fixest_multi estimationsR/fixest_multi.R
coeftable.fixest_multi.RdSeries of methods to extract the coefficients table or its sub-components from a
fixest_multi objects (i.e. the outcome of multiple estimations).
# S3 method for class 'fixest_multi'
coeftable(
object,
vcov = NULL,
keep = NULL,
drop = NULL,
order = NULL,
long = FALSE,
wide = FALSE,
...
)
# S3 method for class 'fixest_multi'
se(
object,
vcov = NULL,
keep = NULL,
drop = NULL,
order = NULL,
long = FALSE,
...
)
# S3 method for class 'fixest_multi'
tstat(
object,
vcov = NULL,
keep = NULL,
drop = NULL,
order = NULL,
long = FALSE,
...
)
# S3 method for class 'fixest_multi'
pvalue(
object,
vcov = NULL,
keep = NULL,
drop = NULL,
order = NULL,
long = FALSE,
...
)A fixest_multi object, coming from a fixest multiple estimation.
A function to be used to compute the standard-errors of each fixest object. You can
pass extra arguments to this function using the argument .vcov_args. See the example.
Character vector. This element is used to display only a subset of variables. This
should be a vector of regular expressions (see base::regex help for more info). Each
variable satisfying any of the regular expressions will be kept. This argument is applied post
aliasing (see argument dict). Example: you have the variable x1 to x55 and want to display
only x1 to x9, then you could use keep = "x[[:digit:]]$". If the first character is an
exclamation mark, the effect is reversed (e.g. keep = "!Intercept" means: every variable that
does not contain “Intercept” is kept). See details.
Character vector. This element is used if some variables are not to be displayed.
This should be a vector of regular expressions (see base::regex help for more info). Each
variable satisfying any of the regular expressions will be discarded. This argument is applied
post aliasing (see argument dict). Example: you have the variable x1 to x55 and want to
display only x1 to x9, then you could use drop = "x[[:digit:]]{2}". If the first character
is an exclamation mark, the effect is reversed (e.g. drop = "!Intercept" means: every variable
that does not contain “Intercept” is dropped). See details.
Character vector. This element is used if the user wants the variables to be
ordered in a certain way. This should be a vector of regular expressions (see base::regex
help for more info). The variables satisfying the first regular expression will be placed first,
then the order follows the sequence of regular expressions. This argument is applied post
aliasing (see argument dict). Example: you have the following variables: month1 to month6,
then x1 to x5, then year1 to year6. If you want to display first the x's, then the
years, then the months you could use: order = c("x", "year"). If the first character is an
exclamation mark, the effect is reversed (e.g. order = "!Intercept" means: every variable that
does not contain “Intercept” goes first). See details.
Logical scalar, default is FALSE. If TRUE, then all the information
is stacked, with two columns containing the information: "param" and "value".
The column param contains the values coef/se/tstat/pvalue.
A logical scalar, default is FALSE. If TRUE, then a list is returned:
the elements of the list are coef/se/tstat/pvalue. Each element of the list is a wide
table with a column per coefficient.
Other arguments to be passed to summary.fixest.
It returns a data.frame containing the coefficients tables (or just the se/pvalue/tstat)
along with the information on which model was estimated.
If wide = TRUE, then a list is returned. The elements of the list are
coef/se/tstat/pvalue. Each element of the list is a wide table with a column per coefficient.
If long = TRUE, then all the information is stacked. This removes the 4 columns
containing the coefficient estimates to the p-values, and replace them with two
new columns: "param" and "value". The column param contains the
values coef/se/tstat/pvalue, and the column values the
associated numerical information.
se(fixest_multi): Extracts the standard-errors from fixest_multi estimations
tstat(fixest_multi): Extracts the t-stats from fixest_multi estimations
pvalue(fixest_multi): Extracts the p-values from fixest_multi estimations
base = setNames(iris, c("y", "x1", "x2", "x3", "species"))
est_multi = feols(y ~ csw(x.[,1:3]), base, split = ~species)
# we get all the coefficient tables at once
coeftable(est_multi)
#> id sample.var sample rhs coefficient Estimate Std. Error
#> 1 1 species setosa x1 (Intercept) 2.6390012 0.31001431
#> 2 1 species setosa x1 x1 0.6904897 0.08989888
#> 3 2 species setosa x1 + x2 (Intercept) 2.3037382 0.38529423
#> 4 2 species setosa x1 + x2 x1 0.6674162 0.09035581
#> 5 2 species setosa x1 + x2 x2 0.2834193 0.19722377
#> 6 3 species setosa x1 + x2 + x3 (Intercept) 2.3518898 0.39286751
#> 7 3 species setosa x1 + x2 + x3 x1 0.6548350 0.09244742
#> 8 3 species setosa x1 + x2 + x3 x2 0.2375602 0.20801921
#> 9 3 species setosa x1 + x2 + x3 x3 0.2521257 0.34686362
#> 10 4 species versicolor x1 (Intercept) 3.5397347 0.56287357
#> 11 4 species versicolor x1 x1 0.8650777 0.20193757
#> 12 5 species versicolor x1 + x2 (Intercept) 2.1164314 0.49425559
#> 13 5 species versicolor x1 + x2 x1 0.2476422 0.18683892
#> 14 5 species versicolor x1 + x2 x2 0.7355868 0.12476776
#> 15 6 species versicolor x1 + x2 + x3 (Intercept) 1.8955395 0.50705524
#> 16 6 species versicolor x1 + x2 + x3 x1 0.3868576 0.20454490
#> 17 6 species versicolor x1 + x2 + x3 x2 0.9083370 0.16543248
#> 18 6 species versicolor x1 + x2 + x3 x3 -0.6792238 0.43538206
#> 19 7 species virginica x1 (Intercept) 3.9068365 0.75706053
#> 20 7 species virginica x1 x1 0.9015345 0.25310551
#> 21 8 species virginica x1 + x2 (Intercept) 0.6247824 0.52486745
#> 22 8 species virginica x1 + x2 x1 0.2599540 0.15333757
#> 23 8 species virginica x1 + x2 x2 0.9348189 0.08960197
#> 24 9 species virginica x1 + x2 + x3 (Intercept) 0.6998830 0.53360089
#> 25 9 species virginica x1 + x2 + x3 x1 0.3303370 0.17432873
#> 26 9 species virginica x1 + x2 + x3 x2 0.9455356 0.09072204
#> 27 9 species virginica x1 + x2 + x3 x3 -0.1697527 0.19807243
#> t value Pr(>|t|)
#> 1 8.5125144 3.742438e-11
#> 2 7.6807376 6.709843e-10
#> 3 5.9791662 2.894273e-07
#> 4 7.3865333 2.125173e-09
#> 5 1.4370442 1.573296e-01
#> 6 5.9864707 3.034183e-07
#> 7 7.0833236 6.834434e-09
#> 8 1.1420107 2.593594e-01
#> 9 0.7268727 4.709870e-01
#> 10 6.2886852 9.069049e-08
#> 11 4.2838870 8.771860e-05
#> 12 4.2820586 9.063960e-05
#> 13 1.3254313 1.914351e-01
#> 14 5.8956480 3.870715e-07
#> 15 3.7383295 5.112246e-04
#> 16 1.8913091 6.488965e-02
#> 17 5.4906811 1.666695e-06
#> 18 -1.5600639 1.255990e-01
#> 19 5.1605338 4.656345e-06
#> 20 3.5618920 8.434625e-04
#> 21 1.1903622 2.398819e-01
#> 22 1.6953052 9.663372e-02
#> 23 10.4330175 8.009442e-14
#> 24 1.3116227 1.961563e-01
#> 25 1.8949086 6.439972e-02
#> 26 10.4223360 1.074269e-13
#> 27 -0.8570233 3.958750e-01
# Now just the standard-errors
se(est_multi)
#> id sample.var sample rhs (Intercept) x1 x2
#> 1 1 species setosa x1 0.3100143 0.08989888 NA
#> 2 2 species setosa x1 + x2 0.3852942 0.09035581 0.19722377
#> 3 3 species setosa x1 + x2 + x3 0.3928675 0.09244742 0.20801921
#> 4 4 species versicolor x1 0.5628736 0.20193757 NA
#> 5 5 species versicolor x1 + x2 0.4942556 0.18683892 0.12476776
#> 6 6 species versicolor x1 + x2 + x3 0.5070552 0.20454490 0.16543248
#> 7 7 species virginica x1 0.7570605 0.25310551 NA
#> 8 8 species virginica x1 + x2 0.5248675 0.15333757 0.08960197
#> 9 9 species virginica x1 + x2 + x3 0.5336009 0.17432873 0.09072204
#> x3
#> 1 NA
#> 2 NA
#> 3 0.3468636
#> 4 NA
#> 5 NA
#> 6 0.4353821
#> 7 NA
#> 8 NA
#> 9 0.1980724
# wide = TRUE => leads toa list of wide tables
coeftable(est_multi, wide = TRUE)
#> $coef
#> id sample.var sample rhs (Intercept) x1 x2
#> 1 1 species setosa x1 2.6390012 0.6904897 NA
#> 2 2 species setosa x1 + x2 2.3037382 0.6674162 0.2834193
#> 3 3 species setosa x1 + x2 + x3 2.3518898 0.6548350 0.2375602
#> 4 4 species versicolor x1 3.5397347 0.8650777 NA
#> 5 5 species versicolor x1 + x2 2.1164314 0.2476422 0.7355868
#> 6 6 species versicolor x1 + x2 + x3 1.8955395 0.3868576 0.9083370
#> 7 7 species virginica x1 3.9068365 0.9015345 NA
#> 8 8 species virginica x1 + x2 0.6247824 0.2599540 0.9348189
#> 9 9 species virginica x1 + x2 + x3 0.6998830 0.3303370 0.9455356
#> x3
#> 1 NA
#> 2 NA
#> 3 0.2521257
#> 4 NA
#> 5 NA
#> 6 -0.6792238
#> 7 NA
#> 8 NA
#> 9 -0.1697527
#>
#> $se
#> id sample.var sample rhs (Intercept) x1 x2
#> 1 1 species setosa x1 0.3100143 0.08989888 NA
#> 2 2 species setosa x1 + x2 0.3852942 0.09035581 0.19722377
#> 3 3 species setosa x1 + x2 + x3 0.3928675 0.09244742 0.20801921
#> 4 4 species versicolor x1 0.5628736 0.20193757 NA
#> 5 5 species versicolor x1 + x2 0.4942556 0.18683892 0.12476776
#> 6 6 species versicolor x1 + x2 + x3 0.5070552 0.20454490 0.16543248
#> 7 7 species virginica x1 0.7570605 0.25310551 NA
#> 8 8 species virginica x1 + x2 0.5248675 0.15333757 0.08960197
#> 9 9 species virginica x1 + x2 + x3 0.5336009 0.17432873 0.09072204
#> x3
#> 1 NA
#> 2 NA
#> 3 0.3468636
#> 4 NA
#> 5 NA
#> 6 0.4353821
#> 7 NA
#> 8 NA
#> 9 0.1980724
#>
#> $tstat
#> id sample.var sample rhs (Intercept) x1 x2
#> 1 1 species setosa x1 8.512514 7.680738 NA
#> 2 2 species setosa x1 + x2 5.979166 7.386533 1.437044
#> 3 3 species setosa x1 + x2 + x3 5.986471 7.083324 1.142011
#> 4 4 species versicolor x1 6.288685 4.283887 NA
#> 5 5 species versicolor x1 + x2 4.282059 1.325431 5.895648
#> 6 6 species versicolor x1 + x2 + x3 3.738329 1.891309 5.490681
#> 7 7 species virginica x1 5.160534 3.561892 NA
#> 8 8 species virginica x1 + x2 1.190362 1.695305 10.433017
#> 9 9 species virginica x1 + x2 + x3 1.311623 1.894909 10.422336
#> x3
#> 1 NA
#> 2 NA
#> 3 0.7268727
#> 4 NA
#> 5 NA
#> 6 -1.5600639
#> 7 NA
#> 8 NA
#> 9 -0.8570233
#>
#> $pvalue
#> id sample.var sample rhs (Intercept) x1 x2
#> 1 1 species setosa x1 3.742438e-11 6.709843e-10 NA
#> 2 2 species setosa x1 + x2 2.894273e-07 2.125173e-09 1.573296e-01
#> 3 3 species setosa x1 + x2 + x3 3.034183e-07 6.834434e-09 2.593594e-01
#> 4 4 species versicolor x1 9.069049e-08 8.771860e-05 NA
#> 5 5 species versicolor x1 + x2 9.063960e-05 1.914351e-01 3.870715e-07
#> 6 6 species versicolor x1 + x2 + x3 5.112246e-04 6.488965e-02 1.666695e-06
#> 7 7 species virginica x1 4.656345e-06 8.434625e-04 NA
#> 8 8 species virginica x1 + x2 2.398819e-01 9.663372e-02 8.009442e-14
#> 9 9 species virginica x1 + x2 + x3 1.961563e-01 6.439972e-02 1.074269e-13
#> x3
#> 1 NA
#> 2 NA
#> 3 0.470987
#> 4 NA
#> 5 NA
#> 6 0.125599
#> 7 NA
#> 8 NA
#> 9 0.395875
#>
# long = TRUE, all the information is stacked
coeftable(est_multi, long = TRUE)
#> id sample.var sample rhs coefficient param value
#> 1 1 species setosa x1 (Intercept) coef 2.639001e+00
#> 2 1 species setosa x1 (Intercept) se 3.100143e-01
#> 3 1 species setosa x1 (Intercept) tstat 8.512514e+00
#> 4 1 species setosa x1 (Intercept) pvalue 3.742438e-11
#> 5 1 species setosa x1 x1 coef 6.904897e-01
#> 6 1 species setosa x1 x1 se 8.989888e-02
#> 7 1 species setosa x1 x1 tstat 7.680738e+00
#> 8 1 species setosa x1 x1 pvalue 6.709843e-10
#> 9 2 species setosa x1 + x2 (Intercept) coef 2.303738e+00
#> 10 2 species setosa x1 + x2 (Intercept) se 3.852942e-01
#> 11 2 species setosa x1 + x2 (Intercept) tstat 5.979166e+00
#> 12 2 species setosa x1 + x2 (Intercept) pvalue 2.894273e-07
#> 13 2 species setosa x1 + x2 x1 coef 6.674162e-01
#> 14 2 species setosa x1 + x2 x1 se 9.035581e-02
#> 15 2 species setosa x1 + x2 x1 tstat 7.386533e+00
#> 16 2 species setosa x1 + x2 x1 pvalue 2.125173e-09
#> 17 2 species setosa x1 + x2 x2 coef 2.834193e-01
#> 18 2 species setosa x1 + x2 x2 se 1.972238e-01
#> 19 2 species setosa x1 + x2 x2 tstat 1.437044e+00
#> 20 2 species setosa x1 + x2 x2 pvalue 1.573296e-01
#> 21 3 species setosa x1 + x2 + x3 (Intercept) coef 2.351890e+00
#> 22 3 species setosa x1 + x2 + x3 (Intercept) se 3.928675e-01
#> 23 3 species setosa x1 + x2 + x3 (Intercept) tstat 5.986471e+00
#> 24 3 species setosa x1 + x2 + x3 (Intercept) pvalue 3.034183e-07
#> 25 3 species setosa x1 + x2 + x3 x1 coef 6.548350e-01
#> 26 3 species setosa x1 + x2 + x3 x1 se 9.244742e-02
#> 27 3 species setosa x1 + x2 + x3 x1 tstat 7.083324e+00
#> 28 3 species setosa x1 + x2 + x3 x1 pvalue 6.834434e-09
#> 29 3 species setosa x1 + x2 + x3 x2 coef 2.375602e-01
#> 30 3 species setosa x1 + x2 + x3 x2 se 2.080192e-01
#> 31 3 species setosa x1 + x2 + x3 x2 tstat 1.142011e+00
#> 32 3 species setosa x1 + x2 + x3 x2 pvalue 2.593594e-01
#> 33 3 species setosa x1 + x2 + x3 x3 coef 2.521257e-01
#> 34 3 species setosa x1 + x2 + x3 x3 se 3.468636e-01
#> 35 3 species setosa x1 + x2 + x3 x3 tstat 7.268727e-01
#> 36 3 species setosa x1 + x2 + x3 x3 pvalue 4.709870e-01
#> 37 4 species versicolor x1 (Intercept) coef 3.539735e+00
#> 38 4 species versicolor x1 (Intercept) se 5.628736e-01
#> 39 4 species versicolor x1 (Intercept) tstat 6.288685e+00
#> 40 4 species versicolor x1 (Intercept) pvalue 9.069049e-08
#> 41 4 species versicolor x1 x1 coef 8.650777e-01
#> 42 4 species versicolor x1 x1 se 2.019376e-01
#> 43 4 species versicolor x1 x1 tstat 4.283887e+00
#> 44 4 species versicolor x1 x1 pvalue 8.771860e-05
#> 45 5 species versicolor x1 + x2 (Intercept) coef 2.116431e+00
#> 46 5 species versicolor x1 + x2 (Intercept) se 4.942556e-01
#> 47 5 species versicolor x1 + x2 (Intercept) tstat 4.282059e+00
#> 48 5 species versicolor x1 + x2 (Intercept) pvalue 9.063960e-05
#> 49 5 species versicolor x1 + x2 x1 coef 2.476422e-01
#> 50 5 species versicolor x1 + x2 x1 se 1.868389e-01
#> 51 5 species versicolor x1 + x2 x1 tstat 1.325431e+00
#> 52 5 species versicolor x1 + x2 x1 pvalue 1.914351e-01
#> 53 5 species versicolor x1 + x2 x2 coef 7.355868e-01
#> 54 5 species versicolor x1 + x2 x2 se 1.247678e-01
#> 55 5 species versicolor x1 + x2 x2 tstat 5.895648e+00
#> 56 5 species versicolor x1 + x2 x2 pvalue 3.870715e-07
#> 57 6 species versicolor x1 + x2 + x3 (Intercept) coef 1.895540e+00
#> 58 6 species versicolor x1 + x2 + x3 (Intercept) se 5.070552e-01
#> 59 6 species versicolor x1 + x2 + x3 (Intercept) tstat 3.738329e+00
#> 60 6 species versicolor x1 + x2 + x3 (Intercept) pvalue 5.112246e-04
#> 61 6 species versicolor x1 + x2 + x3 x1 coef 3.868576e-01
#> 62 6 species versicolor x1 + x2 + x3 x1 se 2.045449e-01
#> 63 6 species versicolor x1 + x2 + x3 x1 tstat 1.891309e+00
#> 64 6 species versicolor x1 + x2 + x3 x1 pvalue 6.488965e-02
#> 65 6 species versicolor x1 + x2 + x3 x2 coef 9.083370e-01
#> 66 6 species versicolor x1 + x2 + x3 x2 se 1.654325e-01
#> 67 6 species versicolor x1 + x2 + x3 x2 tstat 5.490681e+00
#> 68 6 species versicolor x1 + x2 + x3 x2 pvalue 1.666695e-06
#> 69 6 species versicolor x1 + x2 + x3 x3 coef -6.792238e-01
#> 70 6 species versicolor x1 + x2 + x3 x3 se 4.353821e-01
#> 71 6 species versicolor x1 + x2 + x3 x3 tstat -1.560064e+00
#> 72 6 species versicolor x1 + x2 + x3 x3 pvalue 1.255990e-01
#> 73 7 species virginica x1 (Intercept) coef 3.906836e+00
#> 74 7 species virginica x1 (Intercept) se 7.570605e-01
#> 75 7 species virginica x1 (Intercept) tstat 5.160534e+00
#> 76 7 species virginica x1 (Intercept) pvalue 4.656345e-06
#> 77 7 species virginica x1 x1 coef 9.015345e-01
#> 78 7 species virginica x1 x1 se 2.531055e-01
#> 79 7 species virginica x1 x1 tstat 3.561892e+00
#> 80 7 species virginica x1 x1 pvalue 8.434625e-04
#> 81 8 species virginica x1 + x2 (Intercept) coef 6.247824e-01
#> 82 8 species virginica x1 + x2 (Intercept) se 5.248675e-01
#> 83 8 species virginica x1 + x2 (Intercept) tstat 1.190362e+00
#> 84 8 species virginica x1 + x2 (Intercept) pvalue 2.398819e-01
#> 85 8 species virginica x1 + x2 x1 coef 2.599540e-01
#> 86 8 species virginica x1 + x2 x1 se 1.533376e-01
#> 87 8 species virginica x1 + x2 x1 tstat 1.695305e+00
#> 88 8 species virginica x1 + x2 x1 pvalue 9.663372e-02
#> 89 8 species virginica x1 + x2 x2 coef 9.348189e-01
#> 90 8 species virginica x1 + x2 x2 se 8.960197e-02
#> 91 8 species virginica x1 + x2 x2 tstat 1.043302e+01
#> 92 8 species virginica x1 + x2 x2 pvalue 8.009442e-14
#> 93 9 species virginica x1 + x2 + x3 (Intercept) coef 6.998830e-01
#> 94 9 species virginica x1 + x2 + x3 (Intercept) se 5.336009e-01
#> 95 9 species virginica x1 + x2 + x3 (Intercept) tstat 1.311623e+00
#> 96 9 species virginica x1 + x2 + x3 (Intercept) pvalue 1.961563e-01
#> 97 9 species virginica x1 + x2 + x3 x1 coef 3.303370e-01
#> 98 9 species virginica x1 + x2 + x3 x1 se 1.743287e-01
#> 99 9 species virginica x1 + x2 + x3 x1 tstat 1.894909e+00
#> 100 9 species virginica x1 + x2 + x3 x1 pvalue 6.439972e-02
#> 101 9 species virginica x1 + x2 + x3 x2 coef 9.455356e-01
#> 102 9 species virginica x1 + x2 + x3 x2 se 9.072204e-02
#> 103 9 species virginica x1 + x2 + x3 x2 tstat 1.042234e+01
#> 104 9 species virginica x1 + x2 + x3 x2 pvalue 1.074269e-13
#> 105 9 species virginica x1 + x2 + x3 x3 coef -1.697527e-01
#> 106 9 species virginica x1 + x2 + x3 x3 se 1.980724e-01
#> 107 9 species virginica x1 + x2 + x3 x3 tstat -8.570233e-01
#> 108 9 species virginica x1 + x2 + x3 x3 pvalue 3.958750e-01