Generating a table with different probit marginal effect results

Question

I generated three different marginal effect results using "probitmfx", but I am having a hard time generating one table with all the results in using Stargazer. Is there a simple way to get a summary table of the results? Can you assist me with this?

Data:

df2<-dput(df)
structure(list(rate = c(-0.107311222, -0.06023034, -0.138748264, 
0.184619982, 0.088974044, -0.058872607, 0.131348687, -0.053702515, 
0.068216527, 0.215789344, 0.138639749, -0.051174665, -0.045196985, 
-0.095051188, 0.001159392, -0.020764965, 0.033003618, -0.196055395, 
0.103205825, 0.056486418, 0.102000598, -0.128290888, 0.036633325, 
0.084915261, 0.041698451, -0.016896662, 0.107018677, -0.001678324, 
-0.133122898, -0.067082861, 0.116107858, -0.141675429, -0.048393834, 
-0.302778038, -0.319781753, 0.122614883, 0.180881509, 0.133791832, 
-0.285518419, 0.074120241, 0.146543977, 0.018349889, -0.052550724, 
0.102829282, -0.095439954, 0.015700106, -0.019055146, -0.044911272, 
-0.035890397, 0.080070564, 0.079254156, -0.128713942, 0.020552319, 
0.091787908, 0.026699636, -0.089861291, -0.182022828, 0.165705314, 
-0.02526569, 0.071750162, 0.0028876, 0.039990083, -0.145919586, 
0.01756501, -0.076821261, -0.096299975, 0.103830743, 0.175349823, 
0.132691523, 0.073382863, -0.005396216, -0.025773657, -0.085033273, 
-0.126286204, 0.145612984, 0.043970104, 0.082394526, 0.111902488, 
-0.108933889, 0.214464764, 0.183460952, 0.043905414, -0.116871092, 
-0.009204845, 0.111763943, -0.013511424, -0.019898844, -0.302661135, 
0.085131953, 0.088928646, 0.005799219, -0.063777641, 0.089138562, 
0.07636777, 0.095144059, -0.167152088, -0.114270214, -0.002021205, 
-0.355851723, 0.109255315, 0.226139158, 0.042715715, 0.142060449, 
0.051991645, 0.042161443, 0.053057342, -0.015200478, 0.137267308, 
0.09010991, -0.01537582, 0.047613153, -0.054587234, -0.027394119, 
0.06626462, -0.021128573, -0.08560769, 0.112576949, 0.117056966, 
0.137683116, 0.040096638, 0.087228789, -0.007281361, 0.087636202, 
0.110457538, 0.020670111, 0.148268886, 0.119604162, 0.072895971, 
0.089831888, 0.002903016, -0.244865847, 0.184418542, -0.000261158, 
0.047423369, -0.159591184, 0.013388555, -0.020602059, -0.072743877, 
0.198643858, 0.031591368, -0.057969321, 0.068837009, -0.125076487, 
0.072122093, 0.052908327, -0.057523198, -0.165615035, 0.063041103, 
-0.039842437, 0.169087798, 0.041504932, 0.110980852, 0.040990451, 
-0.027947848, 0.016794674, 0.081085416, -0.086043452, 0.029303048, 
-0.036168786, 0.064452177, 0.016847651, 0.00269111, 0.045024958, 
0.042005388, -0.055154066, -0.000458537, -0.061194643, -0.214396792, 
-0.113362324, -0.247180845, 0.127000396, -0.40179256, -0.347393071, 
0.239251561, 0.199867055, -0.008715713, 0.103426078, -0.167573283, 
0.010661758, 0.115340493), biinary = c(0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), hh = c(-1.700208252, 
-1.221236402, -1.415366429, -1.724230647, -1.376907443, -1.318593804, 
-1.323127141, -0.627964932, -1.771106781, -1.72548326, -1.290849521, 
-0.709732326, -0.871852002, -0.982169599, -0.919873475, -0.38658951, 
-0.839677984, -1.249780135, -1.144612644, -1.713778862, -1.035289168, 
-0.846352849, -0.634889346, -0.410342159, -0.108214183, -0.161877195, 
0.346562537, -0.101720078, 0.368500446, 0.562002809, 1.183080796, 
0.497918272, 1.527018669, 1.32436549, 1.302712661, 0.399086101, 
0.649117935, 0.300229793, -0.206831053, -0.248597605, -0.409761882, 
-0.009504444, -0.141062331, 0.293578271, 0.144954889, 0.338743081, 
0.484553588, 1.378067756, 1.942658755, 1.653068487, 2.446998329, 
2.387138021, 2.994776315, 2.715304743, 2.523578466, 2.545702023, 
2.537031823, 2.409704694, 2.137192731, 1.261688289, 0.153714263, 
0.650902351, -0.018299915, 0.224361992, 0.239085583, -0.178449939, 
-0.579422266, -0.663033604, -1.243675409, -1.467109942, -1.472290989, 
-0.881981993, -0.167568798, -0.250467284, 0.283151769, 0.4014822, 
0.740266289, 1.343117989, 1.996128572, 2.683991866, 3.056882731, 
3.353606175, 3.590863568, 3.735257474, 3.370647316, 3.348697861, 
3.199170751, 2.585926833, 2.252272372, 1.96782408, 1.7486161, 
1.139118277, 1.057225871, 0.870909502, 0.672488721, 0.526168935, 
0.706910395, 0.492259083, 0.419016303, 0.909915413, 0.880885705, 
0.792156363, 0.571421514, 0.77073858, 0.691044755, 0.41316398, 
0.264507533, -0.006587776, -0.077652221, -0.094061554, -0.05363432, 
-0.393007572, -0.570448817, -0.587722668, -0.365502433, -0.417728503, 
-0.25108918, 0.119696893, 0.291144679, 0.439919661, 0.596219002, 
0.469285674, 0.404677187, 0.416752323, 0.458923606, 0.410485273, 
0.339049524, 0.244574322, 0.267518772, 0.298566876, 0.136941538, 
0.096886403, 0.092708874, 0.429169916, 0.477281263, 0.499850746, 
0.834308948, 0.78589265, 1.147348708, 1.233803692, 1.351269689, 
1.415989216, 2.227401467, 2.33033277, 2.377393977, 2.386751109, 
2.587956902, 3.007684634, 3.001924336, 3.768072792, 3.327590667, 
3.243714561, 3.243664464, 3.204568679, 2.672146377, 2.784252366, 
2.613174627, 2.6760856, 2.578419652, 2.33481207, 2.271461712, 
1.98385315, 2.235181934, 2.213501194, 2.030414786, 1.898965229, 
1.810263641, 1.559248792, 1.737843666, 1.022180445, 0.914149048, 
0.644886886, 0.55528642, 0.243583993, -0.242556065, -0.756391924, 
-1.089880594, -1.680842722, -2.112480229, -2.381529306), nf = c(-0.721410968, 
0.703926607, 1.480815492, 0.428904812, 2.244358009, 2.533114788, 
1.741810334, 0.609323961, 0.480387065, -0.133961276, 0.432879031, 
1.383401078, 1.262328338, 0.960193986, 1.047190559, 1.820036203, 
1.996403901, 1.625606053, 1.824226658, 2.521470163, 1.391647759, 
1.849664146, 1.768307203, 1.507432244, 1.073486832, 0.46255946, 
0.503969088, 0.059672856, -0.588385137, -0.689274175, 0.010445819, 
-1.022047332, 1.015080177, 1.119227683, 1.184444928, 0.648133909, 
1.128815159, 0.465145185, -1.084407673, -2.32499172, -3.078684953, 
-3.202991544, -3.555853431, -3.03649412, -3.35701576, -3.451503029, 
-3.356153804, -2.674339343, -2.257421939, -2.420857206, -1.731147826, 
-1.377799865, -0.739500248, -0.538212503, -0.474170451, -0.389379639, 
-0.266457898, 0.02310255, 0.109479329, -0.529424167, -1.226760054, 
-0.206193352, -0.128853864, 0.338609122, 0.756883799, 0.652485289, 
0.870439845, 0.788668758, 0.787707575, 0.2831103, 0.101172826, 
0.504598989, 1.135364587, 0.964531422, 1.241644958, 1.473738367, 
0.86764212, 0.963812413, 0.59582581, 1.124129224, 1.218568959, 
2.000548397, 2.407587913, 2.841535442, 2.771992854, 2.330129875, 
2.003500867, 1.386608218, 1.550214567, 1.296228258, 1.332068051, 
0.938158767, 0.710234749, 0.296657891, -0.055008054, -0.369439846, 
-0.679907135, -0.8503127, -0.964963796, -0.852253623, -1.315799098, 
-1.710300134, -2.20880797, -2.701559771, -2.863770746, -3.190666353, 
-3.263183924, -3.611698698, -3.266098117, -3.039248746, -2.914920777, 
-3.046590429, -2.689920177, -2.450661247, -2.046730475, -1.746459372, 
-1.387199542, -0.95890577, -0.768414899, -0.452589985, -0.447734231, 
-0.475499478, -0.420730224, -0.370394906, -0.343987385, -0.241718729, 
-0.131092755, 0.04728446, 0.190377092, 0.358913722, 0.502465769, 
0.552758079, 0.834925373, 1.009999376, 1.273285431, 1.328178262, 
1.515026726, 1.546974199, 1.560021739, 1.484378416, 1.259725479, 
1.055637729, 1.125434377, 1.051754038, 0.648705376, 0.431227793, 
0.095153825, 0.146991215, -0.297805448, -0.50359193, -0.890599817, 
-1.189828086, -1.192595195, -1.3934034, -1.549140464, -1.5771226, 
-1.519104043, -1.273531682, -1.186936781, -1.032766078, -0.863532077, 
-0.474761428, -0.055322374, 0.47403833, 0.602254237, 1.036176901, 
1.417714537, 1.741075172, 2.122312722, 2.124121922, 2.274819366, 
2.506218636, 2.504073414, 2.146107988, 1.642392324, 0.797024937, 
0.416612325, -0.277474419, -0.722985517, -1.282071056)), class = "data.frame", row.names = c(NA, 
-180L))

Code:

library("tidyverse")
library("dplyr")
library(tseries)
library("stargazer")
library(mfx) 

df2

mod1<-probitmfx(biinary~hh,data = df2, atmean=FALSE)
mod1

mod2<-probitmfx(biinary~nf,data = df2, atmean=FALSE)
mod2

mod3<-probitmfx(biinary~hh+nf,data = df2, atmean=FALSE)
mod3


stargazer(mod1,mod2,mod3,
          type="text",
          out="/Users/edah/Desktop//three_years_ahead.htm")

image: I will want to get the results in this form

Answer 1

It looks like the stargazer package does not support these model objects. stargazer does not appear to be actively developed anymore, so I would not expect it to support mfx objects in the near future (I may be wrong).

One alternative would be to use the modelsummary package instead (disclaimer: I am the author). It supports mfx objects out of the box. Note the use of the shape argument to disambiguate between “marginal” and “conditional” estimates of each term. This is explained in detail in the documentation: https://modelsummary.com

library(mfx)
library(modelsummary)

mod1 <- probitmfx(biinary ~ hh, data = df2, atmean = FALSE)
mod2 <- probitmfx(biinary ~ nf, data = df2, atmean = FALSE)
mod3 <- probitmfx(biinary ~ hh + nf, data = df2, atmean = FALSE)

modelsummary(
  list(mod1, mod2, mod3),
  shape = term + component ~ model,
  output = "markdown")

	component	(1)	(2)	(3)
hh	conditional	0.138		0.126
	conditional	(0.096)		(0.096)
	marginal	0.021		0.018
	marginal	(0.015)		(0.014)
(Intercept)	conditional	-1.508	-1.489	-1.599
	conditional	(0.167)	(0.157)	(0.185)
nf	conditional		0.259	0.256
	conditional		(0.110)	(0.111)
	marginal		0.037	0.036
	marginal		(0.016)	(0.016)
Num.Obs.		180	180	180
AIC		105.4	100.8	101.2
BIC		111.8	107.2	110.8
RMSE		0.28	0.27	0.27

Edit to answer the modified question:

cm <- c(
    "hh conditional" = "hh",
    "nf conditional" = "nf",
    "(Intercept) conditional" = "Constant"
)

models <- list(
    "(1) Price" = mod1,
    "(2) Price" = mod2,
    "(3) Price" = mod3
)

modelsummary(
  # models to summarize side-by-side
  models,
  # t statistics in parentheses
  statistic = "statistic",
  # rename the coefficients
  coef_map = cm,
  # significance stars
  stars = TRUE,
  # term and component columns are combined
  shape = term:component ~ model,
  # omit all goodness-of-fit statisitcs except # of observations
  gof_map = "nobs")