| fixef.fixest {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 |
|
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 not regular, then several reference points need to be set, leading to the coefficients to be NOT interpretable. 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)