The spdep sarlm stargazer support was seriously broken when introduced,
and the package author was only interested in advertising the package, and
unresponsive with regard to necessary fixes.
Writing functions to extract what is needed from the summary objects could
be done, but is hard to do right. For example, a sarlm object fitted using
lagsarlm or sacsarlm should never have its covariate coefficients
displayed, only the three-column impacts from the impacts() method (the
coefficients are not equal to of a unit increment on a covariate in these
cases unless \rho_{Lag} = 0 as they would be where the spatial process is
uncorrelated with the covariates).
By the way (checking now), the R code in stargazer is (still) in two
files, and is very difficult to read (badly constructed, one file is 6500
lines long). For spdep fitted objects, it does not use their declared
class, and continues to ignore the need to report the spatial
coefficients. Of course, impacts are ignored. As previously reported,
stargazer should never be used with spdep objects, it is seriously
misleading.
It is possible that pander will work better, but it doesn't do model
comparison tables out of the box as far as I can see - you'd need to take
the relevant parts from the summary object for your fitted model. This
however is something that can be done readily, as can be done for xtable.
I usually create a formatted character matrix and output through xtable if
I need a model comparison table. Look at:
example(NY_data)
esar1f <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME,
data=nydata, listw=listw_NY, family="SAR", method="eigen")
res <- summary(esar1f)
str(res)
to see what is where, and at the code in spdep:::print.summary.spautolm to
see how its print method extracts the relevant pieces.
For example:
c(c(res$Coef[,1:2]), unname(res$lambda), res$lambda.se, res$fit$s2,
res$LL)
gives you four interleaved coefficients and their standard errors,
\rho_{Err} and its standard error, the model sigma^2 and the model log
likelihood. The issue in table construction is then slotting the right
(column) vector elements into the right table rows, because covariates may
be omitted in some models. Row and column names also need care (a trick
for the coefficient standard errors is to use increasing numbers of
spaces). Using a character matrix lets you vary the formatting by row
(different numbers of digits for model sigma^2 and log likelihood).
The stargazer package tried to be clever and make things easy, which might
be OK if it did things right, but it doesn't (for spdep objects and by
extension for others its author doesn't understand), so it should be
avoided. I tried to get its author to fix it, but because of the way the
code is written (one large operative file), suggesting patches without
seeing the workflow clearly remains impossible.
So, work from the summary objects of your fitted models and build a
matrix for, for example, pander or xtable.
Hope this clarifies,
Roger
On Fri, 25 Nov 2016, Melanie Bacou wrote:
You could also check into the `pander` package (documented at
http://rapporter.github.io/pander/#helper-functions).>
AFAIK there's no helper function to output SAR/CAR or GWR objects
directly, but you might be able to work with `pander.lm`.
Else maybe submit a request on GitHub
https://github.com/Rapporter/pander/issues.
--Mel.
On 11/25/2016 04:17 PM, Samar Deen wrote:
Hi Everyone,
I am running some CAR regressions using spautolm. I wanted to generate some
summary tables of the regressions using stargazer to output in Latex.
While stargazer supports some spdep classes, unfortunately, stargazer does
not support spautolm. Has anyone tried to output CAR model regression
results in Latex? Please do let me know if you have come across a
supporting package.
One alternative will be to convert the spautolm class to a supported
class in stargazer. But I haven't had much luck.
Any tips will be most helpful.
Thank you.
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Department of Economics, Norwegian School of Economics,
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