Comparing regression models with R - r

Question

Is there a tool available in R to produce publication ready regression tables? I am working on a course paper in which I need to compare several regression models and I would be very glad if I could make them nest within a single table like this one, from the estout Stata package.
I have checked xtable, but could not reach the same results. Any tips would be appreciated.
Here's what I have in mind:

Answer 1

You probably want the mtable function in 'memisc' package. It has associated LaTeX output arguments:
==========================================================================
Model 1 Model 2 Model 3
--------------------------------------------------------------------------
Constant 30.628*** 6.360*** 28.566***
(7.409) (1.252) (7.355)
Percentage of population under 15 -0.471** -0.461**
(0.147) (0.145)
Percentage of population over 75 -1.934 -1.691
(1.041) (1.084)
Real per-capita disposable income 0.001 -0.000
(0.001) (0.001)
Growth rate of real per-capita disp. income 0.529* 0.410*
(0.210) (0.196)
--------------------------------------------------------------------------
sigma 3.931 4.189 3.803
R-squared 0.262 0.162 0.338
F 8.332 4.528 5.756
p 0.001 0.016 0.001
N 50 50 50
==========================================================================
This is the LaTeX code you get:
texfile123 <- "mtable123.tex"
write.mtable(mtable123,forLaTeX=TRUE,file=texfile123)
file.show(texfile123)
#------------------------
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Calls:
% Model 1: lm(formula = sr ~ pop15 + pop75, data = LifeCycleSavings)
% Model 2: lm(formula = sr ~ dpi + ddpi, data = LifeCycleSavings)
% Model 3: lm(formula = sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{tabular}{lcD{.}{.}{7}cD{.}{.}{7}cD{.}{.}{7}}
\toprule
&&\multicolumn{1}{c}{Model 1} && \multicolumn{1}{c}{Model 2} && \multicolumn{1}{c}{Model 3}\\
\midrule
Constant & & 30.628^{***} && 6.360^{***} && 28.566^{***}\\
& & (7.409) && (1.252) && (7.355) \\
Percentage of population under 15 & & -0.471^{**} && && -0.461^{**} \\
& & (0.147) && && (0.145) \\
Percentage of population over 75 & & -1.934 && && -1.691 \\
& & (1.041) && && (1.084) \\
Real per-capita disposable income & & && 0.001 && -0.000 \\
& & && (0.001) && (0.001) \\
Growth rate of real per-capita disp. income & & && 0.529^{*} && 0.410^{*} \\
& & && (0.210) && (0.196) \\
\midrule
sigma & & 3.931 && 4.189 && 3.803 \\
R-squared & & 0.262 && 0.162 && 0.338 \\
F & & 8.332 && 4.528 && 5.756 \\
p & & 0.001 && 0.016 && 0.001 \\
N & & 50 && 50 && 50 \\
\bottomrule
\end{tabular}

Answer 2

The R wikibook has some good sources on production quality output in R.
I think this function from Paul Johnson that is listed in the wikibook is exactly what you're looking for:
http://pj.freefaculty.org/R/WorkingExamples/outreg-worked.R
I edited the function for my own use to work with the booktabs format and allow for models that have extra attributes:
http://chandlerlutz.com/R/outregBkTabs.r

Answer 3

xtable can do this, but its somewhat of a hack.
Take two linear models, named lm.x and lm.y.
If you use the following code:
myregtables <- rbind(xtable(summary(lm.x)), xtable(summary(lm.y)))
xtable will then produce a table with both regression models. If you add a \hline (or perhaps two) in LaTeX then it should look OK. You'll still only have one label and caption for the two models. As I said, its somewhat of a hacky solution.