ggplot2 add breaks/intervals to discrete variable in an animated plot - r

Context: I'm trying to make an animated line plot where I compare the levels of Resting Blood Pressure (Y variable - The graph says cholesterol, but it should say Resting Blood Pressure) as the Age increases (x variable) in function of the patient having heart disease or not (0 for no heart disease and 1 for heart disease).
The plot has a lot of "spikes," so I'd like to create some classes/breaks to level things out a bit, but I'm not sure how I could do it given that age is a discrete variable. I was trying to add something like scale_x_discrete() and then adding a sequence like seq(10,80,10) but it didn't work. I was also taking a look at using cut with Sturges rules, but once again that's used for continuous variables.
This is the code:
p <- ggplot(
df,
aes(Age, RestingBP, group = HeartDisease, color = HeartDisease)
) +
geom_line() +
scale_color_viridis_d() +
scale_alpha_manual(values=classes) +
labs(x = "Age", y = "Cholesterol") +
theme(legend.position = "top")
p + transition_reveal(Age)
This is the graph:
Thank you in advance!
Some data:
structure(list(Age = c(40L, 49L, 37L, 48L, 54L, 39L, 45L, 54L,
37L, 48L, 37L, 58L, 39L, 49L, 42L, 54L, 38L, 43L, 60L, 36L, 43L,
44L, 49L, 44L, 40L, 36L, 53L, 52L, 53L, 51L, 53L, 56L, 54L, 41L,
43L, 32L, 65L, 41L, 48L, 48L, 54L, 54L, 35L, 52L, 43L, 59L, 37L,
50L, 36L, 41L), Gr_etario = structure(c(2L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L), .Label = c("Menos de 40 anos",
"Entre 40 e 60 anos", "Acima de 60 anos"), class = "factor"),
Sex = structure(c(2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("F",
"M"), class = "factor"), ChestPainType = structure(c(2L,
3L, 2L, 1L, 3L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 3L, 2L,
1L, 2L, 1L, 2L, 4L, 2L, 2L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 3L,
3L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 3L, 2L, 3L, 1L, 3L,
1L, 2L, 3L, 1L), .Label = c("ASY", "ATA", "NAP", "TA"), class = "factor"),
RestingBP = c(140L, 160L, 130L, 138L, 150L, 120L, 130L, 110L,
140L, 120L, 130L, 136L, 120L, 140L, 115L, 120L, 110L, 120L,
100L, 120L, 100L, 120L, 124L, 150L, 130L, 130L, 124L, 120L,
113L, 125L, 145L, 130L, 125L, 130L, 150L, 125L, 140L, 110L,
120L, 150L, 150L, 130L, 150L, 140L, 120L, 130L, 120L, 140L,
112L, 110L), Cholesterol = c(289, 180, 283, 214, 195, 339,
237, 208, 207, 284, 211, 164, 204, 234, 211, 273, 196, 201,
248, 267, 223, 184, 201, 288, 215, 209, 260, 284, 468, 188,
518, 167, 224, 172, 186, 254, 306, 250, 177, 227, 230, 294,
264, 259, 175, 318, 223, 216, 340, 289), FastingBS = 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, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), .Label = c("0", "1"), class = "factor"),
RestingECG = structure(c(2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L,
3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("LVH",
"Normal", "ST"), class = "factor"), MaxHR = c(172L, 156L,
98L, 108L, 122L, 170L, 170L, 142L, 130L, 120L, 142L, 99L,
145L, 140L, 137L, 150L, 166L, 165L, 125L, 160L, 142L, 142L,
164L, 150L, 138L, 178L, 112L, 118L, 127L, 145L, 130L, 114L,
122L, 130L, 154L, 155L, 87L, 142L, 148L, 130L, 130L, 100L,
168L, 170L, 120L, 120L, 168L, 170L, 184L, 170L), ExerciseAngina = structure(c(1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 1L), .Label = c("N", "Y"), class = "factor"),
Oldpeak = c(0, 1, 0, 1.5, 0, 0, 0, 0, 1.5, 0, 0, 2, 0, 1,
0, 1.5, 0, 0, 1, 3, 0, 1, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 2,
2, 0, 0, 1.5, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0), ST_Slope = structure(c(3L,
2L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L,
2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 2L,
3L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L,
3L, 3L, 2L, 2L), .Label = c("Down", "Flat", "Up"), class = "factor"),
HeartDisease = structure(c(1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L), .Label = c("0",
"1"), class = "factor")), row.names = c(NA, 50L), class = "data.frame")

Related

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,
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, 4L, 4L, 4L, 4L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 4L,
4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 3L, 3L,
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,
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, 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, 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, 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, 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, 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,
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, 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.

Is it possible to table() two columns in a way that display the sum horizontally and vertically?

I have
> table(y$treatment,y$WHO)
1 2 3 4
SSTR 43 34 20 27
SSA 23 28 25 0
Control 0 0 0 0
I am looking for a way to display the sum of each row and column generated by table(). Thus, the expected output is:
1 2 3 4
SSTR 43 34 20 27 124
SSA 23 28 25 0 76
Control 0 0 0 0 0
66 62 45 27
I am particularly interested in dplyr-solutions.
My data
y <- structure(list(treatment = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 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), .Label = c("SSTR", "SSA", "Control"), class = "factor"),
WHO = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L,
2L, 3L, 3L, 3L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 4L,
4L, 1L, 4L, 1L, 2L, 1L, 4L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 2L,
1L, 4L, 1L, 4L, 4L, 4L, 4L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 4L,
4L, 2L, 1L, 2L, 2L, 4L, 4L, 4L, 2L, 4L, 1L, 4L, 4L, 2L, 4L,
4L, 3L, 4L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 3L, 2L, 2L,
2L, 3L, 2L, 3L, 1L, 2L, 3L, 3L, 3L, 1L, 2L, 1L, 2L, 3L, 3L,
1L, 3L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 2L, 3L, 1L, 1L, 2L, 1L,
1L, 3L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 3L, 2L, 3L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 1L,
3L, 3L, 3L, 2L, 3L, 2L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 3L, 2L, 3L, 1L, 1L, 1L, 1L), .Label = c("1",
"2", "3", "4"), class = "factor")), na.action = structure(c(`152` = 152L,
`193` = 193L), class = "omit"), row.names = 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, 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, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L,
97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L,
108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L,
119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L,
130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L,
141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L,
153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L,
164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L,
175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L,
186L, 187L, 188L, 189L, 190L, 191L, 192L, 194L, 195L, 196L, 197L,
198L, 199L, 200L, 201L, 202L), class = "data.frame")

A possible solution to consider:
x<-table(y$treatment,y$WHO)
rowcoltotals <- addmargins(x, FUN = list(Total = sum), quiet = TRUE)
output:
1 2 3 4 Total
SSTR 43 34 20 27 124
SSA 23 28 25 0 76
Control 0 0 0 0 0
Total 66 62 45 27 200

Mixed Design ANOVA-Error: object of type 'closure' is not subsettable

I'am running a 2X3X3 mixed ANOVA design. Ive been trying to get the student-newman Kuels function to work in R, but I keep getting the error: object of type 'closure' is not subsettable. Any help would be greatly appreciated!
library(ez)
library(ggplot2)
library(nlme)
library(pastecs)
library(reshape)
library(WRS)
library(GAD)
library(multcomp)
library(psych)
library(lmerTest)
CPPData <- read.table(file = "CPPdatar.csv", header=TRUE, sep = ",")
str(CPPData)
CPPData$Test<-as.factor(CPPData$Test)
CPPData$Exposure<-as.factor(CPPData$Exposure)
CPPData$Dose<-as.factor(CPPData$Dose)
CPPData$Subject<-as.factor(CPPData$Subject)
levels(CPPData$Test)<-list("Habit"=1, "Test of Conditioning"=2)
levels(CPPData$Exposure)<-list("0% HFCS CONT"=1, "50% HFCS CONT"=2, "50% HFCS INT"=3)
levels(CPPData$Dose)<-list("0 OXY"=1, "0.16 OXY"=2, "2.5 OXY"=3)
str(CPPData)
options(contrasts=c("contr.helmert", "contr.poly"))
aov1<-aov(Time~Exposure*Dose*Test+ Error(Subject/(Test)), data=CPPData)
summary(aov1)
aov2 = lme(Time~Exposure + Dose + Test,
random = ~1|Subject,
data = CPPData,
method = "ML")
summary(aov2)
#SNK Test
Test2<-snk.test(lm(Time~Exposure*Test*Dose, data = CPPData))
Test1<-snk.test(lm, term = Exposure*Test*Dose, among = Exposure, within = Test*Dose)
snk.test(aov2, term = "Dose:Exposure:Test", among = "Test", within = "Dose")
Error issues:
SNK Test
Test2<-snk.test(lm(Time~ExposureTestDose, data = CPPData))
Error in estimates(object) :
Design unbalanced! This function can only handle balanced designs.
Test1<-snk.test(lm, term = ExposureTestDose, among = Exposure, within = Test*Dose)
Error: object of type 'closure' is not subsettable
> snk.test(aov2, term = "Dose:Exposure:Test", among = "Test", within = "Dose)
Error in object$model[, 2:(length(object$x) + 1)] :
incorrect number of dimensions
Dput
dput(CPPData)
structure(list(Time = c(476.98, 436.94, 451.79, 514.68, 548.38,
457.96, 489.99, 536.7, 517.02, 566.9, 487.15, 553.89, 527.86,
580.41, 310.14, 508.84, 364.7, 456.79, 616.12, 598.43, 447.45,
570.9, 520.35, 812.48, 756.92, 667.5, 603.77, 547.55, 369.2,
437.6, 296.96, 568.74, 580.91, 526.19, 582.92, 568.23, 576.74,
378.04, 549.38, 548.55, 492.99, 388.72, 581.75, 538.71, 511.51,
895.89, 851.68, 685.85, 741.24, 738.4, 662.5, 485.32, 746.75,
726.89, 638.64, 613.11, 819.65, 475.14, 599.93, 668.34, 486.99,
426.26, 570.74, 482.48, 460.13, 578.08, 541.37, 465.63, 297.13,
543.04, 560.89, 536.04, 419.25, 555.89, 587.75, 530.2, 619.95,
778.61, 602.27, 403.74, 792.63, 815.98, 599.77, 784.28, 543.04,
748.08, 610.94, 700.87, 710.38, 686.02, 735.57, 805.47, 525.02,
524.86, 482.65, 278.28, 547.21, 559.23, 450.95, 579.91, 174.17,
312.64, 683.02, 790.46, 422.92, 664, 435.44, 478.14, 427.43,
308.31, 342.84, 517.02, 541.54, 565.73, 474.81, 522.02, 503.34,
321.99, 498.16, 554.55, 527.69, 776.44, 966.47, 867.2, 846.68,
554.22, 750.75, 435.44, 769.94, 561.39, 544.71, 404.24, 464.8,
482.48, 550.88, 459.79, 552.05, 389.56, 361.86, 814.15, 907.41,
755.92, 729.9, 945.11, 509.34, 529.86, 611.78, 675.84, 452.79,
584.42, 566.4, 420.75, 465.46, 533.87, 579.58, 492.16, 521.86,
565.07, 594.76, 280.95, 563.23, 537.37, 409.07, 439.44, 547.88,
543.88, 496.66, 474.14, 433.43, 418.75, 541.37, 406.74, 477.98,
576.58, 335.5, 405.74, 319.49, 482.15, 501, 409.07, 610.28, 667.17,
560.56, 474.14, 467.3, 561.56, 718.88, 584.25, 689.52, 507.51,
711.04, 520.19, 357.52, 373.04, 504, 360.86, 470.97, 489.49,
397.06, 582.41, 554.05, 481.15, 776.78, 658.66, 721.55, 433.93,
532.03, 625.79, 651.48, 783.78, 729.06, 828.5), Test = structure(c(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, 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, 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, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 1L, 1L, 2L, 2L, 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), .Label = c("Habit", "Test of Conditioning"), class = "factor"),
Exposure = 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, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("0% HFCS CONT",
"50% HFCS CONT", "50% HFCS INT"), class = "factor"), Dose = 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, 2L,
2L, 2L, 2L, 2L, 2L, 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, 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, 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, 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), .Label = c("0 OXY", "0.16 OXY", "2.5 OXY"), class = "factor"),
Subject = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 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, 27L, 28L, 29L, 30L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L,
41L, 42L, 43L, 44L, 45L, 46L, 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, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 66L, 67L, 68L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 82L, 83L, 84L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L,
83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L,
95L, 96L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L,
95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L,
106L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L,
106L), .Label = c("1", "2", "3", "4", "5", "6", "7", "8",
"9", "10", "11", "12", "13", "14", "15", "16", "17", "18",
"19", "20", "21", "22", "23", "24", "25", "26", "27", "28",
"29", "30", "31", "32", "33", "34", "35", "36", "37", "38",
"39", "40", "41", "42", "43", "44", "45", "46", "47", "48",
"49", "50", "51", "52", "53", "54", "55", "56", "57", "58",
"59", "60", "61", "62", "63", "64", "65", "66", "67", "68",
"69", "70", "71", "72", "73", "74", "75", "76", "77", "78",
"79", "80", "81", "82", "83", "84", "85", "86", "87", "88",
"89", "90", "91", "92", "93", "94", "95", "96", "97", "98",
"99", "100", "101", "102", "103", "104", "105", "106"), class = "factor")), row.names = c(NA,
-212L), class = "data.frame")

The cause of the error you were asking about can be seen here:
Test1 <- snk.test(lm, term = ExposureTestDose, ....
The letters "lm" are the name of a function and you gave that to a function that was expecting a data object that could be subsetted. The first call did give the snk.test function a data object created by lm, but it was stopped by code that gave a different reason.
As far as that other errors go, it appears you need some statistical advice. The snk.test does not appear to be designed for the type of analysis you are running. You might consider asking for help about design issues on hte CrossValidated.com site. (It's another StackExchange forum that handles statistical questions.)

R interaction.plot

I want to draw an interaction.plot for my data.
with(data, interaction.plot(biofeedback, diet, as.numeric(drug),
xlab="Biofeedback", ylab="Drug", ylim = c(0, 4), col = 2:3))
Result:
But clearly that's not correct because my data is:
## biofeedback diet drug pressure
## 1 present absent 1 170
## 2 present absent 1 175
## 3 present absent 1 165
...
## 7 present present 1 161
## 8 present present 1 173
## 9 present present 1 157
...
## 19 present present 2 164
## 20 present present 2 166
## 21 present present 2 159
...
This data has different drugs for absent and present Biofeedback for present diet. And it has some drugs for absent diet. None of this shows on the plot. Why?
dput(data)
## structure(list(biofeedback = 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, 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, 1L, 1L, 1L,
## 1L, 1L, 1L), .Label = c("absent", "present"), class = "factor"),
## diet = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
## 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
## 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
## 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
## 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
## 2L, 2L), .Label = c("absent", "present"), class = "factor"),
## drug = structure(c(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, 3L, 3L, 3L, 3L, 3L, 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, 3L, 3L, 3L,
## 3L, 3L), .Label = c("1", "2", "3"), class = "factor"), pressure = c(170L,
## 175L, 165L, 180L, 160L, 158L, 161L, 173L, 157L, 152L, 181L,
## 190L, 186L, 194L, 201L, 215L, 219L, 209L, 164L, 166L, 159L,
## 182L, 187L, 174L, 180L, 187L, 199L, 170L, 204L, 194L, 162L,
## 184L, 183L, 156L, 180L, 173L, 173L, 194L, 197L, 190L, 176L,
## 198L, 164L, 190L, 169L, 164L, 176L, 175L, 189L, 194L, 217L,
## 206L, 199L, 195L, 171L, 173L, 196L, 199L, 180L, 203L, 202L,
## 228L, 190L, 206L, 224L, 204L, 205L, 199L, 170L, 160L, 179L,
## 179L)), .Names = c("biofeedback", "diet", "drug", "pressure"
## ), row.names = c(NA, -72L), class = "data.frame")

It looks like there is no error. The average for each group is 2.0, which is what the plot shows.
> df$drug <- as.numeric(levels(df$drug))[df$drug]
> library(plyr)
> ddply(df, .(biofeedback, diet), summarise, val = mean(drug))
biofeedback diet val
1 absent absent 2
2 absent present 2
3 present absent 2
4 present present 2

Plotting lmer model without covariance matrix

I am trying to plot a number of lmer models for a paper. I had to simplify the random effect structure by dropping the correlation between the random slopes and intercept (Barr et al., 2013). However, when I try to plot using the sjp.lmer funtion, I get the following error:
Error in array(NA, c(J, K)) : 'dims' cannot be of length 0
In addition: Warning message:
In ranef.merMod(object, condVar = TRUE) :
conditional variances not currently available via ranef when there are multiple terms per factor
Is there a potential work-around for this? Any help would be greatly appreciated.
Hi Ben,
Here is some of the data I am working with:
> dput(df)
structure(list(Subject = c(1L, 2L, 3L, 5L, 6L, 6L, 6L, 7L, 7L,
7L, 8L, 8L, 8L, 9L, 9L, 9L, 10L, 10L, 11L, 11L, 11L, 12L, 12L,
13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L, 16L, 17L, 17L, 17L, 18L,
18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L, 23L, 23L, 23L,
24L, 24L, 25L, 25L, 25L, 26L, 26L, 26L, 27L, 27L, 28L, 28L, 29L,
29L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L,
41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L,
67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L,
80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L,
105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L,
116L), A = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1",
"2"), class = "factor"), B = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L), .Label = c("1", "2", "3"), class = "factor"), C = c(9.58,
9.75, 15, 10.75, 13.3, 14.42, 15.5, 9.25, 10.33, 11.33, 9.55,
11, 11.92, 14.25, 15.5, 16.42, 14.92, 16.17, 10.83, 11.92, 12.92,
7.5, 8.5, 10.33, 11.25, 13.08, 13.83, 14.92, 15.92, 9.58, 14.83,
11.92, 8.33, 9.5, 10.5, 6.8, 7.92, 9, 13.5, 10.92, 10, 11, 13,
15.58, 12.92, 11.8, 5.75, 6.75, 7.83, 11.12, 12.25, 12.08, 13.08,
14.58, 8.08, 9.17, 10.67, 10.6, 12.67, 7.83, 8.83, 9.67, 10.58,
11.75, 7, 17.17, 11.25, 13.75, 11.83, 16.92, 8.83, 7.07, 7.83,
15.08, 15.83, 16.67, 18.87, 11.92, 12.83, 7.83, 12.33, 10, 11.08,
12.08, 15.67, 11.75, 15, 14.308, 15.9064, 16.161, 16.9578, 8.90197,
16.2897, 9.05805, 10.5969, 5.15334, 9.1046, 14.1019, 18.9736,
10.9447, 14.5455, 16.172, 6.65389, 11.3171, 12.2864, 17.9929,
10.5778, 16.9195, 7.6, 7.8, 7.2, 16.7, 17, 16.5, 17, 15.1, 16,
16.4, 13.8, 13.8, 14.5, 16.1, 15.8, 15, 14.1, 15, 14.7, 15, 14.5,
10.8, 11.4, 11.3, 10.9, 11.2, 9.3, 10.8, 9.7, 8, 8.2, 8.2, 17.5,
12.6, 11.6, 10.8, 11.8, 12.3, 16.3, 17.1, 9.626283368, 14.6,
13.7), D = structure(c(2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1",
"2"), class = "factor"), Frontal_FA = c(0.4186705, 0.4151535,
0.4349945, 0.4003705, 0.403488, 0.407451, 0.3997135, 0.38826,
0.3742275, 0.3851655, 0.3730715, 0.3825115, 0.3698805, 0.395406,
0.39831, 0.4462415, 0.413532, 0.419088, 0.4373975, 0.4633915,
0.4411375, 0.3545255, 0.389322, 0.349402, 0.352029, 0.367792,
0.365298, 0.3790775, 0.379298, 0.36231, 0.3632755, 0.357868,
0.3764865, 0.3726645, 0.351422, 0.3353255, 0.334196, 0.3462365,
0.367369, 0.3745925, 0.3610755, 0.360576, 0.357035, 0.3554905,
0.3745615, 0.38828, 0.3293275, 0.3246945, 0.3555345, 0.375563,
0.38116, 0.387508, 0.357707, 0.413193, 0.3658075, 0.3776355,
0.362678, 0.3824945, 0.3771, 0.375347, 0.362468, 0.367618, 0.3630925,
0.3763995, 0.359458, 0.3982755, 0.3834765, 0.386135, 0.3691575,
0.388099, 0.350435, 0.3629045, 0.3456775, 0.4404815, 0.4554165,
0.425763, 0.4491515, 0.461206, 0.453745, 0.4501255, 0.4451875,
0.4369835, 0.456838, 0.437759, 0.4377635, 0.44434, 0.4436615,
0.437532, 0.4335325, 0.4407995, 0.470447, 0.4458525, 0.440322,
0.4570775, 0.4410335, 0.436045, 0.4721345, 0.4734515, 0.4373905,
0.4139465, 0.440213, 0.440281, 0.425746, 0.454377, 0.4457435,
0.488561, 0.4393565, 0.4610565, 0.3562055, 0.381041, 0.353253,
0.4265975, 0.4069595, 0.40092, 0.4261365, 0.429605, 0.425479,
0.4331755, 0.3981285, 0.4206245, 0.3798475, 0.3704155, 0.395192,
0.404436, 0.4148915, 0.416144, 0.384652, 0.3916045, 0.41005,
0.3940605, 0.3926085, 0.383909, 0.391792, 0.372398, 0.3531025,
0.414441, 0.404335, 0.3682095, 0.359976, 0.376681, 0.4173705,
0.3492685, 0.397057, 0.3940605, 0.398825, 0.3707115, 0.400228,
0.3946595, 0.4278775, 0.384037, 0.43577)), .Names = c("Subject",
"A", "B", "C", "D", "Frontal_FA"), class = "data.frame", row.names = c(NA,
-151L))
Here is the code that I am running
lmer fit
FA <- lmer(Frontal_FA ~ poly(C) + A + B + D + (poly(C)||Subject), data = df)
plot lmer fit
sjp.lmer(FA)
Thanks for your help.

sjp.lmer, by default, plots the random effects of a model. However, it plots random effects (BLUPs) with confidence intervals, using the arm:se.ranef function. This function causes the first error message you get:
arm::se.ranef(FA)
> Error in array(NA, c(J, K)) : 'dims' cannot be of length 0
Then, the se.ranef functions calls the lme4::ranef function with argument condVar = TRUE, which is not yet implemented for specific conditions (like yours) in lme4. Hence you get the additional warning
In ranef.merMod(object, condVar = TRUE) :
conditional variances not currently available via ranef when there are multiple terms per factor
If you are especially interested in plotting the random effects, you could use the lme4-implemented dotplot-function:
lattice::dotplot(ranef(FA))
If you are interested in any other plot type (fixed effects, marginal effects, predictions, ...), see ?sjp.lmer or some examples at his page.
Edit
If you don't mind installing from GitHub (devtools::install_github("sjPlot/devel"), I have committed a small update, so you can use show.ci = FALSE to avoid computing confidence intervals for random effects:
sjp.lmer(FA, type = "re", show.ci = F, sort.est = "(Intercept)")

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