Description Usage Arguments Value Author(s) See Also Examples
This function aggregates the results of multiple estimations and display them in the form of only one table whose rownames are the variables and the columns contain the coefficients and standard-errors.
1 2 3 4 |
... |
Used to capture different |
se |
Character scalar. Which kind of standard error should be prompted: “standard” (default), “White”, “cluster”, “twoway”, “threeway” or “fourway”? |
cluster |
A list of vectors. Used only if |
digits |
Integer. The number of digits to be displayed. |
pseudo |
Logical, default is |
drop |
Character vector. This element is used if some variables are not to be displayed. This should be a regular expression (see |
order |
Character vector. This element is used if the user wants the variables to be ordered in a certain way. This should be a regular expression (see |
convergence |
Logical, default is |
signifCode |
Named numeric vector, used to provide the significance codes with respect to the p-value of the coefficients. Default is |
subtitles |
Character vector of the same lenght as the number of models to be displayed. If provided, subtitles are added underneath the dependent variable name. |
keepFactors |
Logical, default is |
Returns a data.frame containing the formatted results.
Laurent Berge
See also the main estimation function femlm
. Use summary.femlm
to see the results with the appropriate standard-errors, getFE
to extract the cluster coefficients, and the functions res2table
and res2tex
to visualize the results of multiple estimations.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | n = 100
x = rnorm(n, 1, 5)**2
y = rnorm(n, -1, 5)**2
z = rpois(n, x*y) + rpois(n, 2)
base = data.frame(x, y, z)
# Results of the Poisson..
est_poisson = femlm(z~log(x)+log(y), base, family="poisson")
# .. and of the Negative Binomial
est_negbin = femlm(z~log(x)+log(y), base, family="negbin")
# We export the two results in one Latex table:
res2table(est_poisson, est_negbin)
# Changing the names & significance codes
res2table(est_poisson, est_negbin, dict = c("log(x)" = "First variable (ln)"),
signifCode = c("a" = 0.001, "$$" = 0.1))
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