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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!

A. Oye
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  • you can access the coefficients of your fixed effects with `summary(FA)$coefficients` – Nate Aug 04 '16 at 15:49
  • Thanks Nathan. I meant "to plot" and not "extract". You are right I already have my coefficients. It's just that I can't get the fit itself plotted. – A. Oye Aug 04 '16 at 17:34
  • A `sjPlot`works fine for a simpler version of your model, i.e. `FA <- lmer(Frontal_FA ~ poly(C) + A + D + (1|Subject), data = df); sjp.lmer(FA, y.offset = .4)`. I would recommend you to look into the error message of `lmer` first, it seems like the model you want to estimate has some problems. – majom Aug 04 '16 at 18:12
  • `sjp.lmer` by default plots the random effects of the model. What I need to plot is the fixed effect slopes, which requires `sjp.lmer(FA, type = "fe.slope")` according to `sjPlot`. I ran into the same issues trying to plot fixed effect slopes for the simpler model you suggested. My model removes the correlation matrix between the random intercept and slope terms, but I do think that's the issue as the simpler model also doesn't work. – A. Oye Aug 04 '16 at 18:45
  • Sorry, I mean't "don't" not "do". – A. Oye Aug 05 '16 at 18:18

1 Answers1

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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.

A. Oye
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