Plotting lmer model without covariance matrix

Question

I am trying to plot a number of lmer models for a paper. I had to simplify the random effect structure by dropping the correlation between the random slopes and intercept (Barr et al., 2013). However, when I try to plot using the sjp.lmer funtion, I get the following error:

Error in array(NA, c(J, K)) : 'dims' cannot be of length 0 In addition: Warning message: In ranef.merMod(object, condVar = TRUE) : conditional variances not currently available via ranef when there are multiple terms per factor

Is there a potential work-around for this? Any help would be greatly appreciated.

Hi Ben, Here is some of the data I am working with:

> 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))

Here is the code that I am running

lmer fit

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

plot lmer fit

sjp.lmer(FA)

Thanks for your help.

Answer 1

sjp.lmer, by default, plots the random effects of a model. However, it plots random effects (BLUPs) with confidence intervals, using the arm:se.ranef function. This function causes the first error message you get:

arm::se.ranef(FA)
> Error in array(NA, c(J, K)) : 'dims' cannot be of length 0

Then, the se.ranef functions calls the lme4::ranef function with argument condVar = TRUE, which is not yet implemented for specific conditions (like yours) in lme4. Hence you get the additional warning

In ranef.merMod(object, condVar = TRUE) :
  conditional variances not currently available via ranef when there are multiple terms per factor

If you are especially interested in plotting the random effects, you could use the lme4-implemented dotplot-function:

lattice::dotplot(ranef(FA))

If you are interested in any other plot type (fixed effects, marginal effects, predictions, ...), see ?sjp.lmer or some examples at his page.

Edit If you don't mind installing from GitHub (devtools::install_github("sjPlot/devel"), I have committed a small update, so you can use show.ci = FALSE to avoid computing confidence intervals for random effects:

sjp.lmer(FA, type = "re", show.ci = F, sort.est = "(Intercept)")