fixef.fixest | R Documentation |
fixest
estimation.This function retrieves the fixed effects from a fixest
estimation. It is useful only
when there are one or more fixed-effect dimensions.
## S3 method for class 'fixest'
fixef(
object,
notes = getFixest_notes(),
sorted = TRUE,
nthreads = getFixest_nthreads(),
fixef.tol = 1e-05,
fixef.iter = 10000,
...
)
object |
A |
notes |
Logical. Whether to display a note when the fixed-effects coefficients are not regular. |
sorted |
Logical, default is |
nthreads |
The number of threads. Can be: a) an integer lower than, or equal to,
the maximum number of threads; b) 0: meaning all available threads will be used;
c) a number strictly between 0 and 1 which represents the fraction of all threads to use.
The default is to use 50% of all threads. You can set permanently the number
of threads used within this package using the function |
fixef.tol |
Precision used to obtain the fixed-effects. Defaults to |
fixef.iter |
Maximum number of iterations in fixed-effects algorithm (only in use for 2+ fixed-effects). Default is 10000. |
... |
Not currently used. |
If the fixed-effect coefficients are not regular, then several reference points need to be set: this means that the fixed-effects coefficients cannot be directly interpreted. If this is the case, then a warning is raised.
A list containing the vectors of the fixed effects.
If there is more than 1 fixed-effect, then the attribute “references” is created. This is a vector of length the number of fixed-effects, each element contains the number of coefficients set as references. By construction, the elements of the first fixed-effect dimension are never set as references. In the presence of regular fixed-effects, there should be Q-1 references (with Q the number of fixed-effects).
Laurent Berge
plot.fixest.fixef
. See also the main estimation functions femlm
, feols
or feglm
. Use summary.fixest
to see the results with the appropriate
standard-errors, fixef.fixest
to extract the fixed-effect coefficients, and
the function etable
to visualize the results of multiple estimations.
data(trade)
# We estimate the effect of distance on trade => we account for 3 fixed-effects
est_pois = femlm(Euros ~ log(dist_km)|Origin+Destination+Product, trade)
# Obtaining the fixed-effects coefficients:
fe_trade = fixef(est_pois)
# The fixed-effects of the first fixed-effect dimension:
head(fe_trade$Origin)
# Summary information:
summary(fe_trade)
# Plotting them:
plot(fe_trade)
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