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rlme4sjplotlmertest

Plotting fixed effects slope from lmer model

I have the following dataset : 

> 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))

and would like to plot the fixed effect slope for the following model:

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

However, when using the sjPlot package function sjp.lmer(FA, type = "fe.slope") I get the following error 

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

I figure it may have to do with matrix structure of the output, so tried melting the str output with "reshape2", but without success. Is there a way to plot fixed effect slopes from the model output? Thanks in advance!

Solution

  • I think I've figured it out. The poly term in the model seems to displace the the column containing the variable of interest (C) in the str output of the model. Removing the poly term in the model allows for the 'C' column to be identified by the sjPlot code.