Marginaleffects – obtaining contrasts and plotting predictions

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英文:

Marginaleffects - obtaining contrasts and plotting predictions

问题

  1. 使用marginaleffects，我试图获取关于"period"的对比。
  2. 尝试通过"period"来可视化预测。
  3. 尝试通过"session"来可视化预测。

但在所有这些尝试中都遇到了问题。任何帮助将不胜感激。

在错误消息中提到：

错误：无法使用这个模型计算预测值。您可以尝试向newdata参数提供不同的数据集。如果这不起作用，您可以在GitHub的问题跟踪器上提交报告：https://github.com/vincentarelbundock/marginaleffects/issues

错误：无法使用这个模型计算预测值。您可以尝试向newdata参数提供不同的数据集。如果这不起作用，您可以在GitHub的问题跟踪器上提交报告：https://github.com/vincentarelbundock/marginaleffects/issues

此外，还出现了以下错误：无效的分组因子规范，id2。此外，警告消息提示：一些变量名称在模型数据中缺失：include_random。

df数据如下：（以下为数据示例）

英文:

Using marginaleffects, I was trying to

  1. obtain contrasts by "period"
  2. visualize the predictions by "period"
  3. visualize the predictions by "session"

And failed in all! Any help is appreciated.

Df in the end

  1. library(lme4)
  2. library(lmerTest)
  3. library(marginaleffects)
  4. library(dplyr)
  6. import dat_long
  7. dat_long$group <- as.factor(dat_long$group)
  8. dat_long$period <- as.factor(dat_long$period)
  11. dat_long <- dat_long %>% 
  12.   mutate(group2 = group)
  15. m222 <- lmer(money ~ session + period + group2 + (1 | id2) + (1 | session / date / period), data = dat_long ) 
  16. summary(m222)
  18. contrasts_periods <- comparisons(
  19.   m222,
  20.   variables = "period",
  21.   include_random = FALSE,
  22.   newdata = datagrid(
  23.     period = c("p1", "p2", "p3", "p4")
  24.   )
  25. )
  26.  
  28. #2
  30.     pred_period <- predictions(  m222,
  31.                           newdata = datagrid(id2 = NA,
  32.                                              period = c("p1", "p2", "p3", "p4"),
  33.                                              include_random = FALSE))
  38.     ggplot(pred, aes(x = period, y = predicted,
  39.                      ymin = conf.low, ymax = conf.high))
  41.   
  43. #3
  46. pred_session <- predictions(  m222,
  47.                              newdata = datagrid(id2 = NA,
  48.                                                 session = seq(from = 16, to = 38, by = 1),
  49.                                                 include_random = FALSE))

error codes:

> Error: Unable to compute predicted values with this model. You can
> try to supply a different dataset to the newdata argument. If this
> does not work, you can file a report on the Github Issue Tracker:
> https://github.com/vincentarelbundock/marginaleffects/issues

> Error: Unable to compute predicted values with this model. You can try
> to supply a different dataset to the newdata argument. If this does
> not work, you can file a report on the Github Issue Tracker:
> https://github.com/vincentarelbundock/marginaleffects/issues
>
> This error was also raised: Invalid grouping factor specification,
> id2 In addition: Warning message: Some of the variable names are
> missing from the model data: include_random

df below:

  1. dat_long  <- structure(list(money = c(22625, 23349, 18189, 16302, 12874, 17343, 
  2. 15912, 15300, 18762, 23506, 18290, 10296, 13172, 15288, 12462, 
  3. 16380, 14352, 15052, 14497, 16241, 14832, 14304, 15120, 3745, 
  4. 15012, 13916, 13056, 12432, 12441, 15762, 10660, 18150, 15496, 
  5. 16905, 14872, 16166, 15892, 18755, 16241, 16874, 15836, 15225, 
  6. 32190, 30450, 25200, 19840, 31800, 29892, 10416, 26520, 29029, 
  7. 28623, 26544, 16988, 22801, 19317, 30694, 20447, 26030, 22378, 
  8. 27267, 21760, 26334, 26896, 32085, 28914, 26892, 18683, 19468, 
  9. 16920, 17640, 20829, 17920, 17424, 20538, 21760, 14985, 13407, 
  10. 13624, 15470, 21252, 15129, 21336, 17760, 22908, 16940, 15860, 
  11. 17732, 18048, 16002, 18480, 20328, 22848, 19630, 17030, 24220, 
  12. 16074, 20234, 20413, 20448, 23715, 22010, 24000, 25245, 23088, 
  13. 16445, 22200, 24786, 20100, 17766, 20022, 22194, 16284, 23560, 
  14. 16638, 23345, 26788, 21462, 16786, 16362, 22176, 21600, 21744, 
  15. 21432, 19026, 22330, 20049, 19968, 18876, 20850, 19126, 18788, 
  16. 19650, 24320, 17100, 22785, 18875, 23520, 21252, 17766, 20304, 
  17. 19170, 17780, 19296, 15855, 16244, 19875, 18476, 16284, 17780, 
  18. 14279, 20562, 17556, 17568, 20700, 19750, 22401, 19625, 20264, 
  19. 18176, 19272, 24180, 21855, 22490, 22560, 19599, 20550, 17856, 
  20. 20670, 18768, 20385, 17856, 16891, 18081, 18755, 18796, 21450, 
  21. 18576, 16263, 18460, 16616, 16992, 17250, 18995, 21021, 20368, 
  22. 17536, 18626, 11742, 15872, 19684, 17250, 15616, 17176, 17653, 
  23. 17690, 19890, 18054, 17760, 17346, 17316, 17316, 16610, 15428, 
  24. 19950, 17424, 18720, 18029, 20724, 21574, 21632, 23584, 22059, 
  25. 17741, 19328, 21120, 18029, 20295, 21679, 19803, 16157, 20250, 
  26. 21870, 15052, 19782, 21528, 22275, 21285, 17787, 19635, 20768, 
  27. 19965, 19203, 21666, 23472, 22270, 21528, 14900, 14070, 15120, 
  28. 18306, 15707, 17810, 18630, 13552, 20691, 18375, 21376, 17732, 
  29. 16512, 16896, 22410, 22022, 27512, 18796, 26274, 19877, 24462, 
  30. 29722, 21823, 18834, 23856, 22491, 23055, 26568, 19096, 20944, 
  31. 21320, 21140, 20124, 17415, 15776, 20034, 20698, 19723, 19845, 
  32. 22139, 17272, 18720, 23616, 18144, 21312, 20150, 13560, 13560, 
  33. 15470, 19458, 18944, 19044, 16129, 18354, 23400, 20155, 18161, 
  34. 19881, 20002, 21060, 20436, 16637, 16968, 15656, 12870, 17767, 
  35. 17160, 17549, 15696, 18860, 22116, 14602, 20648, 20680, 17549, 
  36. 19184, 21756, 23718, 24742, 22848, 18511, 23230, 22987, 25480, 
  37. 26064, 18300, 18161, 18300, 17628, 18720, 24072, 23760, 21672, 
  38. 20060, 20280, 20482, 18620, 20160, 16764, 15990, 19328, 18125, 
  39. 18864, 12870, 13899, 16254, 16891, 14742, 16482, 16520, 14278, 
  40. 16074, 16610, 14848, 16002, 16675, 18850, 14964, 15738, 13254, 
  41. 18720, 17135, 21352, 17040, 14784, 20592, 19044, 20770, 18560, 
  42. 13800, 12996, 16256, 18476, 20572, 20445, 16576, 14319, 17408, 
  43. 16128, 16124, 16065, 14756, 12432, 15029, 21352, 17810, 19932, 
  44. 18495, 14720, 22914, 17063, 15645, 20735, 22960, 22925, 19845, 
  45. 15708, 21942, 27531, 20850, 22475, 22484, 22140, 15260, 21106, 
  46. 19817, 15360, 18480, 14586, 20433, 20838, 23881, 21679, 17612, 
  47. 19952, 17856, 23560, 19311, 19728, 18850, 18560, 20139, 15840, 
  48. 14824, 11210, 19728, 14784, 15065, 22638, 18216, 26219, 22797, 
  49. 37047, 20687, 22176, 19519, 18492, 13516, 18327, 15616, 16616, 
  50. 24928, 19840, 20838, 18460, 19176, 17825, 16950, 16786, 23254, 
  51. 20655, 19352, 22632, 19684, 15312, 16770, 17010, 16464, 17135, 
  52. 16568, 15494, 18327, 17136, 19221, 16166, 18944, 16541, 15622, 
  53. 13746, 19720, 16640, 16303, 17690, 15132, 14400, 14060, 14835, 
  54. 13320, 14322, 13860, 14796, 14946, 14790, 12600, 19460, 16940, 
  55. 15708, 18176, 16080, 18161, 14260, 18358, 14632, 16482, 19964, 
  56. 22218, 20139, 19040, 15368, 19880, 17286, 17388, 20424, 17400, 
  57. 16445, 18760, 16958, 13334, 10608, 5940, 23068, 20300, 20944, 
  58. 22046, 23256, 19418, 19456, 20328, 20536, 17343, 18161, 18070, 
  59. 22632, 21624, 22620, 23850, 21840, 20174, 18250, 20172, 15260, 
  60. 18120, 15038, 18445, 15048, 19456, 20328, 20536, 17343, 15594, 
  61. 14124, 12862, 16899, 17160, 15080, 12971, 16430, 13356, 12947, 
  62. 14430, 16764, 17136, 21965, 15729, 18000, 14304, 14214, 19470, 
  63. 17380, 14688, 17666, 16470, 17334, 14976, 14688, 26196, 25993, 
  64. 22704, 24639, 19352, 22188, 21924, 18271, 20240, 14874, 16320, 
  65. 13923, 26989, 24464, 24830, 20320, 21195, 20212, 17168, 18612, 
  66. 19454, 15510, 25277, 19872, 21033, 20808, 20824, 23250, 16002, 
  67. 18492, 18900, 20413, 17446, 20124, 24257, 19557, 23919, 25610, 
  68. 17490, 16157, 17545, 18370, 21357, 23058, 21888, 18796, 22388, 
  69. 18200, 19140, 21808, 19844, 19530, 21879, 22880, 19239, 22230, 
  70. 23166, 18871, 17480, 17199, 16874, 24206, 13224, 16905, 11322, 
  71. 17880, 19536, 13910, 15972, 17145, 16750, 16226, 15972, 14875, 
  72. 15128, 14756, 15972, 14875, 15128, 14756, 17112, 21312, 19126, 
  73. 17556, 20276, 17100, 18972, 15429, 17820, 17024, 16641, 17135, 
  74. 16714, 17324, 14125, 13080, 13189, 16000, 15006, 16740, 16092, 
  75. 17908, 14626, 14859, 15744, 15113, 17292, 13804, 14541, 15113, 
  76. 17292, 13804, 14541, 22308, 20736, 17958, 14976, 15410, 14762, 
  77. 14803, 18900, 17792, 19448, 16872, 17013, 14040, 14840, 16520, 
  78. 14224, 17908, 13462, 16874, 16675, 14715, 16263, 16440, 14317, 
  79. 17082, 13764, 17052, 18120, 17442, 12838, 2322, 14637, 14934, 
  80. 20066, 19520, 17880, 19304, 16422, 17526, 21420, 16254, 18090, 
  81. 14803, 14874, 16320, 13923, 19328, 20592, 17152, 21573, 19716, 
  82. 19800, 17792, 16896, 15128, 16899, 16510, 16375, 15488, 16974, 
  83. 15151, 17980, 15946, 20856, 21158, 17493, 19539, 19488, 21060, 
  84. 19840, 18352, 16974, 20034, 18997, 16758, 16046, 20034, 18997, 
  85. 16758, 16046, 18600, 19939, 18603, 15184, 20829, 19096, 19630, 
  86. 15012, 21384, 16750, 18029, 15410, 18724, 14580, 13420, 17664, 
  87. 16206, 1595, 16675, 17822, 16348, 19304, 17136, 17136, 15812, 
  88. 12648, 20961, 18544, 11748, 14763, 16758, 16046, 11748, 14763, 
  89. 11660, 17589, 19200, 19588, 20727, 10725, 13870, 17374, 12354, 
  90. 16214, 22101, 21080, 21525, 20125, 19434, 19800, 20125, 19434, 
  91. 19800, 21054, 19800, 24250, 20196, 21175, 24790, 18318, 24024, 
  92. 25004, 20083, 18144, 23976, 26660, 18688, 23520, 20304, 19832, 
  93. 19360, 18228, 18921, 20800, 21038, 19873, 22468, 17666, 13635
  94. ), session = c(34, 34, 34, 17, 17, 19, 19, 19, 21, 21, 21, 24, 
  95. 24, 24, 24, 25, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 33, 
  96. 33, 33, 33, 35, 35, 35, 35, 36, 36, 36, 36, 37, 37, 37, 18, 19, 
  97. 19, 19, 21, 21, 21, 23, 24, 24, 24, 24, 26, 26, 27, 27, 27, 34, 
  98. 34, 34, 36, 36, 38, 38, 38, 17, 17, 17, 18, 18, 18, 21, 21, 21, 
  99. 22, 22, 22, 23, 23, 23, 25, 25, 25, 25, 26, 26, 26, 27, 27, 27, 
  100. 37, 37, 37, 38, 38, 38, 38, 16, 16, 16, 18, 18, 18, 19, 19, 19, 
  101. 21, 21, 21, 23, 23, 23, 23, 25, 25, 26, 26, 26, 26, 32, 32, 32, 
  102. 32, 33, 33, 33, 34, 34, 34, 35, 35, 35, 35, 36, 36, 36, 36, 16, 
  103. 16, 16, 17, 17, 17, 21, 21, 21, 21, 23, 23, 25, 25, 25, 26, 26, 
  104. 32, 32, 32, 32, 33, 33, 33, 34, 34, 34, 34, 35, 35, 38, 38, 38, 
  105. 17, 17, 17, 18, 18, 21, 21, 23, 23, 23, 23, 25, 25, 25, 26, 26, 
  106. 26, 26, 32, 32, 32, 34, 34, 34, 35, 35, 35, 35, 16, 19, 19, 16, 
  107. 16, 16, 17, 17, 19, 19, 21, 21, 21, 21, 22, 22, 22, 27, 27, 27, 
  108. 27, 32, 32, 32, 32, 33, 33, 33, 33, 35, 35, 35, 35, 36, 36, 36, 
  109. 36, 16, 16, 16, 19, 19, 19, 20, 20, 21, 21, 21, 22, 22, 22, 23, 
  110. 23, 23, 24, 24, 24, 25, 25, 25, 26, 26, 26, 27, 27, 27, 27, 32, 
  111. 32, 33, 33, 33, 34, 34, 35, 35, 35, 19, 19, 19, 23, 23, 23, 24, 
  112. 24, 24, 24, 25, 25, 25, 27, 27, 27, 27, 33, 33, 33, 37, 37, 23, 
  113. 23, 23, 38, 38, 16, 16, 16, 17, 17, 17, 18, 18, 18, 21, 21, 21, 
  114. 22, 22, 23, 23, 23, 24, 16, 18, 19, 19, 19, 20, 20, 20, 22, 23, 
  115. 24, 16, 16, 16, 18, 18, 18, 19, 19, 19, 22, 22, 22, 23, 23, 23, 
  116. 23, 24, 24, 24, 24, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 
  117. 32, 32, 32, 32, 33, 33, 33, 33, 34, 34, 35, 35, 36, 36, 36, 36, 
  118. 38, 38, 38, 17, 17, 17, 18, 18, 18, 20, 20, 20, 21, 21, 21, 22, 
  119. 22, 22, 23, 23, 23, 23, 24, 24, 24, 24, 35, 35, 35, 36, 36, 36, 
  120. 36, 37, 37, 16, 17, 17, 17, 18, 18, 18, 19, 19, 21, 23, 23, 23, 
  121. 25, 25, 25, 25, 32, 32, 32, 33, 33, 33, 33, 34, 34, 34, 35, 35, 
  122. 35, 35, 36, 36, 36, 36, 38, 38, 38, 38, 16, 16, 16, 19, 19, 19, 
  123. 20, 20, 20, 21, 21, 21, 22, 22, 22, 23, 23, 23, 23, 25, 25, 35, 
  124. 35, 36, 36, 36, 36, 37, 37, 37, 23, 16, 16, 16, 19, 19, 19, 21, 
  125. 21, 21, 21, 31, 31, 31, 32, 32, 32, 32, 34, 34, 36, 36, 36, 16, 
  126. 16, 22, 22, 22, 22, 24, 24, 24, 24, 28, 28, 28, 34, 34, 34, 37, 
  127. 37, 37, 16, 16, 16, 21, 21, 21, 25, 25, 25, 25, 30, 30, 30, 30, 
  128. 31, 31, 31, 32, 32, 32, 36, 36, 36, 16, 16, 16, 20, 20, 34, 34, 
  129. 34, 35, 35, 17, 17, 17, 18, 21, 21, 21, 23, 23, 23, 23, 26, 26, 
  130. 26, 26, 27, 27, 29, 29, 29, 30, 30, 30, 30, 32, 32, 32, 33, 34, 
  131. 34, 34, 35, 35, 36, 36, 36, 17, 17, 17, 19, 19, 23, 23, 23, 26, 
  132. 26, 27, 27, 27, 29, 30, 30, 31, 31, 31, 32, 32, 32, 32, 34, 34, 
  133. 34, 35, 37, 37, 17, 17, 17, 21, 21, 21, 21, 23, 23, 23, 24, 24, 
  134. 24, 24, 25, 25, 25, 25, 28, 28, 28, 28, 29, 29, 29, 29, 30, 30, 
  135. 30, 30, 31, 31, 31, 31, 33, 33, 33, 35, 35, 17, 17, 17, 18, 24, 
  136. 24, 24, 24, 25, 25, 25, 25, 26, 26, 26, 26, 33, 33, 33, 36, 18, 
  137. 18, 18, 20, 20, 20, 26, 26, 27, 27, 27, 28, 28, 28, 28, 31, 31, 
  138. 31, 33, 33, 33, 37, 37, 37, 18, 18, 18, 21, 21, 21, 21, 22, 22, 
  139. 22, 26, 26, 26, 26, 27, 27, 27, 29, 29, 29, 29, 30, 30, 30, 30, 
  140. 31, 31, 31, 31, 33, 33, 18, 18, 18, 19, 19, 19, 22, 22, 22, 24, 
  141. 24, 24, 24, 25, 25, 25, 25, 27, 27, 27, 27, 29, 29, 29, 29, 30, 
  142. 30, 30, 30, 32, 32, 32, 32, 34, 34, 34, 35, 35, 37, 37, 37, 22, 
  143. 22, 22, 22, 24, 24, 24, 24, 25, 25, 25, 25, 28, 28, 28, 28, 35, 
  144. 37, 37, 37, 19, 22, 22, 24, 24, 24, 25, 25, 25, 26, 26, 28, 28, 
  145. 28, 32, 32, 32, 33, 35, 36, 20, 20, 20, 23, 23, 23, 27, 27, 29, 
  146. 29, 29, 36, 36, 20, 28), period = structure(c(1L, 2L, 4L, 1L, 
  147. 2L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
  148. 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
  149. 2L, 3L, 4L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, 4L, 4L, 1L, 2L, 
  150. 3L, 4L, 3L, 4L, 1L, 3L, 4L, 1L, 2L, 3L, 2L, 4L, 1L, 2L, 4L, 1L, 
  151. 2L, 3L, 1L, 2L, 3L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
  152. 3L, 4L, 1L, 2L, 4L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 
  153. 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 2L, 
  154. 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 
  155. 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
  156. 4L, 1L, 3L, 1L, 3L, 4L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 
  157. 2L, 3L, 4L, 1L, 3L, 1L, 3L, 4L, 1L, 2L, 3L, 1L, 3L, 2L, 3L, 1L, 
  158. 2L, 3L, 4L, 1L, 2L, 4L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 2L, 3L, 
  159. 1L, 2L, 3L, 4L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 2L, 
  160. 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
  161. 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 2L, 
  162. 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 
  163. 1L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 4L, 1L, 3L, 4L, 1L, 2L, 2L, 3L, 
  164. 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 2L, 
  165. 3L, 4L, 1L, 2L, 4L, 2L, 3L, 2L, 3L, 4L, 1L, 2L, 1L, 2L, 3L, 1L, 
  166. 2L, 3L, 1L, 2L, 3L, 2L, 3L, 4L, 1L, 3L, 1L, 3L, 4L, 2L, 1L, 1L, 
  167. 1L, 2L, 3L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
  168. 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
  169. 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 
  170. 1L, 3L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
  171. 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 
  172. 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 3L, 1L, 1L, 2L, 3L, 1L, 
  173. 2L, 3L, 1L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 4L, 1L, 
  174. 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 
  175. 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
  176. 3L, 1L, 2L, 3L, 4L, 1L, 4L, 1L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
  177. 1L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 
  178. 3L, 4L, 1L, 2L, 1L, 2L, 3L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
  179. 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
  180. 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 4L, 1L, 2L, 3L, 1L, 2L, 
  181. 3L, 1L, 2L, 3L, 1L, 3L, 1L, 2L, 3L, 2L, 3L, 1L, 2L, 3L, 3L, 1L, 
  182. 2L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 1L, 3L, 4L, 1L, 
  183. 2L, 3L, 4L, 1L, 2L, 4L, 3L, 1L, 2L, 3L, 2L, 3L, 1L, 2L, 3L, 1L, 
  184. 2L, 3L, 1L, 2L, 1L, 3L, 4L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, 3L, 1L, 
  185. 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 1L, 
  186. 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 
  187. 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 
  188. 3L, 2L, 3L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
  189. 2L, 3L, 4L, 1L, 2L, 3L, 2L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 
  190. 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
  191. 2L, 3L, 1L, 2L, 3L, 4L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 
  192. 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 1L, 2L, 
  193. 3L, 1L, 2L, 3L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
  194. 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
  195. 2L, 3L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
  196. 2L, 3L, 4L, 1L, 2L, 3L, 4L, 2L, 1L, 2L, 3L, 3L, 3L, 4L, 2L, 3L, 
  197. 4L, 2L, 3L, 4L, 1L, 2L, 1L, 2L, 4L, 1L, 2L, 3L, 3L, 3L, 3L, 1L, 
  198. 2L, 3L, 1L, 3L, 4L, 2L, 3L, 2L, 3L, 4L, 1L, 2L, 1L, 1L), levels = c("p1", 
  199. "p2", "p3", "p4"), class = "factor"), group = structure(c(2L, 
  200. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  201. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  202. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  203. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  204. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  205. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  206. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  207. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  208. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  209. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  210. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  211. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  212. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  213. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  214. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  215. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  216. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  217. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  218. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  219. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  220. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  221. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  222. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  223. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  224. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  225. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  226. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  227. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  228. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  229. 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  230. 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  231. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  232. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  233. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  234. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  235. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  236. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  237. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  238. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  239. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  240. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  241. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  242. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  243. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  244. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  245. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  246. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  247. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  248. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  249. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  250. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  251. 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  252. 1L), levels = c("con", "int"), class = "factor"), id2 = c(1, 
  253. 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
  254. 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 
  255. 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
  256. 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
  257. 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 
  258. 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
  259. 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 
  260. 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 
  261. 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 
  262. 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 10, 10, 10, 10, 10, 10, 
  263. 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
  264. 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 
  265. 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 
  266. 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 
  267. 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 
  268. 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 14, 
  269. 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 
  270. 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 
  271. 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 
  272. 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 
  273. 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 
  274. 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
  275. 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 
  276. 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 
  277. 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 
  278. 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 
  279. 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 
  280. 19, 19, 19, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 
  281. 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 
  282. 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 
  283. 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 
  284. 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 
  285. 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
  286. 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
  287. 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 
  288. 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 27, 
  289. 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 
  290. 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 
  291. 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 
  292. 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 
  293. 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 
  294. 29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 
  295. 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 
  296. 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 
  297. 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 
  298. 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 
  299. 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 
  300. 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 
  301. 33, 33, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 35, 
  302. 35), date = c(34, 34, 34, 17, 17, 19, 19, 19, 21, 21, 21, 24, 
  303. 24, 24, 24, 25, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 33, 
  304. 33, 33, 33, 35, 35, 35, 35, 36, 36, 36, 36, 37, 37, 37, 18, 19, 
  305. 19, 19, 21, 21, 21, 23, 24, 24, 24, 24, 26, 26, 27, 27, 27, 34, 
  306. 34, 34, 36, 36, 38, 38, 38, 17, 17, 17, 18, 18, 18, 21, 21, 21, 
  307. 22, 22, 22, 23, 23, 23, 25, 25, 25, 25, 26, 26, 26, 27, 27, 27, 
  308. 37, 37, 37, 38, 38, 38, 38, 16, 16, 16, 18, 18, 18, 19, 19, 19, 
  309. 21, 21, 21, 23, 23, 23, 23, 25, 25, 26, 26, 26, 26, 32, 32, 32, 
  310. 32, 33, 33, 33, 34, 34, 34, 35, 35, 35, 35, 36, 36, 36, 36, 16, 
  311. 16, 16, 17, 17, 17, 21, 21, 21, 21, 23, 23, 25, 25, 25, 26, 26, 
  312. 32, 32, 32, 32, 33, 33, 33, 34, 34, 34, 34, 35, 35, 38, 38, 38, 
  313. 17, 17, 17, 18, 18, 21, 21, 23, 23, 23, 23, 25, 25, 25, 26, 26, 
  314. 26, 26, 32, 32, 32, 34, 34, 34, 35, 35, 35, 35, 16, 19, 19, 16, 
  315. 16, 16, 17, 17, 19, 19, 21, 21, 21, 21, 22, 22, 22, 27, 27, 27, 
  316. 27, 32, 32, 32, 32, 33, 33, 33, 33, 35, 35, 35, 35, 36, 36, 36, 
  317. 36, 16, 16, 16, 19, 19, 19, 20, 20, 21, 21, 21, 22, 22, 22, 23, 
  318. 23, 23, 24, 24, 24, 25, 25, 25, 26, 26, 26, 27, 27, 27, 27, 32, 
  319. 32, 33, 33, 33, 34, 34, 35, 35, 35, 19, 19, 19, 23, 23, 23, 24, 
  320. 24, 24, 24, 25, 25, 25, 27, 27, 27, 27, 33, 33, 33, 37, 37, 23, 
  321. 23, 23, 38, 38, 16, 16, 16, 17, 17, 17, 18, 18, 18, 21, 21, 21, 
  322. 22, 22, 23, 23, 23, 24, 16, 18, 19, 19, 19, 20, 20, 20, 22, 23, 
  323. 24, 16, 16, 16, 18, 18, 18, 19, 19, 19, 22, 22, 22, 23, 23, 23, 
  324. 23, 24, 24, 24, 24, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 
  325. 32, 32, 32, 32, 33, 33, 33, 33, 34, 34, 35, 35, 36, 36, 36, 36, 
  326. 38, 38, 38, 17, 17, 17, 18, 18, 18, 20, 20, 20, 21, 21, 21, 22, 
  327. 22, 22, 23, 23, 23, 23, 24, 24, 24, 24, 35, 35, 35, 36, 36, 36, 
  328. 36, 37, 37, 16, 17, 17, 17, 18, 18, 18, 19, 19, 21, 23, 23, 23, 
  329. 25, 25, 25, 25, 32, 32, 32, 33, 33, 33, 33, 34, 34, 34, 35, 35, 
  330. 35, 35, 36, 36, 36, 36, 38, 38, 38, 38, 16, 16, 16, 19, 19, 19, 
  331. 20, 20, 20, 21, 21, 21, 22, 22, 22, 23, 23, 23, 23, 25, 25, 35, 
  332. 35, 36, 36, 36, 36, 37, 37, 37, 23, 32, 32, 32, 38, 38, 38, 42, 
  333. 42, 42, 42, 62, 62, 62, 64, 64, 64, 64, 68, 68, 72, 72, 72, 32, 
  334. 32, 44, 44, 44, 44, 48, 48, 48, 48, 56, 56, 56, 68, 68, 68, 74, 
  335. 74, 74, 32, 32, 32, 42, 42, 42, 50, 50, 50, 50, 60, 60, 60, 60, 
  336. 62, 62, 62, 64, 64, 64, 72, 72, 72, 32, 32, 32, 40, 40, 68, 68, 
  337. 68, 70, 70, 34, 34, 34, 36, 42, 42, 42, 46, 46, 46, 46, 52, 52, 
  338. 52, 52, 54, 54, 58, 58, 58, 60, 60, 60, 60, 64, 64, 64, 66, 68, 
  339. 68, 68, 70, 70, 72, 72, 72, 34, 34, 34, 38, 38, 46, 46, 46, 52, 
  340. 52, 54, 54, 54, 58, 60, 60, 62, 62, 62, 64, 64, 64, 64, 68, 68, 
  341. 68, 70, 74, 74, 34, 34, 34, 42, 42, 42, 42, 46, 46, 46, 48, 48, 
  342. 48, 48, 50, 50, 50, 50, 56, 56, 56, 56, 58, 58, 58, 58, 60, 60, 
  343. 60, 60, 62, 62, 62, 62, 66, 66, 66, 70, 70, 34, 34, 34, 36, 48, 
  344. 48, 48, 48, 50, 50, 50, 50, 52, 52, 52, 52, 66, 66, 66, 72, 36, 
  345. 36, 36, 40, 40, 40, 52, 52, 54, 54, 54, 56, 56, 56, 56, 62, 62, 
  346. 62, 66, 66, 66, 74, 74, 74, 36, 36, 36, 42, 42, 42, 42, 44, 44, 
  347. 44, 52, 52, 52, 52, 54, 54, 54, 58, 58, 58, 58, 60, 60, 60, 60, 
  348. 62, 62, 62, 62, 66, 66, 36, 36, 36, 38, 38, 38, 44, 44, 44, 48, 
  349. 48, 48, 48, 50, 50, 50, 50, 54, 54, 54, 54, 58, 58, 58, 58, 60, 
  350. 60, 60, 60, 64, 64, 64, 64, 68, 68, 68, 70, 70, 74, 74, 74, 44, 
  351. 44, 44, 44, 48, 48, 48, 48, 50, 50, 50, 50, 56, 56, 56, 56, 70, 
  352. 74, 74, 74, 38, 44, 44, 48, 48, 48, 50, 50, 50, 52, 52, 56, 56, 
  353. 56, 64, 64, 64, 66, 70, 72, 40, 40, 40, 46, 46, 46, 54, 54, 58, 
  354. 58, 58, 72, 72, 40, 56)), row.names = c(NA, -834L), class = c("tbl_df", 
  355. "tbl", "data.frame"))

答案1

得分: 1

这个回答使用了marginaleffects的开发版本（0.9.0.9043），你可以按照这里的说明安装它：https://vincentarelbundock.github.io/marginaleffects/

请注意，额外的与lme4相关的参数必须提供给predictions()函数，而不是你在第二个示例中所做的datagrid()函数。

此外，我强烈建议你避免使用include_random，并使用lme4建模包本身提供的默认参数（通过predict.merMod），在这种情况下是re.form和allow.new.levels。

  1. library(lme4)
  2. library(lmerTest)
  3. library(marginaleffects)
  4. library(dplyr)
  6. dat_long$group <- as.factor(dat_long$group)
  7. dat_long$period <- as.factor(dat_long$period)
  8. dat_long <- dat_long %>% mutate(group2 = group)
  10. m222 <- lmer(money ~ session + period + group2 + (1 | id2) + (1 | session / date / period), data = dat_long ) 
  12. comparisons(
  13.     m222,
  14.     variables = "period",
  15.     re.form = NA,
  16.     newdata = datagrid(period = c("p1", "p2", "p3", "p4")))
  17. # 
  18. #    Term Contrast Estimate Std. Error      z   Pr(>|z|)   2.5 % 97.5 % session group2 id2     date
  19. #  period  p2 - p1   -361.6      260.0 -1.391  0.1643688  -871.3  148.1      23    int  16 37.90168
  20. #  period  p2 - p1   -361.6      260.0 -1.391  0.1643688  -871.3  148.1      23    int  16 37.90168
  21. #  period  p2 - p1   -361.6      260.0 -1.391  0.1643688  -871.3  148.1      23    int  16 37.90168
  22. #  period  p2 - p1   -361.6      260.0 -1.391  0.1643688  -871.3  148.1      23    int  16 37.90168
  23. #  period  p3 - p1   -745.6      260.0 -2.868  0.0041366 -1255.3 -236.0      23    int  16 37.90168
  24. #  period  p3 - p1   -745.6      260.0 -2.868  0.0041366 -1255.3 -236.0      23    int  16 37.90168
  25. #  period  p3 - p1   -745.6      260.0 -2.868  0.0041366 -1255.3 -236.0      23    int  16 37.90168
  26. #  period  p3 - p1   -745.6      260.0 -2.868  0.0041366 -1255.3 -236.0      23    int  16 37.90168
  27. #  period  p4 - p1  -1371.6      318.4 -4.308 1.6492e-05 -1995.6 -747.5      23    int  16 37.90168
  28. #  period  p4 - p1  -1371.6      318.4 -4.308 1.6492e-05 -1995.6 -747.5      23    int  16 37.90168
  29. #  period  p4 - p1  -1371.6      318.4 -4.308 1.6492e-05 -1995.6 -747.5      23    int  16 37.90168
  30. #  period  p4 - p1  -1371.6      318.4 -4.308 1.6492e-05 -1995.6 -747.5      23    int  16 37.90168
  31. # 
  32. # Prediction type:  response 
  33. # 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
  1. predictions(
  2.     m222,
  3.     newdata = datagrid(
  4.         id2 = NA,
  5.         session = seq(from = 16, to = 38, by = 1)),
  6.     re.form = NA,
  7.     allow.new.levels = TRUE)
  8. # 
  9. #  Estimate Std. Error     z   Pr(>|z|) 2.5 % 97.5 % period group2     date id2 session
  10. #     19654      656.1 29.96 < 2.22e-16 18368  20940     p1    int 37.90168  NA      16
  11. #     19649      647.4 30.35 < 2.22e-16 18380  20917     p1    int 37.90168  NA      17
  12. #     19643      639.5 30.72 < 2.22e-16 18390  20896     p1    int 37.90168  NA      18
  13. #     19637      632.5 31.05 < 2.22e-16 18398  20877     p1    int 37.90168  NA      19
  14. #     19632      626.2 31.35 < 2.22e-16 18404  20859     p1    int 37.90168  NA      20
  15. #     19626      620.9 31.61 < 2.22e-16 18409  20843     p1    int 37.90168  NA      21
  16. #     19621      616.5 31.83 < 2.22e-16 18412  20829     p1    int 37.90168  NA      22
  17. #     19615      613.0 32.00 < 2.22e-16 18414  20817     p1    int 37.90168  NA      23
  18. #     19610      610.5 32.12 < 2.22e-16 18413  208
  20. <details>
  21. <summary>英文:</summary>
  23. This answer uses the development version (0.9.0.9043) of `marginaleffects`, which you can install by following the instructions here: &lt;https://vincentarelbundock.github.io/marginaleffects/&gt;
  25. Please note that the extra `lme4`-related arguments must be supplied to the `predictions()` function, and not to the `datagrid()` function as you do in your second example.
  27. Also, I strongly suggest you avoid `include_random` and use the default arguments supplied by the `lme4` modelling package itself (via `predict.merMod`). In this case: `re.form` and `allow.new.levels`.
  29. ``` r
  30. library(lme4)
  31. library(lmerTest)
  32. library(marginaleffects)
  33. library(dplyr)
  35. dat_long$group &lt;- as.factor(dat_long$group)
  36. dat_long$period &lt;- as.factor(dat_long$period)
  37. dat_long &lt;- dat_long %&gt;% mutate(group2 = group)
  39. m222 &lt;- lmer(money ~ session + period + group2 + (1 | id2) + (1 | session / date / period), data = dat_long ) 
  41. comparisons(
  42.     m222,
  43.     variables = &quot;period&quot;,
  44.     re.form = NA,
  45.     newdata = datagrid(period = c(&quot;p1&quot;, &quot;p2&quot;, &quot;p3&quot;, &quot;p4&quot;)))
  46. # 
  47. #    Term Contrast Estimate Std. Error      z   Pr(&gt;|z|)   2.5 % 97.5 % session group2 id2     date
  48. #  period  p2 - p1   -361.6      260.0 -1.391  0.1643688  -871.3  148.1      23    int  16 37.90168
  49. #  period  p2 - p1   -361.6      260.0 -1.391  0.1643688  -871.3  148.1      23    int  16 37.90168
  50. #  period  p2 - p1   -361.6      260.0 -1.391  0.1643688  -871.3  148.1      23    int  16 37.90168
  51. #  period  p2 - p1   -361.6      260.0 -1.391  0.1643688  -871.3  148.1      23    int  16 37.90168
  52. #  period  p3 - p1   -745.6      260.0 -2.868  0.0041366 -1255.3 -236.0      23    int  16 37.90168
  53. #  period  p3 - p1   -745.6      260.0 -2.868  0.0041366 -1255.3 -236.0      23    int  16 37.90168
  54. #  period  p3 - p1   -745.6      260.0 -2.868  0.0041366 -1255.3 -236.0      23    int  16 37.90168
  55. #  period  p3 - p1   -745.6      260.0 -2.868  0.0041366 -1255.3 -236.0      23    int  16 37.90168
  56. #  period  p4 - p1  -1371.6      318.4 -4.308 1.6492e-05 -1995.6 -747.5      23    int  16 37.90168
  57. #  period  p4 - p1  -1371.6      318.4 -4.308 1.6492e-05 -1995.6 -747.5      23    int  16 37.90168
  58. #  period  p4 - p1  -1371.6      318.4 -4.308 1.6492e-05 -1995.6 -747.5      23    int  16 37.90168
  59. #  period  p4 - p1  -1371.6      318.4 -4.308 1.6492e-05 -1995.6 -747.5      23    int  16 37.90168
  60. # 
  61. # Prediction type:  response 
  62. # 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
  1. predictions(
  2.     m222,
  3.     newdata = datagrid(
  4.         id2 = NA,
  5.         session = seq(from = 16, to = 38, by = 1)),
  6.     re.form = NA,
  7.     allow.new.levels = TRUE)
  8. # 
  9. #  Estimate Std. Error     z   Pr(&gt;|z|) 2.5 % 97.5 % period group2     date id2 session
  10. #     19654      656.1 29.96 &lt; 2.22e-16 18368  20940     p1    int 37.90168  NA      16
  11. #     19649      647.4 30.35 &lt; 2.22e-16 18380  20917     p1    int 37.90168  NA      17
  12. #     19643      639.5 30.72 &lt; 2.22e-16 18390  20896     p1    int 37.90168  NA      18
  13. #     19637      632.5 31.05 &lt; 2.22e-16 18398  20877     p1    int 37.90168  NA      19
  14. #     19632      626.2 31.35 &lt; 2.22e-16 18404  20859     p1    int 37.90168  NA      20
  15. #     19626      620.9 31.61 &lt; 2.22e-16 18409  20843     p1    int 37.90168  NA      21
  16. #     19621      616.5 31.83 &lt; 2.22e-16 18412  20829     p1    int 37.90168  NA      22
  17. #     19615      613.0 32.00 &lt; 2.22e-16 18414  20817     p1    int 37.90168  NA      23
  18. #     19610      610.5 32.12 &lt; 2.22e-16 18413  20806     p1    int 37.90168  NA      24
  19. #     19604      608.9 32.20 &lt; 2.22e-16 18411  20798     p1    int 37.90168  NA      25
  20. #     19599      608.2 32.22 &lt; 2.22e-16 18406  20791     p1    int 37.90168  NA      26
  21. #     19593      608.6 32.19 &lt; 2.22e-16 18400  20786     p1    int 37.90168  NA      27
  22. #     19587      609.9 32.12 &lt; 2.22e-16 18392  20783     p1    int 37.90168  NA      28
  23. #     19582      612.1 31.99 &lt; 2.22e-16 18382  20782     p1    int 37.90168  NA      29
  24. #     19576      615.3 31.82 &lt; 2.22e-16 18370  20782     p1    int 37.90168  NA      30
  25. #     19571      619.4 31.60 &lt; 2.22e-16 18357  20785     p1    int 37.90168  NA      31
  26. #     19565      624.4 31.33 &lt; 2.22e-16 18341  20789     p1    int 37.90168  NA      32
  27. #     19560      630.4 31.03 &lt; 2.22e-16 18324  20795     p1    int 37.90168  NA      33
  28. #     19554      637.1 30.69 &lt; 2.22e-16 18305  20803     p1    int 37.90168  NA      34
  29. #     19549      644.8 30.32 &lt; 2.22e-16 18285  20812     p1    int 37.90168  NA      35
  30. #     19543      653.2 29.92 &lt; 2.22e-16 18263  20823     p1    int 37.90168  NA      36
  31. #     19538      662.4 29.49 &lt; 2.22e-16 18239  20836     p1    int 37.90168  NA      37
  32. #     19532      672.4 29.05 &lt; 2.22e-16 18214  20850     p1    int 37.90168  NA      38
  33. # 
  34. # Prediction type:  response 
  35. # Columns: rowid, type, estimate, std.error, statistic, p.value, conf.low, conf.high, money, period, group2, date, id2, session

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  • 本文由 发表于 2023年2月14日 22:20:00
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