x-axis position for datapoints from stat_summary() in ggplot2()

x-axis position for datapoints from stat_summary() in ggplot2() - r

I'd like to put subgroup's means above my boxplots, but can't find a way to position them correctly on the x-axis. With my current code, the symbols for the mean values are all put at the x-axis position of the top-level groups.
Here's my data, and the ggplot2() code below:
cc <- structure(list(Individuum = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L,
22L, 23L, 24L, 25L, 26L, 76L, 27L, 28L, 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, 77L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 76L, 27L, 28L, 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, 77L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
26L, 76L, 27L, 28L, 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, 77L
), Fachgruppe = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 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, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 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, 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("F1",
"F2", "F3"), class = "factor"), Kategorie = 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, 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, 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("K1", "K2", "K3"), class = "factor"),
Antwort = c(0.384615384615385, 1, 0.538461538461538, 0.461538461538462,
0.769230769230769, 0.153846153846154, 0.230769230769231,
0.461538461538462, 0, 0.230769230769231, 0.153846153846154,
0, 0.769230769230769, 0.461538461538462, 0.692307692307692,
0, 0.230769230769231, 0.307692307692308, 0.692307692307692,
0.307692307692308, 0.230769230769231, 0.615384615384615,
0.615384615384615, 0.538461538461538, 0, 0.461538461538462,
0, 0.538461538461538, 0.538461538461538, 0.538461538461538,
0, 0.538461538461538, 0.0769230769230769, 0.692307692307692,
0.384615384615385, 0, 0.384615384615385, 0.461538461538462,
0.923076923076923, 0.384615384615385, 0.307692307692308,
0, 1, 0.461538461538462, 0.307692307692308, 0.153846153846154,
0.230769230769231, 0.692307692307692, 0, 0, 0, 0.615384615384615,
0.384615384615385, 0, 0.384615384615385, 0.384615384615385,
0.384615384615385, 0.461538461538462, 0.307692307692308,
0.384615384615385, 0.384615384615385, 0.153846153846154,
0.538461538461538, 0.153846153846154, 0.307692307692308,
0, 0.461538461538462, 0.615384615384615, 0, 0, 0.307692307692308,
0.307692307692308, 0.307692307692308, 0, 0, 0.538461538461538,
0.307692307692308, 0.214285714285714, 0.142857142857143,
0.357142857142857, 0.214285714285714, 0.785714285714286,
0.0714285714285714, 0.0714285714285714, 0.142857142857143,
0, 0, 0, 0, 0.5, 0, 0.571428571428571, 0, 0, 0.285714285714286,
0.142857142857143, 0.357142857142857, 0.0714285714285714,
0.357142857142857, 0.285714285714286, 0.142857142857143,
0, 0.357142857142857, 0, 0.285714285714286, 0.428571428571429,
0.357142857142857, 0, 0, 0.142857142857143, 0, 0.571428571428571,
0, 0.214285714285714, 0.357142857142857, 0.928571428571429,
0.214285714285714, 0.285714285714286, 0, 1, 0.285714285714286,
0.285714285714286, 0.0714285714285714, 0.214285714285714,
0.214285714285714, 0, 0, 0, 0.285714285714286, 0, 0, 0.357142857142857,
0.285714285714286, 0, 0.571428571428571, 0.428571428571429,
0.357142857142857, 0, 0.0714285714285714, 0.428571428571429,
0, 0.285714285714286, 0, 0.428571428571429, 0.714285714285714,
0, 0, 0.285714285714286, 0.214285714285714, 0.142857142857143,
0, 0, 0.5, 0.142857142857143, 0.2, 0.3, 0.4, 0.7, 0.7, 0.2,
0.2, 0.1, 0, 0.1, 0.1, 0, 0.5, 0, 0.4, 0, 0.3, 0.1, 0.4,
0.3, 0, 0.5, 0.7, 0, 0, 0.3, 0, 0.2, 0.4, 0.5, 0, 0.2, 0.1,
0, 0.3, 0, 0.3, 0, 0.7, 0.3, 0.2, 0, 1, 0.5, 0.3, 0, 0.2,
0.4, 0, 0, 0, 0.7, 0, 0, 0.4, 0.1, 0, 0.3, 0.3, 0.5, 0.2,
0.2, 0.4, 0, 0.3, 0, 0.5, 0.5, 0, 0, 0.4, 0, 0.1, 0, 0, 0.8,
0)), .Names = c("Individuum", "Fachgruppe", "Kategorie",
"Antwort"), row.names = c(NA, -231L), class = "data.frame")
The code:
p_cc <- ggplot(cc, aes(x = Fachgruppe, y = Antwort, fill = Kategorie)) +
geom_boxplot(outlier.size=0) +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=16, size=2) +
labs(y = "Mittlerer Anteil\nbekannter Themen")

Encorporating the comment by #MLavoie: use position=position_dodge(0.75)
p_cc <- ggplot(cc, aes(x = Fachgruppe, y = Antwort, fill = Kategorie)) +
geom_boxplot(outlier.size=0) +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=16, size=2, position=position_dodge(0.75)) +
labs(y = "Mittlerer Anteil\nbekannter Themen")

Related

Multi-level Model in R missing one predictor [duplicate]

I am using the lme4 package and running a linear mixed model but I am confused but the output and expect that I am encountering an error even though I do not get an error message.
The basic issue is when I fit a model like lmer(Values ~ stimuli + timeperiod + scale(poly(distance.code,3,raw=FALSE))*habitat + wind.speed + (1|location.code), data=df, REML=FALSE)
and then look at the results using something like summary I see all the model fixed (and random) effects as I would expect however the habitat effect is always displayed as habitatForest. Like this:
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 996.63179 8.16633 31.22730 122.042 < 2e-16 ***
stimuliBHCO -3.57541 1.28877 8750.89273 -2.774 0.005544 **
stimuliCOHA -10.17037 1.29546 8754.17156 -7.851 4.62e-15 ***
timeperiod 0.19900 0.05516 8744.95307 3.608 0.000310 ***
scale(poly(distance.code, 3, raw = FALSE))1 -3.87613 0.71431 8745.70773 -5.426 5.90e-08 ***
scale(poly(distance.code, 3, raw = FALSE))2 2.65854 0.71463 8745.19353 3.720 0.000200 ***
scale(poly(distance.code, 3, raw = FALSE))3 4.66340 0.72262 8745.67948 6.453 1.15e-10 ***
habitatForest -68.82430 11.83009 29.95226 -5.818 2.34e-06 ***
wind.speed -0.35853 0.07631 8403.15191 -4.698 2.66e-06 ***
scale(poly(distance.code, 3, raw = FALSE))1:habitatForest 2.89860 1.03891 8745.46534 2.790 0.005282 **
scale(poly(distance.code, 3, raw = FALSE))2:habitatForest -3.49758 1.03829 8745.11371 -3.369 0.000759 ***
scale(poly(distance.code, 3, raw = FALSE))3:habitatForest -4.67300 1.03913 8745.30579 -4.497 6.98e-06 ***
---
This is happening even though there are two levels of habitat (Forest and Grassland)
at first, I thought this might be because my model had an interaction term but it happens when I try a simpler model like lmer(Values ~ stimuli + timeperiod + distance.code + habitat + wind.speed + (1|location.code), data=ex.df, REML=FALSE)
Why would it say "habitatForest" and not just "habitat" or if it were going to include a factor by name why not say "habitatForest" and "habitatGrassland"?
A quick look at the expected output from this function here: https://rpubs.com/palday/mixed-interactions or here: https://ase.tufts.edu/bugs/guide/assets/mixed_model_guide.html (among others)
shows that the out put that I am getting is not what is expected or normal.
Other output I have seen simply have factors with two levels, like mine, as a single line (eg habitat).
Here is a portion of the data I am using. I used dput and subseting to produce this. I couldn't figure out how to make the data set smaller and still reproduce the error so I apologize if this is too large. The data set that it comes from is MUCH bigger! (also please let me know if I have used dput incorrectly.(Still new to R and stackoverflow)
structure(list(location.code = structure(c(1L, 1L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L,
4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L,
4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L,
4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 3L, 3L, 3L, 4L, 4L, 4L), .Label = c("BSF1", "BSG1", "RLF3",
"RLG3", "CCBSF1", "CCBSG1", "CPF1", "CPF2", "CPG1", "CPG2", "OSG1",
"OSG2", "RLF4", "RLF5", "RLF1", "RLF2", "RLG1", "RLG2", "BNPF1",
"BNPG1", "OSG3", "OSF1", "CMG3", "CMF1", "BSG2", "BSG3", "WSF1",
"WSF2", "HPG1", "HPG2"), class = "factor"), habitat = structure(c(2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L), .Label = c("Grassland",
"Forest"), class = "factor"), distance.code = c(0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L,
30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L), stimuli = structure(c(3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L,
2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L,
3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L,
2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L,
2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L,
3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 1L), .Label = c("FOSP", "BHCO", "COHA", "YEWA", "TUTI"
), class = "factor"), wind.speed = c(0.8, 0.8, 0.8, 0.2, 0.2,
0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2,
0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8,
0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8,
0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8,
0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2,
0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2,
0.2, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65,
55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65,
65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 55, 55,
55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9,
0.9, 0.9, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65,
65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65,
65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 55, 55,
55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9,
0.9, 0.9, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65,
65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65,
65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9), timeperiod = c(6L, 6L, 6L,
6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L,
9L, 9L, 9L, 9L, 9L, 11L, 11L, 11L, 11L, 11L, 11L, 13L, 13L, 13L,
13L, 13L, 13L, 15L, 15L, 15L, 15L, 15L, 15L, 17L, 17L, 17L, 17L,
17L, 17L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L,
20L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L,
23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L), Values = c(910.721895276374, 922.652711611841,
926.219785713456, 1030.28919690464, 1121.98321368732, 992.741416151102,
910.878353926705, 920.201901019659, 922.134996121665, 992.059286431433,
1042.05240231832, 1018.99804250179, 911.976009884021, 918.215389274037,
931.037495260958, 981.032280455129, 983.700699744073, 989.716307418049,
911.476759038955, 918.554393750162, 920.391856289719, 994.583211567691,
1006.58290843226, 1005.52479816571, 908.665064025178, 917.940176257067,
922.746174825048, 986.419049170517, 1042.41789735969, 1082.89658057517,
916.02310296116, 918.254868924698, 931.01648294424, 982.154409713674,
1008.54477137219, 996.577798511801, 912.914857937818, 916.937508116615,
920.933077377339, 997.669828575817, 1007.44452218386, 1151.25894192961,
909.463528658898, 915.293665875472, 921.917039784441, 983.866984633392,
1002.04551764872, 986.791628665069, 907.695668282537, 917.845214744473,
932.330755620455, 972.609449456089, 1155.55960936774, 1083.40557091613,
909.903267624225, 914.846316952797, 921.279328283221, 1000.3672969178,
1021.78461788922, 1011.40975353271, 915.037273600535, 914.099859036178,
924.116937361394, 994.428182266452, 1123.09745015276, 1004.1485272116,
914.431649376896, 915.27037594587, 929.411251949862, 974.273124973661,
1145.99211507205, 1013.58184367388, 913.467056616881, 920.213007520924,
919.794369158301, 983.816025282468, 1103.11322201674, 974.792027063404,
910.532609655114, 917.616832229923, 923.462599912213, 1015.24811721269,
1070.61183211249, 1016.57332551186, 910.196695694198, 923.403802532832,
905.400995326023, 1036.98011238981, 963.147077473505, 916.899569521736,
931.240844862156, 919.11781354823, 995.408916523572, 960.825305234446,
1026.22960551445, 1000.13773127026, 962.347584090332, 904.090295814044,
908.836747102913, 928.867625382891, 918.100799763641, 906.282906701285,
913.146312873635, 977.094140033575, 972.599778534534, 964.658406857446,
921.91272768213, 910.507770576621, 942.269786765654, 1014.34022271036,
1128.29327664605, 1043.1365958913, 919.185972424773, 925.486310755197,
908.769520270226, 1030.20866627018, 956.104935565803, 922.01947330213,
934.451182538208, 928.626906337293, 986.326936258622, 1003.40797963907,
1021.91264348048, 995.68658929192, 993.102343807935, 901.633626404701,
908.255562868123, 922.840049924103, 917.012733437446, 907.541530752433,
915.050696506642, 983.542956895186, 972.236377246083, 965.082329354352,
918.337944633569, 910.137012141557, 952.89462134025, 977.420371016686,
1154.17994731565, 1022.82998099991, 927.061613377597, 926.745527716988,
908.284054932259, 966.157586219165, 974.986841619676, 916.559494755925,
935.817296050643, 918.835719171662, 1023.62078549133, 1009.23121097376,
1005.81651905991, 981.715747809821, 953.127134375762, 902.809201411559,
907.462229880533, 921.595454423298, 919.198277947855, 904.969515265664,
913.438353334218, 974.889830301362, 970.58615968713, 963.029605541189,
915.889893279581, 908.147726780027, 942.742415528349, 979.939535179807,
1153.51966568673, 1020.93502990084, 916.246150801212, 936.016759720656,
914.4488779132, 962.397352323664, 986.957848140285, 985.364195731404,
932.548910038465, 917.363220594089, 1085.89850605988, 1031.66330597084,
1005.64983154588, 991.988118229379, 975.384741587994, 902.60240793926,
907.989086075871, 923.287310593779, 912.878571722023, 904.107623756648,
905.563259817979, 991.530368160932, 975.190212414434, 965.951810135591,
915.334621878897, 910.857441830446, 936.093336975328, 972.074491630181,
1106.77459226532, 993.45400883741, 951.911391767329, 927.688604859773,
915.194279622847, 971.414103170297, 956.138106650696, 965.458656222347,
944.097918792458, 947.157460200658, 1029.14870726558, 992.151638322899,
954.129642526236, 981.48182339388, 968.10870393618, 906.941701681267,
917.956716926981, 923.05649603805, 934.459432014683, 922.801034508827,
920.724850575215, 981.478432929603, 1012.67364507927, 966.471299899978,
912.640460101352, 906.34455384334, 923.738349342148, 970.987788560016,
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Here is the code (I think) that would be needed to fit the model and see the summary after the above data is loaded:
library(lme4)
library(lmerTest)
fit1 <- lmer(Values ~ stimuli + timeperiod + scale(poly(distance.code,3,raw=FALSE))*habitat + wind.speed + (1|location.code), data=df, REML=FALSE)
fit2 <- lmer(Values ~ stimuli + timeperiod + distance.code + habitat + wind.speed + (1|location.code), data=ex.df, REML=FALSE)
summary(fit1)
#or
summary(fit2)
I think this has to do with my data structure and the programming but if it is actually something to do with the stats I am happy to take this post down and re-post over at the stats stackexchange.
Thanks for any help!

note: although your question is about the lmer() function, this answer also applies to lm() and other R functions that fit linear models.
The way that coefficient estimates from linear models in R are presented can be confusing. To understand what's going on, you need to understand how R fits linear models when the predictor is a factor variable.
Coefficients on factor variables in R linear models
Before we look at factor variables, let's look at the more straightforward situation where the predictor is continuous. In your example dataset, one of the predictors is wind speed (continuous variable). The estimated coefficient is about -0.35. It's easy to interpret this: averaged across the other predictors, for every increase of 1 km/h in wind speed, your response value is predicted to decrease by 0.35.
But what about if the predictor is a factor? A categorical variable cannot increase or decrease by 1. Instead it can take several discrete values. So what the lmer() or lm() function does by default is automatically code your factor variable as a set of so-called "dummy variables." Dummy variables are binary (they can take values of 0 or 1). If the factor variable has n levels, you need n-1 dummy variables to encode it. The reference level or control group acts like an intercept.
In the case of your habitat variable, there are only 2 levels so you have only 1 dummy variable which will be 0 if habitat is not Forest and 1 if it is Forest. Now we can interpret the coefficient estimate of -68.8: the average value of your response is expected to be 68.8 less in forest habitat relative to the reference level of grassland habitat. You don't need a second dummy variable for grassland because you only need to estimate the one coefficient to compare the two habitats.
If you had a third habitat, let's say wetland, there would be a second dummy variable that would be 0 if not wetland and 1 if wetland. The coefficient estimate there would be the expected difference between the value of the response variable in wetland habitat compared to grassland habitat. Grassland will be the reference level for all the coefficients.
Default setting of reference level
Now to directly address your question of why habitatForest is the coefficient name.
Because by default no reference level or control group is specified, the first one in the factor level ordering becomes the reference level to which all other levels are compared. Then the coefficients are named by appending the variable's name to the name of the level being compared to the reference level. Your factor is ordered with grassland first and forest second. So the coefficient is the effect of the habitat being forest habitat, compared to the reference level, which is grassland in this case. If you switched the habitat factor level ordering, Forest would be the reference level and you would get habitatGrassland as the coefficient instead. (Note that default factor level ordering is alphabetical, so without specifically ordering the factor levels as you seem to have done, Forest would be the reference level by default).
Incidentally, the two links you give in your question (guides to mixed models from Phillip Alday and Tufts) do in fact have the same kind of output as you are getting. For example in Alday's tutorial, the factor recipe has 3 levels: A, B, and C. There are two coefficients in the fixed effects summary, recipeB and recipeC, just as you would expect from dummy coding using A as reference level. You may be confusing the fixed effects summary with the ANOVA table presented elsewhere in his post. The ANOVA table does only have a single line for recipe which gives you the ratio of variance due to recipe (across all its levels) and the total variance. So that would only be one ratio regardless of how many levels recipe has.
Further reading
This is not the place for a full discussion of contrast coding in linear models in R. The dummy coding (which you may also see called one-hot encoding) I described here is just one way to do it. These resources may be helpful:
Documentation for the base R function contrasts()
description of different types of categorical variable coding in R from UCLA IDRE Stats
Marissa Barlaz' tutorial on R contrast coding

Why does the output of of a linear mixed model using lme4 show one level of a factor but not another?

I am using the lme4 package and running a linear mixed model but I am confused but the output and expect that I am encountering an error even though I do not get an error message.
The basic issue is when I fit a model like lmer(Values ~ stimuli + timeperiod + scale(poly(distance.code,3,raw=FALSE))*habitat + wind.speed + (1|location.code), data=df, REML=FALSE)
and then look at the results using something like summary I see all the model fixed (and random) effects as I would expect however the habitat effect is always displayed as habitatForest. Like this:
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 996.63179 8.16633 31.22730 122.042 < 2e-16 ***
stimuliBHCO -3.57541 1.28877 8750.89273 -2.774 0.005544 **
stimuliCOHA -10.17037 1.29546 8754.17156 -7.851 4.62e-15 ***
timeperiod 0.19900 0.05516 8744.95307 3.608 0.000310 ***
scale(poly(distance.code, 3, raw = FALSE))1 -3.87613 0.71431 8745.70773 -5.426 5.90e-08 ***
scale(poly(distance.code, 3, raw = FALSE))2 2.65854 0.71463 8745.19353 3.720 0.000200 ***
scale(poly(distance.code, 3, raw = FALSE))3 4.66340 0.72262 8745.67948 6.453 1.15e-10 ***
habitatForest -68.82430 11.83009 29.95226 -5.818 2.34e-06 ***
wind.speed -0.35853 0.07631 8403.15191 -4.698 2.66e-06 ***
scale(poly(distance.code, 3, raw = FALSE))1:habitatForest 2.89860 1.03891 8745.46534 2.790 0.005282 **
scale(poly(distance.code, 3, raw = FALSE))2:habitatForest -3.49758 1.03829 8745.11371 -3.369 0.000759 ***
scale(poly(distance.code, 3, raw = FALSE))3:habitatForest -4.67300 1.03913 8745.30579 -4.497 6.98e-06 ***
---
This is happening even though there are two levels of habitat (Forest and Grassland)
at first, I thought this might be because my model had an interaction term but it happens when I try a simpler model like lmer(Values ~ stimuli + timeperiod + distance.code + habitat + wind.speed + (1|location.code), data=ex.df, REML=FALSE)
Why would it say "habitatForest" and not just "habitat" or if it were going to include a factor by name why not say "habitatForest" and "habitatGrassland"?
A quick look at the expected output from this function here: https://rpubs.com/palday/mixed-interactions or here: https://ase.tufts.edu/bugs/guide/assets/mixed_model_guide.html (among others)
shows that the out put that I am getting is not what is expected or normal.
Other output I have seen simply have factors with two levels, like mine, as a single line (eg habitat).
Here is a portion of the data I am using. I used dput and subseting to produce this. I couldn't figure out how to make the data set smaller and still reproduce the error so I apologize if this is too large. The data set that it comes from is MUCH bigger! (also please let me know if I have used dput incorrectly.(Still new to R and stackoverflow)
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1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L), .Label = c("Grassland",
"Forest"), class = "factor"), distance.code = c(0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L,
30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L, 0L, 60L, 0L, 30L, 60L,
0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L, 60L, 0L, 30L,
60L), stimuli = structure(c(3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L,
2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L,
3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L,
2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L,
2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 3L,
3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 1L), .Label = c("FOSP", "BHCO", "COHA", "YEWA", "TUTI"
), class = "factor"), wind.speed = c(0.8, 0.8, 0.8, 0.2, 0.2,
0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2,
0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8,
0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8,
0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8,
0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2,
0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2,
0.2, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65,
55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65,
65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 55, 55,
55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9,
0.9, 0.9, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65,
65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65,
65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 55, 55,
55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9,
0.9, 0.9, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65,
65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 55, 55, 55, 50, 50,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 65,
65, 65, 65, 65, 55, 55, 55, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9), timeperiod = c(6L, 6L, 6L,
6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L,
9L, 9L, 9L, 9L, 9L, 11L, 11L, 11L, 11L, 11L, 11L, 13L, 13L, 13L,
13L, 13L, 13L, 15L, 15L, 15L, 15L, 15L, 15L, 17L, 17L, 17L, 17L,
17L, 17L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L,
20L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L,
23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L), Values = c(910.721895276374, 922.652711611841,
926.219785713456, 1030.28919690464, 1121.98321368732, 992.741416151102,
910.878353926705, 920.201901019659, 922.134996121665, 992.059286431433,
1042.05240231832, 1018.99804250179, 911.976009884021, 918.215389274037,
931.037495260958, 981.032280455129, 983.700699744073, 989.716307418049,
911.476759038955, 918.554393750162, 920.391856289719, 994.583211567691,
1006.58290843226, 1005.52479816571, 908.665064025178, 917.940176257067,
922.746174825048, 986.419049170517, 1042.41789735969, 1082.89658057517,
916.02310296116, 918.254868924698, 931.01648294424, 982.154409713674,
1008.54477137219, 996.577798511801, 912.914857937818, 916.937508116615,
920.933077377339, 997.669828575817, 1007.44452218386, 1151.25894192961,
909.463528658898, 915.293665875472, 921.917039784441, 983.866984633392,
1002.04551764872, 986.791628665069, 907.695668282537, 917.845214744473,
932.330755620455, 972.609449456089, 1155.55960936774, 1083.40557091613,
909.903267624225, 914.846316952797, 921.279328283221, 1000.3672969178,
1021.78461788922, 1011.40975353271, 915.037273600535, 914.099859036178,
924.116937361394, 994.428182266452, 1123.09745015276, 1004.1485272116,
914.431649376896, 915.27037594587, 929.411251949862, 974.273124973661,
1145.99211507205, 1013.58184367388, 913.467056616881, 920.213007520924,
919.794369158301, 983.816025282468, 1103.11322201674, 974.792027063404,
910.532609655114, 917.616832229923, 923.462599912213, 1015.24811721269,
1070.61183211249, 1016.57332551186, 910.196695694198, 923.403802532832,
905.400995326023, 1036.98011238981, 963.147077473505, 916.899569521736,
931.240844862156, 919.11781354823, 995.408916523572, 960.825305234446,
1026.22960551445, 1000.13773127026, 962.347584090332, 904.090295814044,
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1032.27384329955, 974.803996932782)), class = "data.frame", row.names = c(85L,
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1229L))
Here is the code (I think) that would be needed to fit the model and see the summary after the above data is loaded:
library(lme4)
library(lmerTest)
fit1 <- lmer(Values ~ stimuli + timeperiod + scale(poly(distance.code,3,raw=FALSE))*habitat + wind.speed + (1|location.code), data=df, REML=FALSE)
fit2 <- lmer(Values ~ stimuli + timeperiod + distance.code + habitat + wind.speed + (1|location.code), data=ex.df, REML=FALSE)
summary(fit1)
#or
summary(fit2)
I think this has to do with my data structure and the programming but if it is actually something to do with the stats I am happy to take this post down and re-post over at the stats stackexchange.
Thanks for any help!

Error using 'poly': 'degree' must be less than number of unique points while using 'effects' package

I am trying to use the effects package to create plots of effects in a linear mixed model. I specify the model
fit1 <- lmer(Values ~ stimuli + timeperiod + scale(poly(distance.code,3,raw=FALSE))*habitat + wind.speed + (1|location.code), data=ex.df, REML=FALSE)
For this model I can generate results for analysis as I expect using summary or anova but when I try to look at specific effects:
allEffects(fit1)
#or
plot(allEffects(fit1))
#or
emmeans(fit1, pairwise ~ stimuli)
An error is returned:
Error in poly(distance.code, 3, raw = FALSE) :
'degree' must be less than number of unique points
(with the plot function the error is different but is probably arising from the error with allEffects)
I understand, based on the responses to this question and this question, that "numerical overflow" can be an issue with poly terms. However, I am not clear on what this means or how to overcome the issue.
I also saw in this post and in another post about lme4 that I can no longer find, that I might need to update packages so I have updated 'effects' and 'lme4' in an attempt to remedy this but to no avail.
So if this error is happening because of "numerical overflow" how can I remedy the problem? or if it is not numerical overflow what is happening and how can I work around this?
a subset of my data using dput is:
structure(list(location.code = structure(c(1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
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1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
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1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L,
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3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 4L, 4L, 4L), .Label = c("BSF1", "BSG1", "RLF3",
"RLG3", "CCBSF1", "CCBSG1", "CPF1", "CPF2", "CPG1", "CPG2", "OSG1",
"OSG2", "RLF4", "RLF5", "RLF1", "RLF2", "RLG1", "RLG2", "BNPF1",
"BNPG1", "OSG3", "OSF1", "CMG3", "CMF1", "BSG2", "BSG3", "WSF1",
"WSF2", "HPG1", "HPG2"), class = "factor"), stimuli = structure(c(3L,
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3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("FOSP",
"BHCO", "COHA", "YEWA", "TUTI"), class = "factor"), habitat = structure(c(2L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Grassland",
"Forest"), class = "factor"), exp.period = 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, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 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,
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, 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, 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), .Label = c("before",
"during", "after"), class = "factor"), timeperiod = c(6L, 6L,
6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L), distance.code = c(0L, 30L, 60L,
120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L,
60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L,
30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L, 60L,
0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L,
60L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L,
30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L,
0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L,
120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L, 60L, 120L, 0L, 30L,
60L, 120L, 0L, 60L, 120L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L,
30L, 60L, 120L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 120L, 0L, 30L,
60L, 120L, 0L, 30L, 60L, 120L, 0L, 60L, 120L, 0L, 30L, 60L, 120L,
0L, 60L, 120L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L,
120L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 120L,
0L, 30L, 60L, 120L, 0L, 60L, 120L, 0L, 30L, 60L, 120L, 0L, 60L,
120L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L,
30L, 60L, 120L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 120L, 0L, 30L,
60L, 120L, 0L, 60L, 120L, 0L, 30L, 60L, 120L, 0L, 60L, 120L,
0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L,
60L, 120L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 120L, 0L, 30L, 60L,
120L, 0L, 60L, 120L, 0L, 30L, 60L, 120L, 0L, 60L, 120L, 0L, 30L,
60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 120L,
0L, 30L, 60L, 120L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 120L, 0L,
60L, 120L, 0L, 30L, 60L, 120L, 0L, 60L, 120L, 0L, 30L, 60L),
Values = c(910.721895276374, 922.652711611841, 926.219785713456,
918.776924477918, 1030.28919690464, 1121.98321368732, 992.741416151102,
910.878353926705, 920.201901019659, 922.134996121665, 917.610324052986,
992.059286431433, 1042.05240231832, 1018.99804250179, 911.976009884021,
918.215389274037, 931.037495260958, 913.49701806948, 981.032280455129,
983.700699744073, 989.716307418049, 911.476759038955, 918.554393750162,
920.391856289719, 911.795802370903, 994.583211567691, 1006.58290843226,
1005.52479816571, 908.665064025178, 917.940176257067, 922.746174825048,
921.752449434568, 986.419049170517, 1042.41789735969, 1082.89658057517,
916.02310296116, 918.254868924698, 931.01648294424, 924.221021573334,
982.154409713674, 1008.54477137219, 996.577798511801, 912.914857937818,
916.937508116615, 920.933077377339, 917.443294381608, 997.669828575817,
1007.44452218386, 1151.25894192961, 909.463528658898, 915.293665875472,
921.917039784441, 912.073280663674, 983.866984633392, 1002.04551764872,
986.791628665069, 907.695668282537, 917.845214744473, 932.330755620455,
917.500330773026, 972.609449456089, 1155.55960936774, 1083.40557091613,
909.903267624225, 914.846316952797, 921.279328283221, 914.498616645498,
1000.3672969178, 1021.78461788922, 1011.40975353271, 915.037273600535,
914.099859036178, 924.116937361394, 913.523739017819, 994.428182266452,
1123.09745015276, 1004.1485272116, 914.431649376896, 915.27037594587,
929.411251949862, 910.549315840806, 974.273124973661, 1145.99211507205,
1013.58184367388, 913.467056616881, 920.213007520924, 919.794369158301,
912.333012054637, 983.816025282468, 1103.11322201674, 974.792027063404,
910.532609655114, 917.616832229923, 923.462599912213, 913.432298686233,
1015.24811721269, 1070.61183211249, 1016.57332551186, 910.196695694198,
923.403802532832, 905.400995326023, 934.612035397761, 1036.98011238981,
963.147077473505, 953.792949959199, 916.899569521736, 931.240844862156,
919.11781354823, 938.028220926723, 995.408916523572, 960.825305234446,
993.019295484939, 1026.22960551445, 1000.13773127026, 962.347584090332,
1074.31979099791, 904.090295814044, 908.836747102913, 928.867625382891,
918.100799763641, 906.282906701285, 913.146312873635, 921.224088728859,
977.094140033575, 972.599778534534, 964.658406857446, 1197.35130424458,
921.91272768213, 910.507770576621, 942.269786765654, 922.718235872787,
1014.34022271036, 1128.29327664605, 1043.1365958913, 1238.18704569961,
919.185972424773, 925.486310755197, 908.769520270226, 919.644447501213,
1030.20866627018, 956.104935565803, 955.159231718685, 922.01947330213,
934.451182538208, 928.626906337293, 941.089746683706, 986.326936258622,
1003.40797963907, 1007.57786522109, 1021.91264348048, 995.68658929192,
993.102343807935, 1114.80420865448, 901.633626404701, 908.255562868123,
922.840049924103, 917.012733437446, 907.541530752433, 915.050696506642,
925.95358291661, 983.542956895186, 972.236377246083, 965.082329354352,
1205.36753472358, 918.337944633569, 910.137012141557, 952.89462134025,
923.334999242316, 977.420371016686, 1154.17994731565, 1022.82998099991,
1186.66254220951, 927.061613377597, 926.745527716988, 908.284054932259,
921.213190559531, 966.157586219165, 974.986841619676, 959.421220417498,
916.559494755925, 935.817296050643, 918.835719171662, 912.457217113586,
1023.62078549133, 1009.23121097376, 978.938675917385, 1005.81651905991,
981.715747809821, 953.127134375762, 1088.16577366048, 902.809201411559,
907.462229880533, 921.595454423298, 919.198277947855, 904.969515265664,
913.438353334218, 918.91715550342, 974.889830301362, 970.58615968713,
963.029605541189, 1182.94093491074, 915.889893279581, 908.147726780027,
942.742415528349, 928.20319656241, 979.939535179807, 1153.51966568673,
1020.93502990084, 1154.799618481, 916.246150801212, 936.016759720656,
914.4488779132, 918.823772018551, 962.397352323664, 986.957848140285,
972.131488585193, 985.364195731404, 932.548910038465, 917.363220594089,
919.124801182577, 1085.89850605988, 1031.66330597084, 974.763804119707,
1005.64983154588, 991.988118229379, 975.384741587994, 1064.14809010237,
902.60240793926, 907.989086075871, 923.287310593779, 912.878571722023,
904.107623756648, 905.563259817979, 917.423553921906, 991.530368160932,
975.190212414434, 965.951810135591, 1192.3330908297, 915.334621878897,
910.857441830446, 936.093336975328, 932.960789822422, 972.074491630181,
1106.77459226532, 993.45400883741, 1138.94109332484, 951.911391767329,
927.688604859773, 915.194279622847, 920.98264624041, 971.414103170297,
956.138106650696, 969.385400747507, 965.458656222347, 944.097918792458,
947.157460200658, 915.929397317864, 1029.14870726558, 992.151638322899,
964.680220137879, 954.129642526236, 981.48182339388, 968.10870393618,
1097.48462256339, 906.941701681267, 917.956716926981, 923.05649603805,
934.459432014683, 922.801034508827, 920.724850575215, 935.811146196027,
981.478432929603, 1012.67364507927, 966.471299899978, 1192.4066704659,
912.640460101352, 906.34455384334, 923.738349342148, 916.883929696437,
970.987788560016, 1210.42940542072, 975.753397539076, 1138.97675920151,
911.747488522664, 928.34872697947, 910.852487444859, 916.227875349016,
982.304620375747, 1028.52794775628, 999.236663664046, 913.408967803895,
934.334726415048, 916.354017093653, 918.660674732388, 1036.08727658415,
974.408618327141, 1006.21629092128, 1004.71633485176, 995.142763465394,
987.00017276687), wind.speed = c(0.8, 0.8, 0.8, 0.8, 0.2,
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0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8,
0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2, 0.2,
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0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8,
0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2,
0.8, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2,
0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 65, 65, 65,
65, 65, 65, 65, 55, 55, 55, 55, 50, 50, 50, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9,
0.9, 65, 65, 65, 65, 65, 65, 65, 55, 55, 55, 55, 50, 50,
50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.9, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65, 65, 65, 55, 55,
55, 55, 50, 50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 0.9, 65, 65, 65, 65, 65,
65, 65, 55, 55, 55, 55, 50, 50, 50, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9, 0.9, 0.9, 65,
65, 65, 65, 65, 65, 65, 55, 55, 55, 55, 50, 50, 50, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0.9,
0.9, 0.9, 65, 65, 65, 65, 65, 65, 65, 55, 55, 55, 55, 50,
50, 50, 0, 0, 0)), row.names = c(85L, 86L, 87L, 88L, 89L,
90L, 91L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 113L, 114L,
115L, 116L, 117L, 118L, 119L, 127L, 128L, 129L, 130L, 131L, 132L,
133L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 155L, 156L, 157L,
158L, 159L, 160L, 161L, 169L, 170L, 171L, 172L, 173L, 174L, 175L,
183L, 184L, 185L, 186L, 187L, 188L, 189L, 197L, 198L, 199L, 200L,
201L, 202L, 203L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 225L,
226L, 227L, 228L, 229L, 230L, 231L, 239L, 240L, 241L, 242L, 243L,
244L, 245L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 267L, 268L,
269L, 270L, 271L, 272L, 273L, 615L, 616L, 617L, 618L, 619L, 620L,
621L, 622L, 623L, 624L, 625L, 626L, 627L, 628L, 629L, 630L, 631L,
632L, 640L, 641L, 642L, 643L, 644L, 645L, 646L, 647L, 648L, 649L,
650L, 651L, 652L, 653L, 654L, 655L, 656L, 657L, 658L, 659L, 660L,
661L, 662L, 663L, 664L, 665L, 666L, 667L, 668L, 669L, 670L, 671L,
672L, 673L, 674L, 675L, 676L, 684L, 685L, 686L, 687L, 688L, 689L,
690L, 691L, 692L, 693L, 694L, 695L, 696L, 697L, 698L, 699L, 700L,
701L, 702L, 703L, 704L, 705L, 706L, 707L, 708L, 709L, 710L, 711L,
712L, 713L, 714L, 715L, 716L, 717L, 718L, 719L, 720L, 728L, 729L,
730L, 731L, 732L, 733L, 734L, 735L, 736L, 737L, 738L, 739L, 740L,
741L, 742L, 743L, 744L, 745L, 746L, 747L, 748L, 749L, 750L, 751L,
752L, 753L, 754L, 755L, 756L, 757L, 758L, 759L, 760L, 761L, 762L,
763L, 764L, 772L, 773L, 774L, 775L, 776L, 777L, 778L, 779L, 780L,
781L, 782L, 783L, 784L, 785L, 786L, 787L, 788L, 789L, 790L, 791L,
792L, 793L, 794L, 795L, 796L, 797L, 798L, 799L, 800L, 801L, 802L,
803L, 804L, 805L, 806L, 807L, 808L, 816L, 817L, 818L, 819L, 820L,
821L, 822L, 823L, 824L, 825L, 826L, 827L, 828L, 829L, 830L, 831L,
832L, 833L, 834L, 835L, 836L, 837L, 838L, 839L, 840L, 841L, 842L,
843L, 844L, 845L, 846L, 847L, 848L, 849L, 850L, 851L), class = "data.frame")
> ex.df <- head(ex.df, 100)
> dput(ex.df)
structure(list(location.code = structure(c(1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L), .Label = c("BSF1",
"BSG1", "RLF3", "RLG3", "CCBSF1", "CCBSG1", "CPF1", "CPF2", "CPG1",
"CPG2", "OSG1", "OSG2", "RLF4", "RLF5", "RLF1", "RLF2", "RLG1",
"RLG2", "BNPF1", "BNPG1", "OSG3", "OSF1", "CMG3", "CMF1", "BSG2",
"BSG3", "WSF1", "WSF2", "HPG1", "HPG2"), class = "factor"), stimuli = structure(c(3L,
3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L,
2L, 1L, 1L), .Label = c("FOSP", "BHCO", "COHA", "YEWA", "TUTI"
), class = "factor"), habitat = structure(c(2L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L), .Label = c("Grassland",
"Forest"), class = "factor"), exp.period = 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, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 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,
1L, 1L), .Label = c("before", "during", "after"), class = "factor"),
timeperiod = c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L,
23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
6L, 6L), distance.code = c(0L, 30L, 60L, 120L, 0L, 30L, 60L,
0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L,
30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L,
120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L,
30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L,
60L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L,
0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L,
60L, 120L, 0L, 30L, 60L, 0L, 30L, 60L, 120L, 0L, 30L, 60L,
0L, 30L, 60L, 120L, 0L, 30L, 60L, 0L, 30L), Values = c(910.721895276374,
922.652711611841, 926.219785713456, 918.776924477918, 1030.28919690464,
1121.98321368732, 992.741416151102, 910.878353926705, 920.201901019659,
922.134996121665, 917.610324052986, 992.059286431433, 1042.05240231832,
1018.99804250179, 911.976009884021, 918.215389274037, 931.037495260958,
913.49701806948, 981.032280455129, 983.700699744073, 989.716307418049,
911.476759038955, 918.554393750162, 920.391856289719, 911.795802370903,
994.583211567691, 1006.58290843226, 1005.52479816571, 908.665064025178,
917.940176257067, 922.746174825048, 921.752449434568, 986.419049170517,
1042.41789735969, 1082.89658057517, 916.02310296116, 918.254868924698,
931.01648294424, 924.221021573334, 982.154409713674, 1008.54477137219,
996.577798511801, 912.914857937818, 916.937508116615, 920.933077377339,
917.443294381608, 997.669828575817, 1007.44452218386, 1151.25894192961,
909.463528658898, 915.293665875472, 921.917039784441, 912.073280663674,
983.866984633392, 1002.04551764872, 986.791628665069, 907.695668282537,
917.845214744473, 932.330755620455, 917.500330773026, 972.609449456089,
1155.55960936774, 1083.40557091613, 909.903267624225, 914.846316952797,
921.279328283221, 914.498616645498, 1000.3672969178, 1021.78461788922,
1011.40975353271, 915.037273600535, 914.099859036178, 924.116937361394,
913.523739017819, 994.428182266452, 1123.09745015276, 1004.1485272116,
914.431649376896, 915.27037594587, 929.411251949862, 910.549315840806,
974.273124973661, 1145.99211507205, 1013.58184367388, 913.467056616881,
920.213007520924, 919.794369158301, 912.333012054637, 983.816025282468,
1103.11322201674, 974.792027063404, 910.532609655114, 917.616832229923,
923.462599912213, 913.432298686233, 1015.24811721269, 1070.61183211249,
1016.57332551186, 910.196695694198, 923.403802532832), wind.speed = c(0.8,
0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2, 0.2,
0.2, 0.8, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.8,
0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8,
0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2,
0.8, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2,
0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8,
0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8,
0.8, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.8, 0.2, 0.2,
0.2, 65, 65)), row.names = c(85L, 86L, 87L, 88L, 89L, 90L,
91L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 113L, 114L, 115L,
116L, 117L, 118L, 119L, 127L, 128L, 129L, 130L, 131L, 132L, 133L,
141L, 142L, 143L, 144L, 145L, 146L, 147L, 155L, 156L, 157L, 158L,
159L, 160L, 161L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 183L,
184L, 185L, 186L, 187L, 188L, 189L, 197L, 198L, 199L, 200L, 201L,
202L, 203L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 225L, 226L,
227L, 228L, 229L, 230L, 231L, 239L, 240L, 241L, 242L, 243L, 244L,
245L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 267L, 268L, 269L,
270L, 271L, 272L, 273L, 615L, 616L), class = "data.frame")
Thanks for any help!
EDIT!!
I ran terms(fit1) as suggested in the comments, the results were as follows:
terms(fit1)
Values ~ stimuli + timeperiod + scale(poly(distance.code, 3,
raw = FALSE)) * habitat + wind.speed
attr(,"variables")
list(Values, stimuli, timeperiod, scale(poly(distance.code, 3,
raw = FALSE)), habitat, wind.speed)
attr(,"factors")
stimuli timeperiod scale(poly(distance.code, 3, raw = FALSE)) habitat wind.speed
Values 0 0 0 0 0
stimuli 1 0 0 0 0
timeperiod 0 1 0 0 0
scale(poly(distance.code, 3, raw = FALSE)) 0 0 1 0 0
habitat 0 0 0 1 0
wind.speed 0 0 0 0 1
scale(poly(distance.code, 3, raw = FALSE)):habitat
Values 0
stimuli 0
timeperiod 0
scale(poly(distance.code, 3, raw = FALSE)) 1
habitat 1
wind.speed 0
attr(,"term.labels")
[1] "stimuli" "timeperiod"
[3] "scale(poly(distance.code, 3, raw = FALSE))" "habitat"
[5] "wind.speed" "scale(poly(distance.code, 3, raw = FALSE)):habitat"
attr(,"order")
[1] 1 1 1 1 1 2
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: R_GlobalEnv>
attr(,"predvars")
list(Values, stimuli, timeperiod, scale(poly(distance.code, 3,
raw = FALSE)), habitat, wind.speed)

Here is a simple parallel example illustrating that wrapping poly() in scale() is the culprit:
> library(emmeans)
> mod1 = lm(mpg ~ am + poly(disp, 3), data = mtcars)
> ref_grid(mod1)
'emmGrid' object with variables:
am = 0, 1
disp = 230.72
> mod2 = lm(mpg ~ am + scale(poly(disp, 3)), data = mtcars)
> ref_grid(mod2)
Error in poly(disp, 3) :
'degree' must be less than number of unique points
Specifically, the call to scale() messes up the predvars attribute in the model's terms component:
> attr(terms(mod1), "predvars")
list(mpg, am, poly(disp, 3, coefs = list(alpha = c(230.721875,
279.549822668452, 298.198735227759), norm2 = c(1, 32, 476184.7946875,
5315202742.2241, 64139299346388.8))))
This provides the coefficients needed to construct the orthogonal polynomial basis; whereas...
> attr(terms(mod2), "predvars")
list(mpg, am, scale(poly(disp, 3)))
That information is excluded.
Note that the scale() call is completely unnecessary anyway, as poly() generates an orthonormal matrix of predictors.

Newbie attempting linear mixed effects model in R studio - TOTAL FAIL

After searching over an hour (this forum, Youtube, class notes, google) I've found no help for my question. I'm a complete newb who knows nothing about R or stats.
I'm attempting to create a linear mixed effects model in R. I'm measuring leaf width across three different locations (Jacksonville FL, Augusta GA, & Atlanta GA), and within those three locations there is a high-nitrogen and low-nitrogen plot. I have 150 leaf measurements from 50 trees.
My limited understanding tells me that the leaf width is the continuous response variable, and city and plot are the discrete explanatory variables. The random effect would be the individual trees, since the leaf width within a single tree is non-independent.
I've used "nlme" to make a model:
leaf.width.model <- lme(width ~ city*plot, (1|tree.id), data=leaf)
I then ran an ANOVA test, and it suggested there's something going on with city and the interaction between city and plot. This is where I'm stuck. I want to make a plot that has lines for all three cities, but I haven't a clue how to do that. When I try to use the plot function, I just get a boxplot.
I've literally tried for hours and am more lost and confused than before.
1) How can I make this graph?
2) What other tests should I do to analyze and/or visualize this data?
I am forever grateful for any help at all. I really want to learn R and stats very badly, but I'm getting discouraged.
Thank you,
Rich
P.S Here is the output of the dput function:
> dput(tree) structure(list(tree.id = structure(c(24L, 24L, 32L, 25L, 25L, 24L, 24L, 32L, 25L, 25L, 43L, 45L, 45L, 43L, 23L, 23L, 45L, 45L, 23L, 23L, 41L, 41L, 38L, 11L, 11L, 38L, 41L, 41L, 11L, 11L, 14L, 14L, 29L, 13L, 13L, 14L, 14L, 29L, 13L, 13L, 4L, 4L, 1L, 1L, 20L, 1L, 1L, 20L, 6L, 8L, 8L, 5L, 5L, 6L, 4L, 4L, 8L, 8L, 5L, 5L, 9L, 9L, 10L, 10L, 12L, 12L, 13L, 13L, 22L, 22L, 23L, 23L, 24L, 24L, 25L, 25L, 25L, 25L, 40L, 40L, 41L, 41L, 38L, 38L, 39L, 39L, 14L, 14L, 14L, 15L, 15L, 28L, 28L, 29L, 29L, 35L, 35L, 36L, 36L, 37L, 37L, 42L, 42L, 43L, 43L, 44L, 44L, 45L, 45L, 46L, 46L, 47L, 47L, 2L, 1L, 3L, 3L, 4L, 4L, 7L, 11L, 11L, 16L, 16L, 20L, 20L, 21L, 21L, 17L, 17L, 18L, 18L, 19L, 19L, 26L, 26L, 27L, 27L, 30L, 30L, 31L, 31L, 32L, 32L, 33L, 33L, 34L, 34L, 48L), .Label = c("Tree_112", "Tree_112 ", "Tree_115", "Tree_130", "Tree_137", "Tree_139", "Tree_140", "Tree_141", "Tree_153", "Tree_154", "Tree_156", "Tree_159", "Tree_166", "Tree_169", "Tree_171", "Tree_180", "Tree_182", "Tree_184", "Tree_185", "Tree_202", "Tree_213", "Tree_218", "Tree_222", "Tree_227", "Tree_239", "Tree_242", "Tree_246", "Tree_247", "Tree_252", "Tree_260", "Tree_267", "Tree_269", "Tree_271", "Tree_272", "Tree_291", "Tree_293", "Tree_298", "Tree_327", "Tree_329", "Tree_336", "Tree_350", "Tree_401", "Tree_403", "Tree_405", "Tree_407", "Tree_409", "Tree_420", "Tree_851"), class = "factor"), city = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Atlanta", "Augusta", "Jacksonville"), class = "factor"), plot = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("High-N", "Low-N"), class = "factor"), width = c(0.66, 0.716, 0.682, 0.645, 0.645, 0.696, 0.733,
0.707, 0.668, 0.686, 0.617, 0.733, 0.73, 0.615, 0.669, 0.746, 0.687, 0.682, 0.76, 0.713, 0.651, 0.664, 0.679, 0.729, 0.756,
0.669, 0.647, 0.713, 0.767, 0.685, 0.69, 0.731, 0.781, 0.729,
0.725, 0.739, 0.769, 0.791, 0.676, 0.688, 0.719, 0.753, 0.748,
0.791, 0.785, 0.78, 0.723, 0.756, 0.664, 0.645, 0.653, 0.615,
0.591, 0.642, 0.693, 0.716, 0.694, 0.676, 0.662, 0.629, 0.665,
0.748, 0.726, 0.693, 0.715, 0.714, 0.764, 0.732, 0.61, 0.721,
0.703, 0.713, 0.746, 0.752, 0.662, 0.733, 0.707, 0.674, 0.734,
0.79, 0.732, 0.794, 0.703, 0.712, 0.737, 0.731, 0.747, 0.746,
0.787, 0.709, 0.716, 0.764, 0.77, 0.764, 0.802, 0.663, 0.777,
0.642, 0.779, 0.81, 0.724, 0.645, 0.68, 0.637, 0.695, 0.768,
0.761, 0.7, 0.759, 0.726, 0.696, 0.794, 0.774, 0.799, 0.747,
0.606, 0.691, 0.733, 0.707, 0.698, 0.706, 0.72, 0.694, 0.697,
0.737, 0.716, 0.73, 0.706, 0.667, 0.734, 0.528, 0.695, 0.684,
0.763, 0.733, 0.809, 0.6, 0.676, 0.718, 0.759, 0.609, 0.665,
0.667, 0.647, 0.701, 0.663, 0.688, 0.693, 0.899)), .Names = c("tree.id", "city", "plot", "width"), class = "data.frame", row.names = c(NA, -149L))
Thank you all so much for your comments, I sincerely appreciate everyone's help!

As suggested in comments, a line plot might not make sense for your data, as you are studying how width varies in discrete categories (in separate cities and separate plots). Boxplots would make sense as you can make them for each of the interactions of city and plot. To give you a sense of what you can do I generated some fake data and made an example of the sort of plot that might be helpful to you:
# fake data
leaf <- data.frame(tree.id = rep(1:50, each = 3),
city = rep(c("Jackson", "Augusta", "Atlanta"), each = 50),
plot = rep(1:6, each = 25))
# I'll make the average of width different for each plot
leaf$width <- rnorm(nrow(leaf), leaf$plot, 1)
# plotting the data
library(ggplot2) # this is a great library for plotting in R
ggplot(leaf, aes(x = factor(plot), y = width, color = factor(plot))) +
facet_grid(~city, scales = 'free_x') + # This creates a subplot for each city
geom_boxplot() +
geom_point(position = "jitter") +
theme_bw()
In this plot I added the points (the leaf widths for each individual tree) but I 'jittered' them, meaning perturbing their position slightly so that they do not pile up on top of each other and are all visible. You could remove this if you liked.
Exploratory data analysis should be fun! And I think visualization is a good place to start when beginning in statistics. Hopefully this will prove helpful to you.

leaf.width.model <- lme(width ~ city*plot, (1|tree.id), data=leaf)
In this model if you want to plot something, you are probably trying to answer:
How much is the average leaf width for all trees in each city for each type of plot.
To show this information in a figure, you need to plot width on y axis plot plot(high and low nitrogen) on x axis and group the data by city. Then you will get the 3 lines you are taking about. However, you need to get the average width in each group as you only want to show city variation.
To get this plot from raw data: (Using fake data provided by gfgm)
set.seed(100)
leaf <- data.frame(tree.id = rep(1:50, each = 3),
city = rep(c("Jackson", "Augusta", "Atlanta"), each = 50),
plot = rep(c(1, 0), each = 25))
# I'll make the average of width different for each plot
leaf$width <- rnorm(nrow(leaf), leaf$plot, 1)
library(plotly)
library(tidyverse)
leaf %>%
group_by(city,plot) %>%
summarise(avwidth = mean(width, na.rm=T),
avsd = 1.96*sd(width, na.rm=T)/sqrt(25)) %>%
plot_ly(x = ~plot, y = ~avwidth, color= ~city,
type="scatter", mode="markers+lines",
error_y = ~list(array=avsd)
)

[r]: Interpreting results of a glmer, retransforming estimates

EDIT:
I am currently writing my master thesis on the effect of a certain insecticide on bumble bee colonies. I was for example checking if damaged/diseased appearing bees were more prevalent in colonies that were exposed to the insecticide compared to the control.
The study design is hierarchical. 16 fields were paired according to landscape characteristics. In each pair one field was randomly assigned to be treated with the insecticide, while the other is the control field. In each field there are 2 boxes and in each box are 2 bumble bee hives. From each hive I have up to ten pupae per sex.
This is how my data looks like:
structure(list(pair = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L
), .Label = c("P01", "P02", "P03", "P04", "P05", "P10", "P11",
"P12"), class = "factor"), field = structure(c(6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 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, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 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, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L), .Label = c("VR02", "VR03",
"VR04", "VR05", "VR06", "VR07", "VR09", "VR12", "VR13", "VR14",
"VR16", "VR17", "VR18", "VR20", "VR21", "VR23"), class = "factor"),
treatment = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 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, 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, 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, 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,
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, 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), .Label = c("Clothianidin", "Control"), class = "factor"),
box.nested = c(11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12,
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 23, 23, 23,
23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24,
24, 24, 24, 24, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3,
3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 19, 19, 19, 19, 19, 19,
19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
20, 20, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
26, 26, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14,
31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32,
32, 32, 32, 32, 15, 15, 15, 15, 15, 16, 16, 16, 18, 18, 18,
18, 18, 18, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18,
5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 21, 21, 21, 21, 21, 21, 21,
21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22,
22, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 10, 10, 10, 10, 10, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10,
10, 10, 10, 10, 10, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28,
28, 28, 28, 28, 28, 28, 28, 28, 30, 30, 30, 30, 29, 29, 29,
29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30,
30, 30, 30, 30, 30, 30, 30, 30, 30), hive.nested = c(21L,
21L, 21L, 21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L,
23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 45L, 45L,
45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 46L, 46L, 48L,
48L, 48L, 48L, 48L, 48L, 48L, 48L, 48L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 3L, 3L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 37L, 37L, 37L, 37L, 38L, 38L, 38L,
38L, 38L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 40L, 40L, 40L,
40L, 40L, 40L, 40L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L,
49L, 49L, 49L, 50L, 50L, 51L, 52L, 25L, 25L, 25L, 26L, 26L,
26L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 61L, 61L, 61L, 61L,
61L, 62L, 62L, 62L, 62L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 30L, 30L, 30L, 30L, 30L, 32L, 32L, 32L, 36L,
36L, 36L, 36L, 36L, 36L, 34L, 34L, 34L, 34L, 34L, 34L, 34L,
34L, 35L, 35L, 35L, 36L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 41L, 41L,
41L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 43L, 43L,
43L, 43L, 43L, 43L, 44L, 44L, 44L, 44L, 44L, 13L, 14L, 14L,
14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 16L, 16L, 16L, 16L, 16L, 16L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 19L, 20L, 20L, 20L, 20L, 17L, 17L, 17L, 17L,
17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L,
19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 53L, 53L, 53L, 53L,
54L, 54L, 54L, 54L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 56L,
56L, 56L, 60L, 60L, 60L, 60L, 57L, 57L, 57L, 57L, 57L, 57L,
57L, 58L, 58L, 58L, 58L, 58L, 58L, 59L, 59L, 59L, 59L, 59L,
59L, 59L, 59L, 60L, 60L, 60L, 60L, 60L, 60L), stage = structure(c(2L,
1L, 1L, 3L, 1L, 1L, 1L, 1L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L,
1L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, 1L,
3L, 2L, 3L, 1L, 1L, 2L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 2L, 2L,
2L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 2L,
3L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 2L, 3L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 3L, 2L,
2L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 3L, 1L, 3L, 2L, 3L, 3L, 1L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 1L,
3L, 1L, 2L, 1L, 1L, 3L, 3L, 3L, 2L, 1L, 3L, 1L, 3L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 1L, 2L, 3L,
3L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 3L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 3L, 3L, 1L, 2L,
3L, 2L, 1L, 2L, 3L, 1L, 2L, 2L, 1L, 1L, 3L), .Label = c("1",
"2", "3"), class = "factor"), condition = structure(c(2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 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, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L), .Label = c("d",
"h"), class = "factor"), sex = structure(c(2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("f", "m", "q"
), class = "factor"), diseased = c(0, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0,
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0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,
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1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0)), .Names = c("pair",
"field", "treatment", "box.nested", "hive.nested", "stage", "condition",
"sex", "diseased"), class = "data.frame", row.names = c(5L, 7L,
8L, 9L, 10L, 14L, 15L, 16L, 21L, 23L, 24L, 26L, 28L, 29L, 30L,
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680L, 681L, 682L, 684L, 685L, 686L, 687L, 688L, 689L, 690L))
I have run binomial glmer models from the lme4 package to test whether the presence of disease/damage signs in bumble bee colonies is affected by the insecticide.
damage.prev <- glmer(diseased ~ treatment + sex + stage
+ (1|pair/field/box.nested/hive.nested)
,data=df.cocoons.white,
family=binomial)
I have been trying to get estimates and confidence intervals. Thanks to #Benjamin
I got a little closer to the solution, but the estimates sill seem too high.
That's how I tried to get a data.frame of CIs and estimates:
fixed <- fixef(damage.prev)
wald <-confint(damage.prev,method="Wald")
estCloth.damage.ratio <- exp(fixed[1])
estCont.damage.ratio <- exp(fixed[1] + fixed[2])
lwrCloth.damage.ratio <- exp(wald[1,1])
lwrCont.damage.ratio <- exp(wald[1,1] + wald[2,1])
uprCloth.damage.ratio <- exp(wald[1,2])
uprCont.damage.ratio <- exp(wald[1,2] + wald[2,2])
estCloth.damage <- estCloth.damage.ratio/ (1+estCloth.damage.ratio)
estCont.damage <- estCont.damage.ratio / (1+ estCont.damage.ratio)
lwrCloth.damage <- lwrCloth.damage.ratio/ (1+ lwrCloth.damage.ratio)
lwrCont.damage <- lwrCont.damage.ratio /(1+ lwrCont.damage.ratio)
uprCloth.damage <- uprCloth.damage.ratio /(1+uprCloth.damage.ratio)
uprCont.damage <- uprCont.damage.ratio /(1+uprCont.damage.ratio )
treat.damage <- data.frame(Treatment,Estimate,lwr,upr)
What still confuses me are the high estimates of beyond 94%, yet
sum(df.cocoons.white$diseased)/length(df.cocoons.white$diseased)
gives me less than 70%. Doesn't seem realistic. Any idea what might be wrong?

Your model is using a logit transformation.
The way I look at generalized linear models is that they really aren't any different than simple linear regression. In simple linear regression, your response variable is continuous on (theoretically) the entire real number line (-Inf, Inf).
In logistic regression, your response is a proportion, which is continuous on the interval [0, 1]. The odds calculates (p / (1-p)) which is continuous over the interval of [0, inf). The log odds log(p / (1-p)) is continuous over the interval (-Inf, Inf).
This complete transformation (log(p / (1-p))) is referred to as the logit transformation and is pretty standard in logistic regression.
The results of your glmer model, which is a random effects version of logistic regression, uses the same transformation and so the estimated coefficients are on the scale of (-Inf, Inf). If you want odds ratios, you can exponentiate the coefficients, which will give you the odds measured on a scale of (0, Inf), with 1.0 being the null value.

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