Extracts the coefficients tables from fixest_multi estimations
Source: R/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).
Usage
# S3 method for fixest_multi
coeftable(
object,
vcov = NULL,
keep = NULL,
drop = NULL,
order = NULL,
long = FALSE,
wide = FALSE,
...
)
# S3 method for fixest_multi
se(
object,
vcov = NULL,
keep = NULL,
drop = NULL,
order = NULL,
long = FALSE,
...
)
# S3 method for fixest_multi
tstat(
object,
vcov = NULL,
keep = NULL,
drop = NULL,
order = NULL,
long = FALSE,
...
)
# S3 method for fixest_multi
pvalue(
object,
vcov = NULL,
keep = NULL,
drop = NULL,
order = NULL,
long = FALSE,
...
)Arguments
- object
A
fixest_multiobject, coming from afixestmultiple estimation.- vcov
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.- keep
Character vector. This element is used to display only a subset of variables. This should be a vector of regular expressions (see
base::regexhelp for more info). Each variable satisfying any of the regular expressions will be kept. This argument is applied post aliasing (see argumentdict). Example: you have the variablex1tox55and want to display onlyx1tox9, then you could usekeep = "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.- drop
Character vector. This element is used if some variables are not to be displayed. This should be a vector of regular expressions (see
base::regexhelp for more info). Each variable satisfying any of the regular expressions will be discarded. This argument is applied post aliasing (see argumentdict). Example: you have the variablex1tox55and want to display onlyx1tox9, then you could usedrop = "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.- order
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::regexhelp 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 argumentdict). Example: you have the following variables:month1tomonth6, thenx1tox5, thenyear1toyear6. 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.- long
Logical scalar, default is
FALSE. IfTRUE, then all the information is stacked, with two columns containing the information:"param"and"value". The columnparamcontains the valuescoef/se/tstat/pvalue.- wide
A logical scalar, default is
FALSE. IfTRUE, 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.
Value
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.
Functions
se(fixest_multi): Extracts the standard-errors fromfixest_multiestimationststat(fixest_multi): Extracts the t-stats fromfixest_multiestimationspvalue(fixest_multi): Extracts the p-values fromfixest_multiestimations
Examples
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