利用边际效应，我试图
1.按"周期"获取对比度
1.按"周期"可视化预测
1.按"会话"可视化预测
结果失败了!任何帮助都很感激。
df到底

library(lme4)
library(lmerTest)
library(marginaleffects)
library(dplyr)

import dat_long
dat_long$group <- as.factor(dat_long$group)
dat_long$period <- as.factor(dat_long$period)

dat_long <- dat_long %>% 
  mutate(group2 = group)

m222 <- lmer(money ~ session + period + group2 + (1 | id2) + (1 | session / date / period), data = dat_long ) 
summary(m222)

contrasts_periods <- comparisons(
  m222,
  variables = "period",
  include_random = FALSE,
  newdata = datagrid(
    period = c("p1", "p2", "p3", "p4")
  )
)
 

#2

    pred_period <- predictions(  m222,
                          newdata = datagrid(id2 = NA,
                                             period = c("p1", "p2", "p3", "p4"),
                                             include_random = FALSE))



    ggplot(pred, aes(x = period, y = predicted,
                     ymin = conf.low, ymax = conf.high))

  

#3

pred_session <- predictions(  m222,
                             newdata = datagrid(id2 = NA,
                                                session = seq(from = 16, to = 38, by = 1),
                                                include_random = FALSE))

错误代码：
错误：无法使用此模型计算预测值。您可以
尝试为newdata参数提供不同的数据集。如果不起作用，您可以在Github Issue Tracker上提交报告：
https://github.com/vincentarelbundock/marginaleffects/issues
错误：无法使用此模型计算预测值。您可以尝试为newdata参数提供不同的数据集。如果不起作用，您可以在Github Issue Tracker上提交报告：
https://github.com/vincentarelbundock/marginaleffects/issues
还引发了此错误：无效的分组因子规范id2此外：警告信息：模型数据中缺少某些变量名称：包括_随机
df如下：

dat_long  <- structure(list(money = c(22625, 23349, 18189, 16302, 12874, 17343, 
15912, 15300, 18762, 23506, 18290, 10296, 13172, 15288, 12462, 
16380, 14352, 15052, 14497, 16241, 14832, 14304, 15120, 3745, 
15012, 13916, 13056, 12432, 12441, 15762, 10660, 18150, 15496, 
16905, 14872, 16166, 15892, 18755, 16241, 16874, 15836, 15225, 
32190, 30450, 25200, 19840, 31800, 29892, 10416, 26520, 29029, 
28623, 26544, 16988, 22801, 19317, 30694, 20447, 26030, 22378, 
27267, 21760, 26334, 26896, 32085, 28914, 26892, 18683, 19468, 
16920, 17640, 20829, 17920, 17424, 20538, 21760, 14985, 13407, 
13624, 15470, 21252, 15129, 21336, 17760, 22908, 16940, 15860, 
17732, 18048, 16002, 18480, 20328, 22848, 19630, 17030, 24220, 
16074, 20234, 20413, 20448, 23715, 22010, 24000, 25245, 23088, 
16445, 22200, 24786, 20100, 17766, 20022, 22194, 16284, 23560, 
16638, 23345, 26788, 21462, 16786, 16362, 22176, 21600, 21744, 
21432, 19026, 22330, 20049, 19968, 18876, 20850, 19126, 18788, 
19650, 24320, 17100, 22785, 18875, 23520, 21252, 17766, 20304, 
19170, 17780, 19296, 15855, 16244, 19875, 18476, 16284, 17780, 
14279, 20562, 17556, 17568, 20700, 19750, 22401, 19625, 20264, 
18176, 19272, 24180, 21855, 22490, 22560, 19599, 20550, 17856, 
20670, 18768, 20385, 17856, 16891, 18081, 18755, 18796, 21450, 
18576, 16263, 18460, 16616, 16992, 17250, 18995, 21021, 20368, 
17536, 18626, 11742, 15872, 19684, 17250, 15616, 17176, 17653, 
17690, 19890, 18054, 17760, 17346, 17316, 17316, 16610, 15428, 
19950, 17424, 18720, 18029, 20724, 21574, 21632, 23584, 22059, 
17741, 19328, 21120, 18029, 20295, 21679, 19803, 16157, 20250, 
21870, 15052, 19782, 21528, 22275, 21285, 17787, 19635, 20768, 
19965, 19203, 21666, 23472, 22270, 21528, 14900, 14070, 15120, 
18306, 15707, 17810, 18630, 13552, 20691, 18375, 21376, 17732, 
16512, 16896, 22410, 22022, 27512, 18796, 26274, 19877, 24462, 
29722, 21823, 18834, 23856, 22491, 23055, 26568, 19096, 20944, 
21320, 21140, 20124, 17415, 15776, 20034, 20698, 19723, 19845, 
22139, 17272, 18720, 23616, 18144, 21312, 20150, 13560, 13560, 
15470, 19458, 18944, 19044, 16129, 18354, 23400, 20155, 18161, 
19881, 20002, 21060, 20436, 16637, 16968, 15656, 12870, 17767, 
17160, 17549, 15696, 18860, 22116, 14602, 20648, 20680, 17549, 
19184, 21756, 23718, 24742, 22848, 18511, 23230, 22987, 25480, 
26064, 18300, 18161, 18300, 17628, 18720, 24072, 23760, 21672, 
20060, 20280, 20482, 18620, 20160, 16764, 15990, 19328, 18125, 
18864, 12870, 13899, 16254, 16891, 14742, 16482, 16520, 14278, 
16074, 16610, 14848, 16002, 16675, 18850, 14964, 15738, 13254, 
18720, 17135, 21352, 17040, 14784, 20592, 19044, 20770, 18560, 
13800, 12996, 16256, 18476, 20572, 20445, 16576, 14319, 17408, 
16128, 16124, 16065, 14756, 12432, 15029, 21352, 17810, 19932, 
18495, 14720, 22914, 17063, 15645, 20735, 22960, 22925, 19845, 
15708, 21942, 27531, 20850, 22475, 22484, 22140, 15260, 21106, 
19817, 15360, 18480, 14586, 20433, 20838, 23881, 21679, 17612, 
19952, 17856, 23560, 19311, 19728, 18850, 18560, 20139, 15840, 
14824, 11210, 19728, 14784, 15065, 22638, 18216, 26219, 22797, 
37047, 20687, 22176, 19519, 18492, 13516, 18327, 15616, 16616, 
24928, 19840, 20838, 18460, 19176, 17825, 16950, 16786, 23254, 
20655, 19352, 22632, 19684, 15312, 16770, 17010, 16464, 17135, 
16568, 15494, 18327, 17136, 19221, 16166, 18944, 16541, 15622, 
13746, 19720, 16640, 16303, 17690, 15132, 14400, 14060, 14835, 
13320, 14322, 13860, 14796, 14946, 14790, 12600, 19460, 16940, 
15708, 18176, 16080, 18161, 14260, 18358, 14632, 16482, 19964, 
22218, 20139, 19040, 15368, 19880, 17286, 17388, 20424, 17400, 
16445, 18760, 16958, 13334, 10608, 5940, 23068, 20300, 20944, 
22046, 23256, 19418, 19456, 20328, 20536, 17343, 18161, 18070, 
22632, 21624, 22620, 23850, 21840, 20174, 18250, 20172, 15260, 
18120, 15038, 18445, 15048, 19456, 20328, 20536, 17343, 15594, 
14124, 12862, 16899, 17160, 15080, 12971, 16430, 13356, 12947, 
14430, 16764, 17136, 21965, 15729, 18000, 14304, 14214, 19470, 
17380, 14688, 17666, 16470, 17334, 14976, 14688, 26196, 25993, 
22704, 24639, 19352, 22188, 21924, 18271, 20240, 14874, 16320, 
13923, 26989, 24464, 24830, 20320, 21195, 20212, 17168, 18612, 
19454, 15510, 25277, 19872, 21033, 20808, 20824, 23250, 16002, 
18492, 18900, 20413, 17446, 20124, 24257, 19557, 23919, 25610, 
17490, 16157, 17545, 18370, 21357, 23058, 21888, 18796, 22388, 
18200, 19140, 21808, 19844, 19530, 21879, 22880, 19239, 22230, 
23166, 18871, 17480, 17199, 16874, 24206, 13224, 16905, 11322, 
17880, 19536, 13910, 15972, 17145, 16750, 16226, 15972, 14875, 
15128, 14756, 15972, 14875, 15128, 14756, 17112, 21312, 19126, 
17556, 20276, 17100, 18972, 15429, 17820, 17024, 16641, 17135, 
16714, 17324, 14125, 13080, 13189, 16000, 15006, 16740, 16092, 
17908, 14626, 14859, 15744, 15113, 17292, 13804, 14541, 15113, 
17292, 13804, 14541, 22308, 20736, 17958, 14976, 15410, 14762, 
14803, 18900, 17792, 19448, 16872, 17013, 14040, 14840, 16520, 
14224, 17908, 13462, 16874, 16675, 14715, 16263, 16440, 14317, 
17082, 13764, 17052, 18120, 17442, 12838, 2322, 14637, 14934, 
20066, 19520, 17880, 19304, 16422, 17526, 21420, 16254, 18090, 
14803, 14874, 16320, 13923, 19328, 20592, 17152, 21573, 19716, 
19800, 17792, 16896, 15128, 16899, 16510, 16375, 15488, 16974, 
15151, 17980, 15946, 20856, 21158, 17493, 19539, 19488, 21060, 
19840, 18352, 16974, 20034, 18997, 16758, 16046, 20034, 18997, 
16758, 16046, 18600, 19939, 18603, 15184, 20829, 19096, 19630, 
15012, 21384, 16750, 18029, 15410, 18724, 14580, 13420, 17664, 
16206, 1595, 16675, 17822, 16348, 19304, 17136, 17136, 15812, 
12648, 20961, 18544, 11748, 14763, 16758, 16046, 11748, 14763, 
11660, 17589, 19200, 19588, 20727, 10725, 13870, 17374, 12354, 
16214, 22101, 21080, 21525, 20125, 19434, 19800, 20125, 19434, 
19800, 21054, 19800, 24250, 20196, 21175, 24790, 18318, 24024, 
25004, 20083, 18144, 23976, 26660, 18688, 23520, 20304, 19832, 
19360, 18228, 18921, 20800, 21038, 19873, 22468, 17666, 13635
), session = c(34, 34, 34, 17, 17, 19, 19, 19, 21, 21, 21, 24, 
24, 24, 24, 25, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 33, 
33, 33, 33, 35, 35, 35, 35, 36, 36, 36, 36, 37, 37, 37, 18, 19, 
19, 19, 21, 21, 21, 23, 24, 24, 24, 24, 26, 26, 27, 27, 27, 34, 
34, 34, 36, 36, 38, 38, 38, 17, 17, 17, 18, 18, 18, 21, 21, 21, 
22, 22, 22, 23, 23, 23, 25, 25, 25, 25, 26, 26, 26, 27, 27, 27, 
37, 37, 37, 38, 38, 38, 38, 16, 16, 16, 18, 18, 18, 19, 19, 19, 
21, 21, 21, 23, 23, 23, 23, 25, 25, 26, 26, 26, 26, 32, 32, 32, 
32, 33, 33, 33, 34, 34, 34, 35, 35, 35, 35, 36, 36, 36, 36, 16, 
16, 16, 17, 17, 17, 21, 21, 21, 21, 23, 23, 25, 25, 25, 26, 26, 
32, 32, 32, 32, 33, 33, 33, 34, 34, 34, 34, 35, 35, 38, 38, 38, 
17, 17, 17, 18, 18, 21, 21, 23, 23, 23, 23, 25, 25, 25, 26, 26, 
26, 26, 32, 32, 32, 34, 34, 34, 35, 35, 35, 35, 16, 19, 19, 16, 
16, 16, 17, 17, 19, 19, 21, 21, 21, 21, 22, 22, 22, 27, 27, 27, 
27, 32, 32, 32, 32, 33, 33, 33, 33, 35, 35, 35, 35, 36, 36, 36, 
36, 16, 16, 16, 19, 19, 19, 20, 20, 21, 21, 21, 22, 22, 22, 23, 
23, 23, 24, 24, 24, 25, 25, 25, 26, 26, 26, 27, 27, 27, 27, 32, 
32, 33, 33, 33, 34, 34, 35, 35, 35, 19, 19, 19, 23, 23, 23, 24, 
24, 24, 24, 25, 25, 25, 27, 27, 27, 27, 33, 33, 33, 37, 37, 23, 
23, 23, 38, 38, 16, 16, 16, 17, 17, 17, 18, 18, 18, 21, 21, 21, 
22, 22, 23, 23, 23, 24, 16, 18, 19, 19, 19, 20, 20, 20, 22, 23, 
24, 16, 16, 16, 18, 18, 18, 19, 19, 19, 22, 22, 22, 23, 23, 23, 
23, 24, 24, 24, 24, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 
32, 32, 32, 32, 33, 33, 33, 33, 34, 34, 35, 35, 36, 36, 36, 36, 
38, 38, 38, 17, 17, 17, 18, 18, 18, 20, 20, 20, 21, 21, 21, 22, 
22, 22, 23, 23, 23, 23, 24, 24, 24, 24, 35, 35, 35, 36, 36, 36, 
36, 37, 37, 16, 17, 17, 17, 18, 18, 18, 19, 19, 21, 23, 23, 23, 
25, 25, 25, 25, 32, 32, 32, 33, 33, 33, 33, 34, 34, 34, 35, 35, 
35, 35, 36, 36, 36, 36, 38, 38, 38, 38, 16, 16, 16, 19, 19, 19, 
20, 20, 20, 21, 21, 21, 22, 22, 22, 23, 23, 23, 23, 25, 25, 35, 
35, 36, 36, 36, 36, 37, 37, 37, 23, 16, 16, 16, 19, 19, 19, 21, 
21, 21, 21, 31, 31, 31, 32, 32, 32, 32, 34, 34, 36, 36, 36, 16, 
16, 22, 22, 22, 22, 24, 24, 24, 24, 28, 28, 28, 34, 34, 34, 37, 
37, 37, 16, 16, 16, 21, 21, 21, 25, 25, 25, 25, 30, 30, 30, 30, 
31, 31, 31, 32, 32, 32, 36, 36, 36, 16, 16, 16, 20, 20, 34, 34, 
34, 35, 35, 17, 17, 17, 18, 21, 21, 21, 23, 23, 23, 23, 26, 26, 
26, 26, 27, 27, 29, 29, 29, 30, 30, 30, 30, 32, 32, 32, 33, 34, 
34, 34, 35, 35, 36, 36, 36, 17, 17, 17, 19, 19, 23, 23, 23, 26, 
26, 27, 27, 27, 29, 30, 30, 31, 31, 31, 32, 32, 32, 32, 34, 34, 
34, 35, 37, 37, 17, 17, 17, 21, 21, 21, 21, 23, 23, 23, 24, 24, 
24, 24, 25, 25, 25, 25, 28, 28, 28, 28, 29, 29, 29, 29, 30, 30, 
30, 30, 31, 31, 31, 31, 33, 33, 33, 35, 35, 17, 17, 17, 18, 24, 
24, 24, 24, 25, 25, 25, 25, 26, 26, 26, 26, 33, 33, 33, 36, 18, 
18, 18, 20, 20, 20, 26, 26, 27, 27, 27, 28, 28, 28, 28, 31, 31, 
31, 33, 33, 33, 37, 37, 37, 18, 18, 18, 21, 21, 21, 21, 22, 22, 
22, 26, 26, 26, 26, 27, 27, 27, 29, 29, 29, 29, 30, 30, 30, 30, 
31, 31, 31, 31, 33, 33, 18, 18, 18, 19, 19, 19, 22, 22, 22, 24, 
24, 24, 24, 25, 25, 25, 25, 27, 27, 27, 27, 29, 29, 29, 29, 30, 
30, 30, 30, 32, 32, 32, 32, 34, 34, 34, 35, 35, 37, 37, 37, 22, 
22, 22, 22, 24, 24, 24, 24, 25, 25, 25, 25, 28, 28, 28, 28, 35, 
37, 37, 37, 19, 22, 22, 24, 24, 24, 25, 25, 25, 26, 26, 28, 28, 
28, 32, 32, 32, 33, 35, 36, 20, 20, 20, 23, 23, 23, 27, 27, 29, 
29, 29, 36, 36, 20, 28), period = structure(c(1L, 2L, 4L, 1L, 
2L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
2L, 3L, 4L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, 4L, 4L, 1L, 2L, 
3L, 4L, 3L, 4L, 1L, 3L, 4L, 1L, 2L, 3L, 2L, 4L, 1L, 2L, 4L, 1L, 
2L, 3L, 1L, 2L, 3L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 4L, 1L, 2L, 4L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 2L, 
3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
4L, 1L, 3L, 1L, 3L, 4L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 
2L, 3L, 4L, 1L, 3L, 1L, 3L, 4L, 1L, 2L, 3L, 1L, 3L, 2L, 3L, 1L, 
2L, 3L, 4L, 1L, 2L, 4L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 2L, 3L, 
1L, 2L, 3L, 4L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 2L, 
3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 
1L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 4L, 1L, 3L, 4L, 1L, 2L, 2L, 3L, 
4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 2L, 
3L, 4L, 1L, 2L, 4L, 2L, 3L, 2L, 3L, 4L, 1L, 2L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 2L, 3L, 4L, 1L, 3L, 1L, 3L, 4L, 2L, 1L, 1L, 
1L, 2L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 
1L, 3L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 
3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 3L, 1L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 4L, 1L, 
2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 
3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 4L, 1L, 4L, 1L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
1L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 
3L, 4L, 1L, 2L, 1L, 2L, 3L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 4L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 3L, 1L, 2L, 3L, 2L, 3L, 1L, 2L, 3L, 3L, 1L, 
2L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 1L, 3L, 4L, 1L, 
2L, 3L, 4L, 1L, 2L, 4L, 3L, 1L, 2L, 3L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 1L, 3L, 4L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 
3L, 2L, 3L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
2L, 3L, 4L, 1L, 2L, 3L, 2L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 
2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 4L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 
1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 1L, 2L, 
3L, 1L, 2L, 3L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
2L, 3L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
2L, 3L, 4L, 1L, 2L, 3L, 4L, 2L, 1L, 2L, 3L, 3L, 3L, 4L, 2L, 3L, 
4L, 2L, 3L, 4L, 1L, 2L, 1L, 2L, 4L, 1L, 2L, 3L, 3L, 3L, 3L, 1L, 
2L, 3L, 1L, 3L, 4L, 2L, 3L, 2L, 3L, 4L, 1L, 2L, 1L, 1L), levels = c("p1", 
"p2", "p3", "p4"), class = "factor"), group = structure(c(2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L), levels = c("con", "int"), class = "factor"), id2 = c(1, 
1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 
7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 
11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 14, 
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 
14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 
16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 
16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 
16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 
18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 
18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 
18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 
19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 
19, 19, 19, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 
21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 
22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 
23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 
23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 
25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 
26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 27, 
27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 
27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 
27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 
29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 
29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 
30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 
30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 
31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 
31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 
32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 
33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 
33, 33, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 35, 
35), date = c(34, 34, 34, 17, 17, 19, 19, 19, 21, 21, 21, 24, 
24, 24, 24, 25, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 33, 
33, 33, 33, 35, 35, 35, 35, 36, 36, 36, 36, 37, 37, 37, 18, 19, 
19, 19, 21, 21, 21, 23, 24, 24, 24, 24, 26, 26, 27, 27, 27, 34, 
34, 34, 36, 36, 38, 38, 38, 17, 17, 17, 18, 18, 18, 21, 21, 21, 
22, 22, 22, 23, 23, 23, 25, 25, 25, 25, 26, 26, 26, 27, 27, 27, 
37, 37, 37, 38, 38, 38, 38, 16, 16, 16, 18, 18, 18, 19, 19, 19, 
21, 21, 21, 23, 23, 23, 23, 25, 25, 26, 26, 26, 26, 32, 32, 32, 
32, 33, 33, 33, 34, 34, 34, 35, 35, 35, 35, 36, 36, 36, 36, 16, 
16, 16, 17, 17, 17, 21, 21, 21, 21, 23, 23, 25, 25, 25, 26, 26, 
32, 32, 32, 32, 33, 33, 33, 34, 34, 34, 34, 35, 35, 38, 38, 38, 
17, 17, 17, 18, 18, 21, 21, 23, 23, 23, 23, 25, 25, 25, 26, 26, 
26, 26, 32, 32, 32, 34, 34, 34, 35, 35, 35, 35, 16, 19, 19, 16, 
16, 16, 17, 17, 19, 19, 21, 21, 21, 21, 22, 22, 22, 27, 27, 27, 
27, 32, 32, 32, 32, 33, 33, 33, 33, 35, 35, 35, 35, 36, 36, 36, 
36, 16, 16, 16, 19, 19, 19, 20, 20, 21, 21, 21, 22, 22, 22, 23, 
23, 23, 24, 24, 24, 25, 25, 25, 26, 26, 26, 27, 27, 27, 27, 32, 
32, 33, 33, 33, 34, 34, 35, 35, 35, 19, 19, 19, 23, 23, 23, 24, 
24, 24, 24, 25, 25, 25, 27, 27, 27, 27, 33, 33, 33, 37, 37, 23, 
23, 23, 38, 38, 16, 16, 16, 17, 17, 17, 18, 18, 18, 21, 21, 21, 
22, 22, 23, 23, 23, 24, 16, 18, 19, 19, 19, 20, 20, 20, 22, 23, 
24, 16, 16, 16, 18, 18, 18, 19, 19, 19, 22, 22, 22, 23, 23, 23, 
23, 24, 24, 24, 24, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 
32, 32, 32, 32, 33, 33, 33, 33, 34, 34, 35, 35, 36, 36, 36, 36, 
38, 38, 38, 17, 17, 17, 18, 18, 18, 20, 20, 20, 21, 21, 21, 22, 
22, 22, 23, 23, 23, 23, 24, 24, 24, 24, 35, 35, 35, 36, 36, 36, 
36, 37, 37, 16, 17, 17, 17, 18, 18, 18, 19, 19, 21, 23, 23, 23, 
25, 25, 25, 25, 32, 32, 32, 33, 33, 33, 33, 34, 34, 34, 35, 35, 
35, 35, 36, 36, 36, 36, 38, 38, 38, 38, 16, 16, 16, 19, 19, 19, 
20, 20, 20, 21, 21, 21, 22, 22, 22, 23, 23, 23, 23, 25, 25, 35, 
35, 36, 36, 36, 36, 37, 37, 37, 23, 32, 32, 32, 38, 38, 38, 42, 
42, 42, 42, 62, 62, 62, 64, 64, 64, 64, 68, 68, 72, 72, 72, 32, 
32, 44, 44, 44, 44, 48, 48, 48, 48, 56, 56, 56, 68, 68, 68, 74, 
74, 74, 32, 32, 32, 42, 42, 42, 50, 50, 50, 50, 60, 60, 60, 60, 
62, 62, 62, 64, 64, 64, 72, 72, 72, 32, 32, 32, 40, 40, 68, 68, 
68, 70, 70, 34, 34, 34, 36, 42, 42, 42, 46, 46, 46, 46, 52, 52, 
52, 52, 54, 54, 58, 58, 58, 60, 60, 60, 60, 64, 64, 64, 66, 68, 
68, 68, 70, 70, 72, 72, 72, 34, 34, 34, 38, 38, 46, 46, 46, 52, 
52, 54, 54, 54, 58, 60, 60, 62, 62, 62, 64, 64, 64, 64, 68, 68, 
68, 70, 74, 74, 34, 34, 34, 42, 42, 42, 42, 46, 46, 46, 48, 48, 
48, 48, 50, 50, 50, 50, 56, 56, 56, 56, 58, 58, 58, 58, 60, 60, 
60, 60, 62, 62, 62, 62, 66, 66, 66, 70, 70, 34, 34, 34, 36, 48, 
48, 48, 48, 50, 50, 50, 50, 52, 52, 52, 52, 66, 66, 66, 72, 36, 
36, 36, 40, 40, 40, 52, 52, 54, 54, 54, 56, 56, 56, 56, 62, 62, 
62, 66, 66, 66, 74, 74, 74, 36, 36, 36, 42, 42, 42, 42, 44, 44, 
44, 52, 52, 52, 52, 54, 54, 54, 58, 58, 58, 58, 60, 60, 60, 60, 
62, 62, 62, 62, 66, 66, 36, 36, 36, 38, 38, 38, 44, 44, 44, 48, 
48, 48, 48, 50, 50, 50, 50, 54, 54, 54, 54, 58, 58, 58, 58, 60, 
60, 60, 60, 64, 64, 64, 64, 68, 68, 68, 70, 70, 74, 74, 74, 44, 
44, 44, 44, 48, 48, 48, 48, 50, 50, 50, 50, 56, 56, 56, 56, 70, 
74, 74, 74, 38, 44, 44, 48, 48, 48, 50, 50, 50, 52, 52, 56, 56, 
56, 64, 64, 64, 66, 70, 72, 40, 40, 40, 46, 46, 46, 54, 54, 58, 
58, 58, 72, 72, 40, 56)), row.names = c(NA, -834L), class = c("tbl_df", 
"tbl", "data.frame"))

1条答案

按热度按时间

zi8p0yeb1#

此答案使用marginaleffects的开发版本（0.9.0.9043），您可以按照以下说明安装该版本：https://vincentarelbundock.github.io/marginaleffects/
请注意，额外的lme4相关参数必须提供给predictions()函数，而不是像第二个示例中那样提供给datagrid()函数。
另外，我强烈建议您避免使用include_random，而是使用lme4建模包本身提供的默认参数（通过predict.merMod）。re.form和allow.new.levels。

library(lme4)
library(lmerTest)
library(marginaleffects)
library(dplyr)

dat_long$group <- as.factor(dat_long$group)
dat_long$period <- as.factor(dat_long$period)
dat_long <- dat_long %>% mutate(group2 = group)

m222 <- lmer(money ~ session + period + group2 + (1 | id2) + (1 | session / date / period), data = dat_long ) 

comparisons(
    m222,
    variables = "period",
    re.form = NA,
    newdata = datagrid(period = c("p1", "p2", "p3", "p4")))
# 
#    Term Contrast Estimate Std. Error      z   Pr(>|z|)   2.5 % 97.5 % session group2 id2     date
#  period  p2 - p1   -361.6      260.0 -1.391  0.1643688  -871.3  148.1      23    int  16 37.90168
#  period  p2 - p1   -361.6      260.0 -1.391  0.1643688  -871.3  148.1      23    int  16 37.90168
#  period  p2 - p1   -361.6      260.0 -1.391  0.1643688  -871.3  148.1      23    int  16 37.90168
#  period  p2 - p1   -361.6      260.0 -1.391  0.1643688  -871.3  148.1      23    int  16 37.90168
#  period  p3 - p1   -745.6      260.0 -2.868  0.0041366 -1255.3 -236.0      23    int  16 37.90168
#  period  p3 - p1   -745.6      260.0 -2.868  0.0041366 -1255.3 -236.0      23    int  16 37.90168
#  period  p3 - p1   -745.6      260.0 -2.868  0.0041366 -1255.3 -236.0      23    int  16 37.90168
#  period  p3 - p1   -745.6      260.0 -2.868  0.0041366 -1255.3 -236.0      23    int  16 37.90168
#  period  p4 - p1  -1371.6      318.4 -4.308 1.6492e-05 -1995.6 -747.5      23    int  16 37.90168
#  period  p4 - p1  -1371.6      318.4 -4.308 1.6492e-05 -1995.6 -747.5      23    int  16 37.90168
#  period  p4 - p1  -1371.6      318.4 -4.308 1.6492e-05 -1995.6 -747.5      23    int  16 37.90168
#  period  p4 - p1  -1371.6      318.4 -4.308 1.6492e-05 -1995.6 -747.5      23    int  16 37.90168
# 
# Prediction type:  response 
# Columns: rowid, type, term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo, money, session, group2, id2, date, period

predictions(
    m222,
    newdata = datagrid(
        id2 = NA,
        session = seq(from = 16, to = 38, by = 1)),
    re.form = NA,
    allow.new.levels = TRUE)
# 
#  Estimate Std. Error     z   Pr(>|z|) 2.5 % 97.5 % period group2     date id2 session
#     19654      656.1 29.96 < 2.22e-16 18368  20940     p1    int 37.90168  NA      16
#     19649      647.4 30.35 < 2.22e-16 18380  20917     p1    int 37.90168  NA      17
#     19643      639.5 30.72 < 2.22e-16 18390  20896     p1    int 37.90168  NA      18
#     19637      632.5 31.05 < 2.22e-16 18398  20877     p1    int 37.90168  NA      19
#     19632      626.2 31.35 < 2.22e-16 18404  20859     p1    int 37.90168  NA      20
#     19626      620.9 31.61 < 2.22e-16 18409  20843     p1    int 37.90168  NA      21
#     19621      616.5 31.83 < 2.22e-16 18412  20829     p1    int 37.90168  NA      22
#     19615      613.0 32.00 < 2.22e-16 18414  20817     p1    int 37.90168  NA      23
#     19610      610.5 32.12 < 2.22e-16 18413  20806     p1    int 37.90168  NA      24
#     19604      608.9 32.20 < 2.22e-16 18411  20798     p1    int 37.90168  NA      25
#     19599      608.2 32.22 < 2.22e-16 18406  20791     p1    int 37.90168  NA      26
#     19593      608.6 32.19 < 2.22e-16 18400  20786     p1    int 37.90168  NA      27
#     19587      609.9 32.12 < 2.22e-16 18392  20783     p1    int 37.90168  NA      28
#     19582      612.1 31.99 < 2.22e-16 18382  20782     p1    int 37.90168  NA      29
#     19576      615.3 31.82 < 2.22e-16 18370  20782     p1    int 37.90168  NA      30
#     19571      619.4 31.60 < 2.22e-16 18357  20785     p1    int 37.90168  NA      31
#     19565      624.4 31.33 < 2.22e-16 18341  20789     p1    int 37.90168  NA      32
#     19560      630.4 31.03 < 2.22e-16 18324  20795     p1    int 37.90168  NA      33
#     19554      637.1 30.69 < 2.22e-16 18305  20803     p1    int 37.90168  NA      34
#     19549      644.8 30.32 < 2.22e-16 18285  20812     p1    int 37.90168  NA      35
#     19543      653.2 29.92 < 2.22e-16 18263  20823     p1    int 37.90168  NA      36
#     19538      662.4 29.49 < 2.22e-16 18239  20836     p1    int 37.90168  NA      37
#     19532      672.4 29.05 < 2.22e-16 18214  20850     p1    int 37.90168  NA      38
# 
# Prediction type:  response 
# Columns: rowid, type, estimate, std.error, statistic, p.value, conf.low, conf.high, money, period, group2, date, id2, session

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R语言边际效应-获取对比度并绘制预测图

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R语言 边际效应-获取对比度并绘制预测图

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R语言边际效应-获取对比度并绘制预测图