Examples
- Examples
- Default
- Below Statistics
- Standard Errors on same line as coefficient
- Keep, drop and order
- Formatting Estimates, Statistics and decimal points
- Labeling Coefficients
- Grouping Regressions
- Do not print $X block
- Re-order Fixed Effects
- Change Labels for Regression Statistics
- All Available Statistics
- LaTeX Output
- Extralines
- Do Not Print Fixed Effect Suffix
- Standardize Coefficients
- Show Clustered Standard Errors
- MixedModels Support
- Typst Support
Setup for the following examples:
using RegressionTables, DataFrames, RDatasets, FixedEffectModels, GLM;
df = dataset("datasets", "iris");
df[!,:isSmall] = df[!,:SepalWidth] .< 2.9;
rr1 = reg(df, @formula(SepalLength ~ SepalWidth));
rr2 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength + fe(Species)));
rr3 = reg(df, @formula(SepalLength ~ SepalWidth + PetalLength * PetalWidth + fe(Species) + fe(isSmall)));
rr4 = reg(df, @formula(SepalWidth ~ SepalLength + PetalLength + PetalWidth + fe(Species)));
rr5 = reg(df, @formula(SepalWidth ~ SepalLength + (PetalLength ~ PetalWidth) + fe(Species)));
rr6 = glm(@formula(isSmall ~ PetalLength + PetalWidth + Species), df, Binomial());
rr7 = glm(@formula(isSmall ~ SepalLength + PetalLength + PetalWidth), df, Binomial());
lm1 = lm(@formula(SepalLength ~ SepalWidth), df);
lm2 = lm(@formula(SepalLength ~ SepalWidth + PetalLength + Species), df);Default
regtable(rr1,rr2,rr3,rr4,rr5,rr6)
# output
------------------------------------------------------------------------------------------
SepalLength SepalWidth isSmall
------------------------------ ------------------ ---------
(1) (2) (3) (4) (5) (6)
------------------------------------------------------------------------------------------
(Intercept) 6.526*** -1.917
(0.479) (1.242)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** -0.188* 1.048** -0.773
(0.064) (0.129) (0.083) (0.362) (0.554)
PetalWidth -0.625 0.626*** -3.782**
(0.354) (0.123) (1.256)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength 0.378*** -0.313
(0.066) (0.239)
Species: versicolor 10.441***
(1.957)
Species: virginica 13.230***
(2.636)
------------------------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes Yes
isSmall Fixed Effects Yes
------------------------------------------------------------------------------------------
Estimator OLS OLS OLS OLS IV Binomial
------------------------------------------------------------------------------------------
N 150 150 150 150 150 150
R2 0.014 0.863 0.868 0.635 0.080
Within-R2 0.642 0.598 0.391 -0.535
First-stage F statistic 19.962
Pseudo R2 0.006 0.811 0.826 0.862 0.072 0.347
------------------------------------------------------------------------------------------Below Statistics
StdError (default)
regtable(rr1,rr2,rr3,rr4; below_statistic = StdError)
# output
----------------------------------------------------------------------
SepalLength SepalWidth
------------------------------ ----------
(1) (2) (3) (4)
----------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** -0.188*
(0.064) (0.129) (0.083)
PetalWidth -0.625 0.626***
(0.354) (0.123)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength 0.378***
(0.066)
----------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
----------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868 0.635
Within-R2 0.642 0.598 0.391
----------------------------------------------------------------------No statistics
regtable(rr1,rr2,rr3,rr4; below_statistic = nothing)
# output
----------------------------------------------------------------------
SepalLength SepalWidth
------------------------------ ----------
(1) (2) (3) (4)
----------------------------------------------------------------------
(Intercept) 6.526***
SepalWidth -0.223 0.432*** 0.516***
PetalLength 0.776*** 0.723*** -0.188*
PetalWidth -0.625 0.626***
PetalLength & PetalWidth 0.066
SepalLength 0.378***
----------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
----------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868 0.635
Within-R2 0.642 0.598 0.391
----------------------------------------------------------------------TStat
regtable(rr1,rr2,rr3,rr4; below_statistic = TStat)
# output
----------------------------------------------------------------------
SepalLength SepalWidth
------------------------------ ----------
(1) (2) (3) (4)
----------------------------------------------------------------------
(Intercept) 6.526***
(13.628)
SepalWidth -0.223 0.432*** 0.516***
(-1.440) (5.310) (4.982)
PetalLength 0.776*** 0.723*** -0.188*
(12.073) (5.615) (-2.246)
PetalWidth -0.625 0.626***
(-1.763) (5.072)
PetalLength & PetalWidth 0.066
(0.981)
SepalLength 0.378***
(5.761)
----------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
----------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868 0.635
Within-R2 0.642 0.598 0.391
----------------------------------------------------------------------ConfInt (Confidence Interval)
Confidence level defaults to the 95th percentile:
regtable(rr1,rr2,rr3,rr4,lm1,rr7; below_statistic = ConfInt)
# output
-------------------------------------------------------------------------------------------------------------------------------------
SepalLength SepalWidth SepalLength isSmall
-------------------------------------------------- ---------------- --------------- ----------------
(1) (2) (3) (4) (5) (6)
-------------------------------------------------------------------------------------------------------------------------------------
(Intercept) 6.526*** 6.526*** 10.189***
(5.580, 7.473) (5.580, 7.473) (5.080, 15.298)
SepalWidth -0.223 0.432*** 0.516*** -0.223
(-0.530, 0.083) (0.271, 0.593) (0.311, 0.721) (-0.530, 0.083)
PetalLength 0.776*** 0.723*** -0.188* 3.580***
(0.649, 0.903) (0.469, 0.978) (-0.353, -0.023) (2.192, 4.968)
PetalWidth -0.625 0.626*** -3.637**
(-1.325, 0.076) (0.382, 0.870) (-5.847, -1.428)
PetalLength & PetalWidth 0.066
(-0.067, 0.199)
SepalLength 0.378*** -3.519***
(0.248, 0.507) (-4.884, -2.153)
-------------------------------------------------------------------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
-------------------------------------------------------------------------------------------------------------------------------------
Estimator OLS OLS OLS OLS OLS Binomial
-------------------------------------------------------------------------------------------------------------------------------------
N 150 150 150 150 150 150
R2 0.014 0.863 0.868 0.635 0.014
Within-R2 0.642 0.598 0.391
Pseudo R2 0.006 0.811 0.826 0.862 0.006 0.297
-------------------------------------------------------------------------------------------------------------------------------------Set the Confidence Interval level either by setting RegressionTables.default_confint_level or by adjusting the confint_level keyword argument
regtable(rr1,rr2,rr3,rr4; below_statistic = ConfInt, confint_level=0.9, align=:c)
# output
-------------------------------------------------------------------------------------------------
SepalLength SepalWidth
--------------------------------------------------- ----------------
(1) (2) (3) (4)
-------------------------------------------------------------------------------------------------
(Intercept) 6.526***
(5.734, 7.319)
SepalWidth -0.223 0.432*** 0.516***
(-0.480, 0.033) (0.297, 0.567) (0.345, 0.688)
PetalLength 0.776*** 0.723*** -0.188*
(0.669, 0.882) (0.510, 0.937) (-0.326, -0.049)
PetalWidth -0.625 0.626***
(-1.211, -0.038) (0.421, 0.830)
PetalLength & PetalWidth 0.066
(-0.045, 0.177)
SepalLength 0.378***
(0.269, 0.486)
-------------------------------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
-------------------------------------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868 0.635
Within-R2 0.642 0.598 0.391
-------------------------------------------------------------------------------------------------Below statistics (including confidence intervals) are impacted by standardizing the coefficients:
regtable(lm1,lm2,rr6,rr7; below_statistic = ConfInt, standardize_coef=true)
# output
-----------------------------------------------------------------------------------------------
SepalLength isSmall
---------------------------------- ------------------------------------
(1) (2) (3) (4)
-----------------------------------------------------------------------------------------------
(Intercept) 7.881*** 2.887*** -4.119 21.894***
(6.738, 9.024) (2.261, 3.513) (-9.350, 1.112) (10.916, 32.871)
SepalWidth -0.118 0.228***
(-0.279, 0.044) (0.143, 0.312)
PetalLength 1.654*** -2.934 13.578***
(1.383, 1.924) (-7.053, 1.185) (8.313, 18.842)
Species: versicolor -0.546*** 10.611***
(-0.789, -0.303) (6.713, 14.509)
Species: virginica -0.796*** 13.445***
(-1.119, -0.474) (8.195, 18.696)
PetalWidth -6.193** -5.957**
(-10.225, -2.162) (-9.576, -2.339)
SepalLength -6.260***
(-8.690, -3.831)
-----------------------------------------------------------------------------------------------
Estimator OLS OLS Binomial Binomial
-----------------------------------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863
Pseudo R2 0.006 0.811 0.347 0.297
-----------------------------------------------------------------------------------------------Standard Errors on same line as coefficient
regtable(rr1,rr2,rr3,rr4; stat_below=false)
# output
----------------------------------------------------------------------------------------------------
SepalLength SepalWidth
------------------------------------------------------ ----------------
(1) (2) (3) (4)
----------------------------------------------------------------------------------------------------
(Intercept) 6.526*** (0.479)
SepalWidth -0.223 (0.155) 0.432*** (0.081) 0.516*** (0.104)
PetalLength 0.776*** (0.064) 0.723*** (0.129) -0.188* (0.083)
PetalWidth -0.625 (0.354) 0.626*** (0.123)
PetalLength & PetalWidth 0.066 (0.067)
SepalLength 0.378*** (0.066)
----------------------------------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
----------------------------------------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868 0.635
Within-R2 0.642 0.598 0.391
----------------------------------------------------------------------------------------------------Keep, drop and order
See Keep Drop and Order Arguments
Formatting Estimates, Statistics and decimal points
Also see Customization of Defaults
regtable(rr1,rr2,rr3,rr4; estimformat = "%02.5f")
# output
----------------------------------------------------------------------------
SepalLength SepalWidth
------------------------------------ ----------
(1) (2) (3) (4)
----------------------------------------------------------------------------
(Intercept) 6.52622***
(0.479)
SepalWidth -0.22336 0.43222*** 0.51611***
(0.155) (0.081) (0.104)
PetalLength 0.77563*** 0.72335*** -0.18757*
(0.064) (0.129) (0.083)
PetalWidth -0.62469 0.62571***
(0.354) (0.123)
PetalLength & PetalWidth 0.06596
(0.067)
SepalLength 0.37777***
(0.066)
----------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
----------------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868 0.635
Within-R2 0.642 0.598 0.391
----------------------------------------------------------------------------regtable(rr1,rr2,rr3,rr4; digits = 4)
# output
-------------------------------------------------------------------------
SepalLength SepalWidth
--------------------------------- ----------
(1) (2) (3) (4)
-------------------------------------------------------------------------
(Intercept) 6.5262***
(0.479)
SepalWidth -0.2234 0.4322*** 0.5161***
(0.155) (0.081) (0.104)
PetalLength 0.7756*** 0.7234*** -0.1876*
(0.064) (0.129) (0.083)
PetalWidth -0.6247 0.6257***
(0.354) (0.123)
PetalLength & PetalWidth 0.0660
(0.067)
SepalLength 0.3778***
(0.066)
-------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
-------------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868 0.635
Within-R2 0.642 0.598 0.391
-------------------------------------------------------------------------regtable(rr1,rr2,rr3,rr4; statisticformat = "%02.5f")
# output
-------------------------------------------------------------------------
SepalLength SepalWidth
--------------------------------- ----------
(1) (2) (3) (4)
-------------------------------------------------------------------------
(Intercept) 6.526***
(0.47890)
SepalWidth -0.223 0.432*** 0.516***
(0.15508) (0.08139) (0.10359)
PetalLength 0.776*** 0.723*** -0.188*
(0.06425) (0.12883) (0.08349)
PetalWidth -0.625 0.626***
(0.35439) (0.12338)
PetalLength & PetalWidth 0.066
(0.06726)
SepalLength 0.378***
(0.06557)
-------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
-------------------------------------------------------------------------
N 150 150 150 150
R2 0.01382 0.86331 0.86824 0.63516
Within-R2 0.64151 0.59784 0.39114
-------------------------------------------------------------------------regtable(rr1,rr2,rr3,rr4; digits_stats = 4)
# output
----------------------------------------------------------------------
SepalLength SepalWidth
------------------------------ ----------
(1) (2) (3) (4)
----------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** -0.188*
(0.064) (0.129) (0.083)
PetalWidth -0.625 0.626***
(0.354) (0.123)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength 0.378***
(0.066)
----------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
----------------------------------------------------------------------
N 150 150 150 150
R2 0.0138 0.8633 0.8682 0.6352
Within-R2 0.6415 0.5978 0.3911
----------------------------------------------------------------------Labeling Coefficients
labels is applied first, transform_labels applies to within each coefficient
regtable(rr1,rr2; labels = Dict(
"SepalLength" => "My dependent variable: SepalLength",
"PetalLength" => "Length of Petal",
"PetalWidth" => "Width of Petal",
"(Intercept)" => "Const." ,
"isSmall" => "isSmall Dummies",
"SpeciesDummy" => "Species Dummies"
))
# output
-----------------------------------------------------------
My dependent variable: SepalLength
-----------------------------------
(1) (2)
-----------------------------------------------------------
Const. 6.526***
(0.479)
SepalWidth -0.223 0.432***
(0.155) (0.081)
Length of Petal 0.776***
(0.064)
-----------------------------------------------------------
Species Fixed Effects Yes
-----------------------------------------------------------
N 150 150
R2 0.014 0.863
Within-R2 0.642
-----------------------------------------------------------Each piece of an interaction term (or categorical term) is labeled based on its components:
regtable(rr3; labels=Dict(
"SepalWidth" => "Sepal Width",
"PetalLength" => "Petal Length",
"PetalWidth" => "Petal Width"
)) # it is not necessary to specify a "PetalLength & PetalWidth" label
# output
----------------------------------------
SepalLength
----------------------------------------
Sepal Width 0.516***
(0.104)
Petal Length 0.723***
(0.129)
Petal Width -0.625
(0.354)
Petal Length & Petal Width 0.066
(0.067)
----------------------------------------
Species Fixed Effects Yes
isSmall Fixed Effects Yes
----------------------------------------
N 150
R2 0.868
Within-R2 0.598
----------------------------------------transform_labels uses the replace function, so the name does not have to match completely:
regtable(rr1, rr2, rr3; transform_labels = Dict("Width" => " Width", "Length" => " Length"))
# output
-----------------------------------------------------------
Sepal Length
------------------------------
(1) (2) (3)
-----------------------------------------------------------
(Intercept) 6.526***
(0.479)
Sepal Width -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
Petal Length 0.776*** 0.723***
(0.064) (0.129)
Petal Width -0.625
(0.354)
Petal Length & Petal Width 0.066
(0.067)
-----------------------------------------------------------
Species Fixed Effects Yes Yes
isSmall Fixed Effects Yes
-----------------------------------------------------------
N 150 150 150
R2 0.014 0.863 0.868
Within-R2 0.642 0.598
-----------------------------------------------------------Grouping Regressions
Groups are placed above the dependent variable names, allowing you to specify splits or some other group information. Repeated group names are automatically combined.
regtable(rr1,rr2,rr4,rr3; groups = ["grp1", "grp1", "grp2", "grp2"])
# output
-------------------------------------------------------------------------
grp1 grp2
------------------- ------------------------
SepalLength SepalWidth SepalLength
------------------- ---------- -----------
(1) (2) (3) (4)
-------------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** -0.188* 0.723***
(0.064) (0.083) (0.129)
SepalLength 0.378***
(0.066)
PetalWidth 0.626*** -0.625
(0.123) (0.354)
PetalLength & PetalWidth 0.066
(0.067)
-------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
-------------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.635 0.868
Within-R2 0.642 0.391 0.598
-------------------------------------------------------------------------If the length of groups is one more than the number of regressions, the first element is placed in the column above the coefficient names:
regtable(rr1,rr2,rr4,rr3; groups = ["My Group:", "grp1", "grp1", "grp2", "grp2"])
# output
-------------------------------------------------------------------------
My Group: grp1 grp2
------------------- ------------------------
SepalLength SepalWidth SepalLength
------------------- ---------- -----------
(1) (2) (3) (4)
-------------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** -0.188* 0.723***
(0.064) (0.083) (0.129)
SepalLength 0.378***
(0.066)
PetalWidth 0.626*** -0.625
(0.123) (0.354)
PetalLength & PetalWidth 0.066
(0.067)
-------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
-------------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.635 0.868
Within-R2 0.642 0.391 0.598
-------------------------------------------------------------------------You can also specify groups with integer ranges, just note that column 1 is the column with the coefficient names:
regtable(rr1,rr2,rr4,rr3; groups = ["My Group:", "grp1" => 2:3, "grp2" => 4:5])
# output
-------------------------------------------------------------------------
My Group: grp1 grp2
------------------- ------------------------
SepalLength SepalWidth SepalLength
------------------- ---------- -----------
(1) (2) (3) (4)
-------------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** -0.188* 0.723***
(0.064) (0.083) (0.129)
SepalLength 0.378***
(0.066)
PetalWidth 0.626*** -0.625
(0.123) (0.354)
PetalLength & PetalWidth 0.066
(0.067)
-------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
-------------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.635 0.868
Within-R2 0.642 0.391 0.598
-------------------------------------------------------------------------Multi-Level Groups
Specify multiple group levels by passing a matrix or a vector of vectors:
regtable(rr1,rr2,rr4,rr3; groups = [
"grp parent" "grp parent" "grp parent" "other group";
"grp1" "grp1" "grp2" "grp2"
])
# output
-------------------------------------------------------------------------
grp parent other group
-------------------------------- -----------
grp1 grp2
------------------- ------------------------
SepalLength SepalWidth SepalLength
------------------- ---------- -----------
(1) (2) (3) (4)
-------------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** -0.188* 0.723***
(0.064) (0.083) (0.129)
SepalLength 0.378***
(0.066)
PetalWidth 0.626*** -0.625
(0.123) (0.354)
PetalLength & PetalWidth 0.066
(0.067)
-------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
-------------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.635 0.868
Within-R2 0.642 0.391 0.598
-------------------------------------------------------------------------regtable(rr1,rr2,rr4,rr3; groups = [
["Parent Group:", "grp parent" => 2:4, "other group"],
["grp1", "grp1", "grp2", "grp2"]
])
# output
----------------------------------------------------------------------------
Parent Group: grp parent other group
----------------------------------- -----------
grp1 grp2
---------------------- ------------------------
SepalLength SepalWidth SepalLength
---------------------- ---------- -----------
(1) (2) (3) (4)
----------------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** -0.188* 0.723***
(0.064) (0.083) (0.129)
SepalLength 0.378***
(0.066)
PetalWidth 0.626*** -0.625
(0.123) (0.354)
PetalLength & PetalWidth 0.066
(0.067)
----------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
----------------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.635 0.868
Within-R2 0.642 0.391 0.598
----------------------------------------------------------------------------Do not print $X block
regtable(rr1,rr2,rr3,rr7; print_fe_section = false)
# output
---------------------------------------------------------------------
SepalLength isSmall
------------------------------ ---------
(1) (2) (3) (4)
---------------------------------------------------------------------
(Intercept) 6.526*** 10.189***
(0.479) (2.607)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** 3.580***
(0.064) (0.129) (0.708)
PetalWidth -0.625 -3.637**
(0.354) (1.127)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength -3.519***
(0.697)
---------------------------------------------------------------------
Estimator OLS OLS OLS Binomial
---------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868
Within-R2 0.642 0.598
Pseudo R2 0.006 0.811 0.826 0.297
---------------------------------------------------------------------regtable(rr1,rr2,rr3,rr7; print_depvar = false)
# output
---------------------------------------------------------------------
(1) (2) (3) (4)
---------------------------------------------------------------------
(Intercept) 6.526*** 10.189***
(0.479) (2.607)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** 3.580***
(0.064) (0.129) (0.708)
PetalWidth -0.625 -3.637**
(0.354) (1.127)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength -3.519***
(0.697)
---------------------------------------------------------------------
Species Fixed Effects Yes Yes
isSmall Fixed Effects Yes
---------------------------------------------------------------------
Estimator OLS OLS OLS Binomial
---------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868
Within-R2 0.642 0.598
Pseudo R2 0.006 0.811 0.826 0.297
---------------------------------------------------------------------regtable(rr1,rr2,rr3,rr7; print_estimator_section = false)
# output
---------------------------------------------------------------------
SepalLength isSmall
------------------------------ ---------
(1) (2) (3) (4)
---------------------------------------------------------------------
(Intercept) 6.526*** 10.189***
(0.479) (2.607)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** 3.580***
(0.064) (0.129) (0.708)
PetalWidth -0.625 -3.637**
(0.354) (1.127)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength -3.519***
(0.697)
---------------------------------------------------------------------
Species Fixed Effects Yes Yes
isSmall Fixed Effects Yes
---------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868
Within-R2 0.642 0.598
Pseudo R2 0.006 0.811 0.826 0.297
---------------------------------------------------------------------regtable(rr1,rr2,rr3,rr7; number_regressions = false)
# output
---------------------------------------------------------------------
SepalLength isSmall
---------------------------------------------------------------------
(Intercept) 6.526*** 10.189***
(0.479) (2.607)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** 3.580***
(0.064) (0.129) (0.708)
PetalWidth -0.625 -3.637**
(0.354) (1.127)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength -3.519***
(0.697)
---------------------------------------------------------------------
Species Fixed Effects Yes Yes
isSmall Fixed Effects Yes
---------------------------------------------------------------------
Estimator OLS OLS OLS Binomial
---------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868
Within-R2 0.642 0.598
Pseudo R2 0.006 0.811 0.826 0.297
---------------------------------------------------------------------Re-order Fixed Effects
Similar arguments to Keep Drop and Order Arguments (equivalent to keep before the fe_suffix is applied)
regtable(rr1,rr2,rr3,rr4; fixedeffects = [r"isSmall", "SpeciesDummy"])
# output
----------------------------------------------------------------------
SepalLength SepalWidth
------------------------------ ----------
(1) (2) (3) (4)
----------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** -0.188*
(0.064) (0.129) (0.083)
PetalWidth -0.625 0.626***
(0.354) (0.123)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength 0.378***
(0.066)
----------------------------------------------------------------------
isSmall Fixed Effects Yes
----------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868 0.635
Within-R2 0.642 0.598 0.391
----------------------------------------------------------------------Change Labels for Regression Statistics
Also see Customization of Defaults
regtable(rr1,rr2,rr3,rr4; regression_statistics=[
Nobs => "Number of Observations",
R2,
AdjR2 => "Adj. R2"
])
# output
----------------------------------------------------------------------
SepalLength SepalWidth
------------------------------ ----------
(1) (2) (3) (4)
----------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** -0.188*
(0.064) (0.129) (0.083)
PetalWidth -0.625 0.626***
(0.354) (0.123)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength 0.378***
(0.066)
----------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
----------------------------------------------------------------------
Number of Observations 150 150 150 150
R2 0.014 0.863 0.868 0.635
Adj. R2 0.007 0.860 0.861 0.622
----------------------------------------------------------------------All Available Statistics
regtable(rr1,rr2,rr3,rr5; regression_statistics = [
Nobs,
R2,
PseudoR2,
R2CoxSnell,
R2Nagelkerke,
R2Deviance,
AdjR2,
AdjPseudoR2,
AdjR2Deviance,
DOF,
LogLikelihood,
AIC,
AICC,
BIC,
FStat,
FStatPValue,
FStatIV,
FStatIVPValue,
R2Within
])
# output
----------------------------------------------------------------------
SepalLength SepalWidth
------------------------------ ----------
(1) (2) (3) (4)
----------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** 1.048**
(0.064) (0.129) (0.362)
PetalWidth -0.625
(0.354)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength -0.313
(0.239)
----------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
----------------------------------------------------------------------
Estimator OLS OLS OLS IV
----------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868 0.080
Pseudo R2 0.006 0.811 0.826 0.072
Cox-Snell R2 0.014 0.863 0.868 0.080
Nagelkerke R2 0.015 0.944 0.950 0.116
Deviance R2 0.014 0.863 0.868 0.080
Adjusted R2 0.007 0.860 0.861 0.055
Pseudo Adjusted R2 0.000 0.789 0.782 0.026
Deviance Adjusted R2 0.007 0.860 0.861 0.055
Degrees of Freedom 147 145 141 145
Log Likelihood -182.996 -34.787 -32.031 -81.497
AIC 367.992 73.575 72.062 166.995
AICC 368.019 73.657 72.338 167.076
BIC 371.002 79.596 84.105 173.016
F 2.074 129.736 52.402 17.468
F-test p value 0.152 0.000 0.000 0.000
First-stage F statistic 19.962
First-stage p value 0.000
Within-R2 0.642 0.598 -0.535
----------------------------------------------------------------------LaTeX Output
regtable(rr1,rr2,rr3,rr4; render = LatexTable())
# output
\begin{tabular}{lrrrr}
\toprule
& \multicolumn{3}{c}{SepalLength} & \multicolumn{1}{c}{SepalWidth} \\
\cmidrule(lr){2-4} \cmidrule(lr){5-5}
& (1) & (2) & (3) & (4) \\
\midrule
(Intercept) & 6.526*** & & & \\
& (0.479) & & & \\
SepalWidth & -0.223 & 0.432*** & 0.516*** & \\
& (0.155) & (0.081) & (0.104) & \\
PetalLength & & 0.776*** & 0.723*** & -0.188* \\
& & (0.064) & (0.129) & (0.083) \\
PetalWidth & & & -0.625 & 0.626*** \\
& & & (0.354) & (0.123) \\
PetalLength $\times$ PetalWidth & & & 0.066 & \\
& & & (0.067) & \\
SepalLength & & & & 0.378*** \\
& & & & (0.066) \\
\midrule
Species Fixed Effects & & Yes & Yes & Yes \\
isSmall Fixed Effects & & & Yes & \\
\midrule
$N$ & 150 & 150 & 150 & 150 \\
$R^2$ & 0.014 & 0.863 & 0.868 & 0.635 \\
Within-$R^2$ & & 0.642 & 0.598 & 0.391 \\
\bottomrule
\end{tabular}Extralines
Extralines are added to the end of a regression table
regtable(rr1,rr2,rr3,rr4;
extralines=["Specification:", "Option 1", "Option 2", "Option 3", "Option 4"]
)
# output
----------------------------------------------------------------------
SepalLength SepalWidth
------------------------------ ----------
(1) (2) (3) (4)
----------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** -0.188*
(0.064) (0.129) (0.083)
PetalWidth -0.625 0.626***
(0.354) (0.123)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength 0.378***
(0.066)
----------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
----------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868 0.635
Within-R2 0.642 0.598 0.391
Specification: Option 1 Option 2 Option 3 Option 4
----------------------------------------------------------------------You can specify that a single value should fill two columns, note that these will inherit the alignment from their section (so with the default align=:r, the below example would have items below the second and fourth regression):
regtable(rr1,rr2,rr3,rr4; extralines=[
["Specification:", "Option 1", "Option 2", "Option 3", "Option 4"],
["Difference in coefficients", 1.503 => 2:3, 3.515 => 4:5]
], align=:c)
# output
------------------------------------------------------------------------
SepalLength SepalWidth
------------------------------ ----------
(1) (2) (3) (4)
------------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** -0.188*
(0.064) (0.129) (0.083)
PetalWidth -0.625 0.626***
(0.354) (0.123)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength 0.378***
(0.066)
------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
------------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868 0.635
Within-R2 0.642 0.598 0.391
Specification: Option 1 Option 2 Option 3 Option 4
Difference in coefficients 1.503 3.515
------------------------------------------------------------------------You can use the DataRow function to allow for more control, such as underlines and alignment
regtable(rr1,rr2,rr3,rr4; extralines=[
DataRow(["Difference in coefficients", 1.5032 => 2:3, 3.5152 => 4:5]; align = "lcc", print_underlines=[false, true, true]),
["Specification:", "Option 1", "Option 2", "Option 3", "Option 4"],
])
# output
------------------------------------------------------------------------
SepalLength SepalWidth
------------------------------ ----------
(1) (2) (3) (4)
------------------------------------------------------------------------
(Intercept) 6.526***
(0.479)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** -0.188*
(0.064) (0.129) (0.083)
PetalWidth -0.625 0.626***
(0.354) (0.123)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength 0.378***
(0.066)
------------------------------------------------------------------------
Species Fixed Effects Yes Yes Yes
isSmall Fixed Effects Yes
------------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868 0.635
Within-R2 0.642 0.598 0.391
Difference in coefficients 1.503 3.515
------------------- ---------------------
Specification: Option 1 Option 2 Option 3 Option 4
------------------------------------------------------------------------Works similarly with HTML or Latex:
regtable(rr1,rr2,rr3,rr4; render=LatexTable(), extralines=[
["Specification:", "Option 1", "Option 2", "Option 3", "Option 4"],
DataRow(["Difference in coefficients", 1.503 => 2:3, 3.515 => 4:5]; align = "lcc", print_underlines=[false, true, true])
]) # use DataRow to customize alignment
# output
\begin{tabular}{lrrrr}
\toprule
& \multicolumn{3}{c}{SepalLength} & \multicolumn{1}{c}{SepalWidth} \\
\cmidrule(lr){2-4} \cmidrule(lr){5-5}
& (1) & (2) & (3) & (4) \\
\midrule
(Intercept) & 6.526*** & & & \\
& (0.479) & & & \\
SepalWidth & -0.223 & 0.432*** & 0.516*** & \\
& (0.155) & (0.081) & (0.104) & \\
PetalLength & & 0.776*** & 0.723*** & -0.188* \\
& & (0.064) & (0.129) & (0.083) \\
PetalWidth & & & -0.625 & 0.626*** \\
& & & (0.354) & (0.123) \\
PetalLength $\times$ PetalWidth & & & 0.066 & \\
& & & (0.067) & \\
SepalLength & & & & 0.378*** \\
& & & & (0.066) \\
\midrule
Species Fixed Effects & & Yes & Yes & Yes \\
isSmall Fixed Effects & & & Yes & \\
\midrule
$N$ & 150 & 150 & 150 & 150 \\
$R^2$ & 0.014 & 0.863 & 0.868 & 0.635 \\
Within-$R^2$ & & 0.642 & 0.598 & 0.391 \\
Specification: & Option 1 & Option 2 & Option 3 & Option 4 \\
Difference in coefficients & \multicolumn{2}{c}{1.503} & \multicolumn{2}{c}{3.515} \\
\cmidrule(lr){2-3} \cmidrule(lr){4-5}
\bottomrule
\end{tabular}Do Not Print Fixed Effect Suffix
regtable(rr1, rr2, rr3, rr7; print_fe_suffix=false)
# output
---------------------------------------------------------------------
SepalLength isSmall
------------------------------ ---------
(1) (2) (3) (4)
---------------------------------------------------------------------
(Intercept) 6.526*** 10.189***
(0.479) (2.607)
SepalWidth -0.223 0.432*** 0.516***
(0.155) (0.081) (0.104)
PetalLength 0.776*** 0.723*** 3.580***
(0.064) (0.129) (0.708)
PetalWidth -0.625 -3.637**
(0.354) (1.127)
PetalLength & PetalWidth 0.066
(0.067)
SepalLength -3.519***
(0.697)
---------------------------------------------------------------------
Species Yes Yes
isSmall Yes
---------------------------------------------------------------------
Estimator OLS OLS OLS Binomial
---------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863 0.868
Within-R2 0.642 0.598
Pseudo R2 0.006 0.811 0.826 0.297
---------------------------------------------------------------------Standardize Coefficients
Standardizing coefficients adjusts each coefficient by its standard deviation and the standard deviation of the $Y$ variable, making the coefficients equivalent to a 1 standard deviation in $X$ leads to a (result) standard deviation change in $Y$. This is only possible for regressions that store enough information to calculate these standard deviations, currently GLM.jl and MixedModels.jl. The intercept, lacking a standard deviation, is simply the number of standard deviations of $Y$.
regtable(lm1, lm2, rr6, rr7; standardize_coef=true)
# output
------------------------------------------------------------------
SepalLength isSmall
-------------------- ---------------------
(1) (2) (3) (4)
------------------------------------------------------------------
(Intercept) 7.881*** 2.887*** -4.119 21.894***
(0.578) (0.317) (2.669) (5.601)
SepalWidth -0.118 0.228***
(0.082) (0.043)
PetalLength 1.654*** -2.934 13.578***
(0.137) (2.102) (2.686)
Species: versicolor -0.546*** 10.611***
(0.123) (1.989)
Species: virginica -0.796*** 13.445***
(0.163) (2.679)
PetalWidth -6.193** -5.957**
(2.057) (1.846)
SepalLength -6.260***
(1.240)
------------------------------------------------------------------
Estimator OLS OLS Binomial Binomial
------------------------------------------------------------------
N 150 150 150 150
R2 0.014 0.863
Pseudo R2 0.006 0.811 0.347 0.297
------------------------------------------------------------------It is also possible to standardize some coefficients and not others
lm1 = lm(@formula(SepalLength ~ SepalWidth), df);
regtable(lm1, lm1, rr7, rr7; standardize_coef=[false, true, false, true])
# output
---------------------------------------------------------
SepalLength isSmall
------------------- ---------------------
(1) (2) (3) (4)
---------------------------------------------------------
(Intercept) 6.526*** 7.881*** 10.189*** 21.894***
(0.479) (0.578) (2.607) (5.601)
SepalWidth -0.223 -0.118
(0.155) (0.082)
SepalLength -3.519*** -6.260***
(0.697) (1.240)
PetalLength 3.580*** 13.578***
(0.708) (2.686)
PetalWidth -3.637** -5.957**
(1.127) (1.846)
---------------------------------------------------------
Estimator OLS OLS Binomial Binomial
---------------------------------------------------------
N 150 150 150 150
R2 0.014 0.014
Pseudo R2 0.006 0.006 0.297 0.297
---------------------------------------------------------Show Clustered Standard Errors
Displays whether or not the standard errors are clustered and in what ways.
df_cigar = RDatasets.dataset("plm", "Cigar");
rr_c1 = reg(df_cigar, @formula(Sales ~ NDI + fe(State) + fe(Year)), Vcov.cluster(:State));
rr_c2 = reg(df_cigar, @formula(Sales ~ NDI + Price + fe(State) + fe(Year)), Vcov.cluster(:State, :Year));
rr_c3 = reg(df_cigar, @formula(Sales ~ NDI + Price + fe(State)), Vcov.cluster(:Year));
regtable(rr_c1, rr_c2, rr_c3; print_clusters=true, labels=Dict("Year" => "Sales Year"))
# output
----------------------------------------------------------
Sales
-------------------------------
(1) (2) (3)
----------------------------------------------------------
NDI -0.007** -0.005* 0.002*
(0.003) (0.003) (0.001)
Price -0.823** -0.413***
(0.230) (0.084)
----------------------------------------------------------
State Fixed Effects Yes Yes Yes
Sales Year Fixed Effects Yes Yes
----------------------------------------------------------
State Clustering Yes Yes
Sales Year Clustering Yes Yes
----------------------------------------------------------
N 1,380 1,380 1,380
R2 0.832 0.846 0.774
Within-R2 0.154 0.227 0.273
----------------------------------------------------------MixedModels Support
This package does support MixedModels.jl, but instead of displaying fixed effects it will display the variation from the random effects.
form1 = @formula(rt_trunc ~ 1 + spkr + prec + load +
(1 + load | item) +
(1 + spkr + prec + load | subj))
contr = Dict(:spkr => EffectsCoding(),
:prec => EffectsCoding(),
:load => EffectsCoding(),
:item => Grouping(),
:subj => Grouping())
# to make sure the results are always the same, these values help fix the model into one result
fmreθ = [
0.8648075226444749, 0.43344406279292136, 0.532698219245229,
0.03139575786126669, 0.269825335511795, 0.5307313041693793,
0.23438217856147925, 0.0349964462168697, 0.948766814931185,
0.40866263683286375, 0.6055999220729944, 0.9928229644500718,
0.05342261972167761,
]
kbm1 = updateL!(setθ!(LinearMixedModel(form1, MixedModels.dataset(:kb07); contrasts=contr), fmreθ))
form2 = @formula(rt_trunc ~ 1 + spkr + prec + load +
(1 + spkr + prec + load | subj))
fmreθ = [
0.27115451643185495, 0.02114691520013967, 0.8794734878503344,
0.7343424423913391, 0.6603201740011742, 0.8497808579576883,
0.6355311618411573, 0.7807843933484198, 0.9669197738773895,
0.03814101806846881,
]
kbm2 = updateL!(setθ!(LinearMixedModel(form2, MixedModels.dataset(:kb07); contrasts=contr), fmreθ))
form3 = @formula(rt_trunc ~ 1 + spkr + prec + load +
(1 + load | item) +
(1 + spkr + prec * load | subj))
fmreθ = [
0.7221403658923715, 0.3078425012729602, 0.2917886795704724,
0.5000142926713435, 0.7426865162047754, 0.1731367021580622,
0.020327890985133656, 0.7595447732279332, 0.48724482279872006,
0.6205745741154292, 0.3954285463498247, 0.09594315730251379,
0.13946651488431383, 0.6672989094861689, 0.2341117878022333,
0.053650218835408436, 0.143772505670828, 0.027822254707002392,
]
kbm3 = updateL!(setθ!(LinearMixedModel(form3, MixedModels.dataset(:kb07); contrasts=contr), fmreθ))
regtable(kbm1, kbm2, kbm3; labels=Dict(
"subj" => "Subject",
"item" => "Item",
"load: yes" => "Load",
"prec: maintain" => "Prec",
"spkr: old" => "Old Speaker"
)
)
# output
---------------------------------------------------------------
rt_trunc
---------------------------------------
(1) (2) (3)
---------------------------------------------------------------
(Intercept) 2181.911*** 2182.017*** 2180.342***
(117.772) (35.214) (58.616)
Old Speaker 68.034 67.884 67.225*
(53.386) (74.384) (28.793)
Prec -333.636*** -333.785* -334.362***
(76.398) (158.971) (66.565)
Load 78.532 78.426 75.764
(167.790) (150.341) (70.860)
---------------------------------------------------------------
Item | (Intercept) 447.735 40.270
Item | Load 735.081 109.918
Subject | (Intercept) 639.374 220.685 542.038
Subject | Old Speaker 377.476 537.692 265.104
Subject | Prec 556.544 1,180.880 514.950
Subject | Load 783.505 1,115.791 751.626
Subject | Prec & Load 855.252
---------------------------------------------------------------
N 1,789 1,789 1,789
Log Likelihood -14,685.198 -14,765.033 -14,711.866
---------------------------------------------------------------Typst Support
Similar to Latex, this package can produce Typst Tables. This requires Typst v0.11.
regtable(rr1,rr2,rr3,rr4; render = TypstTable())
# output
#table(
columns: (auto, auto, auto, auto, auto),
align: (left, right, right, right, right),
column-gutter: 1fr,
stroke: none,
table.hline(),
[] , table.cell(colspan: 3, align: center)[SepalLength] , table.cell(colspan: 1, align: center)[SepalWidth],
table.hline(start: 1, end: 4, stroke: 0.5pt), table.hline(start: 4, end: 5, stroke: 0.5pt),
[] , [(1)], [(2)], [(3)], [(4)],
table.hline(stroke: 0.7pt),
[(Intercept)] , [6.526$""^(***)$], [], [], [],
[] , [(0.479)], [], [], [],
[SepalWidth] , [-0.223], [0.432$""^(***)$], [0.516$""^(***)$], [],
[] , [(0.155)], [(0.081)], [(0.104)], [],
[PetalLength] , [], [0.776$""^(***)$], [0.723$""^(***)$], [-0.188$""^(*)$],
[] , [], [(0.064)], [(0.129)], [(0.083)],
[PetalWidth] , [], [], [-0.625], [0.626$""^(***)$],
[] , [], [], [(0.354)], [(0.123)],
[PetalLength $times$ PetalWidth], [], [], [0.066], [],
[] , [], [], [(0.067)], [],
[SepalLength] , [], [], [], [0.378$""^(***)$],
[] , [], [], [], [(0.066)],
table.hline(stroke: 0.7pt),
[Species Fixed Effects] , [], [Yes], [Yes], [Yes],
[isSmall Fixed Effects] , [], [], [Yes], [],
table.hline(stroke: 0.7pt),
[_N_] , [150], [150], [150], [150],
[_R_$""^2$] , [0.014], [0.863], [0.868], [0.635],
[Within-_R_$""^2$] , [], [0.642], [0.598], [0.391],
table.hline(),
)regtable(rr1, rr6, rr7; render = TypstTable())
# output
#table(
columns: (auto, auto, auto, auto),
align: (left, right, right, right),
column-gutter: 1fr,
stroke: none,
table.hline(),
[] , table.cell(colspan: 1, align: center)[SepalLength], table.cell(colspan: 2, align: center)[isSmall],
table.hline(start: 1, end: 2, stroke: 0.5pt), table.hline(start: 2, end: 4, stroke: 0.5pt),
[] , [(1)], [(2)], [(3)],
table.hline(stroke: 0.7pt),
[(Intercept)] , [6.526$""^(***)$], [-1.917], [10.189$""^(***)$],
[] , [(0.479)], [(1.242)], [(2.607)],
[SepalWidth] , [-0.223], [], [],
[] , [(0.155)], [], [],
[PetalLength] , [], [-0.773], [3.580$""^(***)$],
[] , [], [(0.554)], [(0.708)],
[PetalWidth] , [], [-3.782$""^(**)$], [-3.637$""^(**)$],
[] , [], [(1.256)], [(1.127)],
[Species: versicolor], [], [10.441$""^(***)$], [],
[] , [], [(1.957)], [],
[Species: virginica] , [], [13.230$""^(***)$], [],
[] , [], [(2.636)], [],
[SepalLength] , [], [], [-3.519$""^(***)$],
[] , [], [], [(0.697)],
table.hline(stroke: 0.7pt),
[Estimator] , [OLS], [Binomial], [Binomial],
table.hline(stroke: 0.7pt),
[_N_] , [150], [150], [150],
[_R_$""^2$] , [0.014], [], [],
[Pseudo _R_$""^2$] , [0.006], [0.347], [0.297],
table.hline(),
)