生成具有不同概率边际效应结果的表格

Question

我使用“probitmfx”生成了三个不同的边际效应结果，但我很难生成一张包含使用 stargazer 的所有结果的表。你能帮我吗？

数据：

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))

代码：

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")

Answer 1

看起来

stargazer

包不支持这些模型对象。

stargazer

似乎不再被积极开发，所以我不希望它在不久的将来支持

mfx

对象（我可能是错的）。

一种替代方法是使用

modelsummary

包（免责声明：我是作者）。它支持开箱即用的

mfx

对象。请注意使用

shape

参数来消除每一项的“边际”和“条件”估计之间的歧义。文档对此进行了详细解释：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")

	组件	(1)	(2)	(3)
hh	有条件	0.138		0.126
	有条件	（0.096）		（0.096）
	边缘	0.021		0.018
	边缘	（0.015）		（0.014）
（拦截）	有条件	-1.508	-1.489	-1.599
	有条件	（0.167）	（0.157）	（0.185）
nf	有条件		0.259	0.256
	有条件		（0.110）	（0.111）
	边缘		0.037	0.036
	边缘		（0.016）	（0.016）
观察数。		180	180	180
AIC		105.4	100.8	101.2
BIC		111.8	107.2	110.8
均方根误差		0.28	0.27	0.27