Lukas
Nov 11 at 10:16