Построение фиксированного наклона эффектов от модели Lmer

Question

У меня есть следующий набор данных:

> dput(df)
structure(list(Subject = c(1L, 2L, 3L, 5L, 6L, 6L, 6L, 7L, 7L, 
7L, 8L, 8L, 8L, 9L, 9L, 9L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 
13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L, 16L, 17L, 17L, 17L, 18L, 
18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L, 23L, 23L, 23L, 
24L, 24L, 25L, 25L, 25L, 26L, 26L, 26L, 27L, 27L, 28L, 28L, 29L, 
29L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 
41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 
54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 
67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 
80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 
105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 
116L), A = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1", 
"2"), class = "factor"), B = structure(c(1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 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, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L), .Label = c("1", "2", "3"), class = "factor"), C = c(9.58, 
9.75, 15, 10.75, 13.3, 14.42, 15.5, 9.25, 10.33, 11.33, 9.55, 
11, 11.92, 14.25, 15.5, 16.42, 14.92, 16.17, 10.83, 11.92, 12.92, 
7.5, 8.5, 10.33, 11.25, 13.08, 13.83, 14.92, 15.92, 9.58, 14.83, 
11.92, 8.33, 9.5, 10.5, 6.8, 7.92, 9, 13.5, 10.92, 10, 11, 13, 
15.58, 12.92, 11.8, 5.75, 6.75, 7.83, 11.12, 12.25, 12.08, 13.08, 
14.58, 8.08, 9.17, 10.67, 10.6, 12.67, 7.83, 8.83, 9.67, 10.58, 
11.75, 7, 17.17, 11.25, 13.75, 11.83, 16.92, 8.83, 7.07, 7.83, 
15.08, 15.83, 16.67, 18.87, 11.92, 12.83, 7.83, 12.33, 10, 11.08, 
12.08, 15.67, 11.75, 15, 14.308, 15.9064, 16.161, 16.9578, 8.90197, 
16.2897, 9.05805, 10.5969, 5.15334, 9.1046, 14.1019, 18.9736, 
10.9447, 14.5455, 16.172, 6.65389, 11.3171, 12.2864, 17.9929, 
10.5778, 16.9195, 7.6, 7.8, 7.2, 16.7, 17, 16.5, 17, 15.1, 16, 
16.4, 13.8, 13.8, 14.5, 16.1, 15.8, 15, 14.1, 15, 14.7, 15, 14.5, 
10.8, 11.4, 11.3, 10.9, 11.2, 9.3, 10.8, 9.7, 8, 8.2, 8.2, 17.5, 
12.6, 11.6, 10.8, 11.8, 12.3, 16.3, 17.1, 9.626283368, 14.6, 
13.7), D = structure(c(2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 
1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 
1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1", 
"2"), class = "factor"), Frontal_FA = c(0.4186705, 0.4151535, 
0.4349945, 0.4003705, 0.403488, 0.407451, 0.3997135, 0.38826, 
0.3742275, 0.3851655, 0.3730715, 0.3825115, 0.3698805, 0.395406, 
0.39831, 0.4462415, 0.413532, 0.419088, 0.4373975, 0.4633915, 
0.4411375, 0.3545255, 0.389322, 0.349402, 0.352029, 0.367792, 
0.365298, 0.3790775, 0.379298, 0.36231, 0.3632755, 0.357868, 
0.3764865, 0.3726645, 0.351422, 0.3353255, 0.334196, 0.3462365, 
0.367369, 0.3745925, 0.3610755, 0.360576, 0.357035, 0.3554905, 
0.3745615, 0.38828, 0.3293275, 0.3246945, 0.3555345, 0.375563, 
0.38116, 0.387508, 0.357707, 0.413193, 0.3658075, 0.3776355, 
0.362678, 0.3824945, 0.3771, 0.375347, 0.362468, 0.367618, 0.3630925, 
0.3763995, 0.359458, 0.3982755, 0.3834765, 0.386135, 0.3691575, 
0.388099, 0.350435, 0.3629045, 0.3456775, 0.4404815, 0.4554165, 
0.425763, 0.4491515, 0.461206, 0.453745, 0.4501255, 0.4451875, 
0.4369835, 0.456838, 0.437759, 0.4377635, 0.44434, 0.4436615, 
0.437532, 0.4335325, 0.4407995, 0.470447, 0.4458525, 0.440322, 
0.4570775, 0.4410335, 0.436045, 0.4721345, 0.4734515, 0.4373905, 
0.4139465, 0.440213, 0.440281, 0.425746, 0.454377, 0.4457435, 
0.488561, 0.4393565, 0.4610565, 0.3562055, 0.381041, 0.353253, 
0.4265975, 0.4069595, 0.40092, 0.4261365, 0.429605, 0.425479, 
0.4331755, 0.3981285, 0.4206245, 0.3798475, 0.3704155, 0.395192, 
0.404436, 0.4148915, 0.416144, 0.384652, 0.3916045, 0.41005, 
0.3940605, 0.3926085, 0.383909, 0.391792, 0.372398, 0.3531025, 
0.414441, 0.404335, 0.3682095, 0.359976, 0.376681, 0.4173705, 
0.3492685, 0.397057, 0.3940605, 0.398825, 0.3707115, 0.400228, 
0.3946595, 0.4278775, 0.384037, 0.43577)), .Names = c("Subject", 
"A", "B", "C", "D", "Frontal_FA"), class = "data.frame", row.names = c(NA, 
-151L))

и хотел бы построить фиксированный наклон эффекта для следующей модели:

FA <- lmer(Frontal_FA ~ poly(C) + A + B + D + (poly(C)||Subject), data = df)

Однако при использовании функции пакета sjPlot sjp.lmer(FA, type = "fe.slope") Я получаю следующую ошибку

Error in data.frame(x = model_data[[p_v]], y = resp) : 
  arguments imply differing number of rows: 0, 151
In addition: Warning message:
Insufficient length of color palette provided. 2 color values needed

Я полагаю, что это может иметь отношение к матричной структуре вывода, поэтому попытался расплавить вывод str с помощью "reshape2", но безуспешно. Есть ли способ построить фиксированные наклоны эффекта из выходных данных модели? Заранее спасибо!

Answer 1

Я думаю, что я понял это. poly term in the model seems to displace the the column containing the variable of interest (C) in the str output of the model. Удаление poly term in the model allows for the 'C' column to be identified by the sjPlot код.