ggplot2 - Survival Swimmer Plot Not Displaying Sorted Values - r

Using this question I've been assembling a customized Swimmer Plot, but despite my having sorted the patients by Therapy.Length, when I try to plot it the second half of the bars often come out much longer than they should be - putting the graph out of order.
I've checked and the symbols are in the correct place even when the bar is too long, so somehow the bar lengths are getting extended. By commenting out scale_y_continuous chunk, I'm able to get the proper length on the bars, but then all of the symbol placement is compressed.
ggplot(dat,
aes(Patient.ID, Therapy.Length)) +
geom_bar(stat="identity", aes(fill=factor(Disease.Stage)), width=0.7) +
geom_point(data=dat, aes(Patient.ID, value, colour=variable, shape=variable), size=4) +
geom_segment(data=dat %>% filter(Continued == 1),
aes(x=Patient.ID, xend=Patient.ID, y=Therapy.Length + 0.1, yend=Therapy.Length + 20),
arrow=arrow(type="closed", length=unit(0.13,"in"))) +
coord_flip() +
scale_fill_manual(values=hcl(seq(15,375,length.out=5)[1:4],100,70)) +
scale_colour_manual(values=c(hcl(seq(15,375,length.out=3)[1:2],100,40),c("black","darkgreen"))) +
scale_y_continuous(limits=c(-1,max(dat$Therapy.Length)), breaks=seq(0,max(dat$Therapy.Length),30)) +
labs(fill="Disease Stage", colour="", shape="", x="Subject Received Study Drug")
+ theme_bw() +
theme(panel.grid.minor=element_blank(),panel.grid.major=element_blank(),
axis.text.y=element_blank(),axis.ticks.y=element_blank())
Here's the rest of the code: [x]
ANSWER:
dat had originally been melted – changed dat to be unmelted (and created dat.m to be the melted version), and supplied dat.mto geom_point and geom_segment for adding points to bars.
data:
structure(list(Patient.ID = 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, 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), .Label = c("13",
"5", "14", "11", "6", "2", "7", "12", "3", "8", "1", "10", "4",
"15", "9"), class = "factor"), Disease.Stage = c(1L, 2L, 4L,
1L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 1L, 3L, 2L, 3L, 1L, 2L, 4L, 1L,
2L, 3L, 1L, 1L, 3L, 2L, 1L, 1L, 3L, 2L, 3L, 1L, 2L, 4L, 1L, 2L,
3L, 1L, 1L, 3L, 2L, 1L, 1L, 3L, 2L, 3L, 1L, 2L, 4L, 1L, 2L, 3L,
1L, 1L, 3L, 2L, 1L, 1L, 3L, 2L, 3L), Response.Start = c(15, 40,
51, 26, 36, 26, 32, 43, 67, 80, 93, 105, 160, 34, 94, 15, 40,
51, 26, 36, 26, 32, 43, 67, 80, 93, 105, 160, 34, 94, 15, 40,
51, 26, 36, 26, 32, 43, 67, 80, 93, 105, 160, 34, 94, 15, 40,
51, 26, 36, 26, 32, 43, 67, 80, 93, 105, 160, 34, 94), Therapy.Length = c(74,
84, 84, 108, 128, 185, 194, 198, 257, 274, 293, 311, 325, 336,
357, 74, 84, 84, 108, 128, 185, 194, 198, 257, 274, 293, 311,
325, 336, 357, 74, 84, 84, 108, 128, 185, 194, 198, 257, 274,
293, 311, 325, 336, 357, 74, 84, 84, 108, 128, 185, 194, 198,
257, 274, 293, 311, 325, 336, 357), Continued = c(1, 1, 1, NA,
1, NA, 1, NA, 1, NA, 1, NA, 1, NA, 1, 1, 1, 1, NA, 1, NA, 1,
NA, 1, NA, 1, NA, 1, NA, 1, 1, 1, 1, NA, 1, NA, 1, NA, 1, NA,
1, NA, 1, NA, 1, 1, 1, 1, NA, 1, NA, 1, NA, 1, NA, 1, NA, 1,
NA, 1), variable = 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, 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), .Label = c("Response.End", "Durable", "Complete",
"Partial"), class = "factor"), value = c(40, 78, 78, 106, 89,
177, 108, 101, 138, 209, 279, 244, 311, 167, 271, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, -1, NA, NA, NA, NA, NA, 40, NA, NA,
NA, NA, 32, 43, NA, NA, 93, NA, 160, NA, 94, 15, NA, 51, 26,
36, 26, NA, NA, 67, 80, NA, 105, NA, 34, NA)), row.names = c(NA,
-60L), .Names = c("Patient.ID", "Disease.Stage", "Response.Start",
"Therapy.Length", "Continued", "variable", "value"), class = "data.frame")

Related

My interaction term doesn't cover all groups

I am trying to execute linear-mixed models with 3 factors as groups. However running the syntax, I just get 2 terms: time:grup_int2 or time:grup_int3 as you can see in the picture. There should be another term for time:grup_int1. My reference factor group is the level 1, however I don't know how to relevel in the loop, and I intended to post a example with a database provided by the package. The problem is with the database provided by the package works... and I get 3 terms
My code with genes_wide dataframe (plenty of NAs)
#pivot wider to make horizontal data
genes_wide <- genes1 %>% pivot_wider(names_from = gen, values_from = dCt)
genes_wide$time [genes_wide$time == "1"] <- 1
genes_wide$time [genes_wide$time == "3"] <- 3
genes_wide$time<-as.numeric(genes_wide$time)
# if they need to be relevel
#genes_wide$grup_int <- relevel(genes_wide$grup_int, "2")
# names of variables
genes.names <- colnames(genes_wide)[13:67]
no.genes <- length(genes.names)
# create a named list to hold the fitted models
gene.list <- as.list(1:no.genes)
names(gene.list) <- genes.names
# loop over gene names
for(i in genes.names){
# create temporary data matrix and model formula
#1st: adjusting per several variables (edad0 = numeric, grup_int = factor, sexo = factor, time = numeric, id = numeric (need to change?)
#2nd: adjusting just per group
tmp <- genes_wide[, c(i, "grup_int","time", "id", "edad0", "sexo")]
fml <- as.formula( paste( i, "~", paste(c("time", "time:grup_int", "edad0", "sexo"), collapse="+")))
gene.list[[i]] <- lme(fml, random= ~ time|id , method="REML", data=tmp, control = lmeControl(opt = "optim"), na.action = na.omit)
}
# List of data.frames/tibble
result <- lapply(gene.list, function(x) tidy(x))
# Solution 2
result <- dplyr::bind_rows(result, .id = "var")
result2 <- result %>% dplyr::select( var, term, p.value)
**
Example database
**
However running the exact syntax with an example database (+ 5 variables to iterate) from the nlme package, the result is different
#adding variables to df problem
BodyWeight$var1 <- rnorm(176, 47 ,3)
BodyWeight$var2 <- rnorm(176, 50 ,10)
BodyWeight$var3 <- rnorm(176, 68, 7)
BodyWeight$var4 <- rnorm(176, 150 ,10)
BodyWeight$var5 <- rnorm(176, 140, 7)
# names of variables
var.names <- colnames(BodyWeight)[5:9]
no.var <- length(var.names)
# create a named list to hold the fitted models
var.list <- as.list(1:no.var)
names(var.list) <- var.names
# loop over gene names
for(i in var.names){
#2nd: adjusting just per group
tmp <- BodyWeight[, c(i, "Diet","Time", "Rat", "weight")]
fml <- as.formula( paste( i, "~", paste(c("Time:Diet", "weight"), collapse="+")))
var.list[[i]] <- lme(fml, random= ~ Time|Rat , method="REML", data=tmp, control = lmeControl(opt = "optim"), na.action = na.omit)
}
# List of data.frames/tibble
result <- lapply(var.list, function(x) tidy(x))
# Solution
result <- dplyr::bind_rows(result, .id = "var")
result2 <- result %>% dplyr::select( var, term, p.value)
As you can see I get 3 terms:
Time:Diet1
Time:Diet2
Time:Diet3
My original database is made up by more variables, but time is numeric, as well as grouping variables are factor (grup_int and Diet respectively). The original database contains NAs
I was wondering if it is possible to relevel to get the interaction remaining
UPDATE
I don't know how to put in words, what is happening when I get this:
What does it mean? time:grup_int1 is significant but time:grup_int2 not? Is time:grup_int1 the meaning for statistical differences between group 1 ant the others groups (2 and 3) across time ? It doesn't make any sense to be significant for one but not for the others. How can I get an overall p-value comparison between groups?
The df used for the loop
structure(list(weight = c(240, 250, 255, 260, 262, 258, 266,
266, 265, 272, 278, 225, 230, 230, 232, 240, 240, 243, 244, 238,
247, 245, 245, 250, 250, 255, 262, 265, 267, 267, 264, 268, 269,
260, 255, 255, 265, 265, 268, 270, 272, 274, 273, 275, 255, 260,
255, 270, 270, 273, 274, 273, 276, 278, 280, 260, 265, 270, 275,
275, 277, 278, 278, 284, 279, 281, 275, 275, 260, 270, 273, 274,
276, 271, 282, 281, 284, 245, 255, 260, 268, 270, 265, 265, 267,
273, 274, 278, 410, 415, 425, 428, 438, 443, 442, 446, 456, 468,
478, 405, 420, 430, 440, 448, 460, 458, 464, 475, 484, 496, 445,
445, 450, 452, 455, 455, 451, 450, 462, 466, 472, 555, 560, 565,
580, 590, 597, 595, 595, 612, 618, 628, 470, 465, 475, 485, 487,
493, 493, 504, 507, 518, 525, 535, 525, 530, 533, 535, 540, 525,
530, 543, 544, 559, 520, 525, 530, 540, 543, 546, 538, 544, 553,
555, 548, 510, 510, 520, 515, 530, 538, 535, 542, 550, 553, 569
), Time = c(1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64), Rat = structure(c(4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L), levels = c("2", "3", "4", "1", "8", "5", "6", "7", "11",
"9", "10", "12", "13", "15", "14", "16"), class = c("ordered",
"factor")), Diet = 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,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 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), levels = c("1", "2", "3"), class = "factor"),
var1 = c(46.804, 45.792, 43.572, 50.587, 52.268, 45.402,
45.996, 48.566, 47.933, 48.336, 43.964, 52.441, 45.077, 45.699,
51.348, 47.888, 50.245, 48.111, 42.373, 44.231, 46.461, 45.639,
44.993, 50.391, 47.997, 48.368, 44.939, 47.805, 49.195, 51.419,
50.242, 45.856, 48.115, 48.053, 44.318, 50.493, 40.48, 47.418,
49.441, 49.414, 49.776, 49.043, 41.456, 48.52, 46.079, 43.058,
43.649, 43.967, 44.799, 48.081, 50.728, 51.207, 50.01, 47.346,
43.816, 52.575, 47.962, 49.061, 45.438, 43.822, 43.022, 48.864,
41.669, 45.58, 42.206, 42.788, 43.988, 46.636, 45.651, 42.502,
42.93, 49.196, 48.314, 46.412, 49.436, 47.193, 48.806, 45.142,
48.4, 48.419, 47.937, 42.605, 46.027, 45.325, 43.811, 45.922,
47.262, 46.566, 47.016, 46.111, 50.573, 47.436, 47.859, 47.839,
48.239, 46.762, 46.581, 55.9, 42.816, 49.636, 47.514, 39.489,
46.513, 49.323, 51.504, 45.558, 42.402, 45.153, 48.632, 47.908,
46.033, 44.963, 49.312, 45.733, 51.149, 45.755, 47.247, 46.674,
49.121, 43.411, 43.735, 44.777, 44.942, 48.207, 50.962, 47.1,
44.167, 50.007, 48.531, 46.749, 47.69, 47.396, 45.074, 49.203,
42.869, 47.713, 46.186, 52.191, 46.809, 47.578, 45.365, 53.228,
45.896, 45.428, 42.702, 47.328, 46.108, 49.857, 45.128, 43.772,
46.479, 51.573, 51.255, 45.33, 45.651, 48.521, 49.006, 51.906,
48.924, 53.693, 48.555, 48.367, 49.659, 47.839, 47.829, 48.212,
45.867, 51.965, 48.275, 49.091, 44.54, 44.978, 50.431, 48.915,
51.569, 46.008), var2 = c(53.273, 35.7, 44.406, 62.644, 42.971,
49.393, 52.585, 30.572, 52.156, 58.566, 44.631, 50.967, 48.583,
60.878, 50.712, 48.741, 47.584, 54.774, 62.288, 49.952, 33.225,
39.251, 53.158, 46.74, 46.796, 40.343, 58.491, 48.12, 41.854,
55.315, 44.553, 55.674, 47.404, 56.085, 53.443, 56.382, 74.641,
44.822, 73.191, 46.409, 66.453, 60.929, 51.394, 56.812, 53.244,
48.993, 54.763, 35.624, 63.346, 38.389, 48.288, 61.422, 45.707,
50.213, 42.629, 64.381, 39.218, 58.923, 61.569, 39.279, 54.748,
63.149, 54.639, 56.494, 66.907, 36.303, 52.297, 46.288, 62.852,
53.063, 43.096, 70.983, 49.175, 43.663, 44.456, 55.17, 51.383,
56.945, 51.471, 49.053, 63.062, 46.386, 48.094, 43.282, 35.509,
48.079, 48.223, 34.605, 42.669, 40.146, 53.089, 40.911, 39.396,
39.507, 41.481, 41.729, 28.367, 62.781, 59.154, 50.788, 84.282,
58.214, 53.404, 46.312, 36.983, 61.712, 53.109, 37.285, 50.834,
32.306, 47.969, 36.232, 46.744, 37.876, 50.369, 59.684, 40.427,
44.2, 46.955, 56.388, 46.667, 46.373, 49.932, 60.3, 27.732,
56.772, 54.569, 45.164, 37.747, 57.827, 49.998, 23.109, 53.339,
51.024, 58.875, 55.315, 40.637, 37.781, 47.209, 57.51, 46.956,
66.715, 51.034, 52.54, 60.342, 34.439, 58.08, 64.298, 56.374,
64.829, 49.392, 48.939, 38.746, 40.048, 46.856, 61.697, 51.253,
54.024, 61.166, 45.834, 43.578, 39.212, 67.632, 35.208, 23.676,
50.577, 46.832, 42.866, 57.808, 53.292, 42.474, 62.779, 34.747,
55.367, 27.928, 57.481), var3 = c(67.483, 67.977, 70.749,
76.851, 77.351, 59.821, 67.203, 72.635, 67.767, 67.627, 64.729,
70.388, 71.889, 61.95, 54.182, 71.049, 56.33, 68.982, 70.615,
68.416, 60.661, 73.205, 68.086, 72.611, 62.082, 74.026, 69.073,
67.488, 79.656, 79.612, 71.016, 61.284, 76.297, 62.713, 79.335,
68.155, 67.184, 73.889, 61.59, 68.273, 57.056, 66.95, 60.504,
77.121, 73.056, 60.739, 75.891, 74.363, 82.829, 86.902, 59.38,
77.63, 67.613, 72.666, 68.903, 71.631, 68.86, 57.653, 65.119,
60.825, 63.301, 64.945, 65.89, 80.092, 79.951, 65.475, 66.491,
72.355, 71.909, 70.339, 83.508, 59.686, 62.102, 67.291, 52.908,
70.508, 63.2, 66.62, 75.138, 72.311, 66.394, 64.812, 74.063,
51.087, 55.095, 56.906, 55.88, 71.916, 81.805, 67.339, 66.459,
70.005, 82.891, 65.456, 86.611, 65.401, 72.647, 59.198, 73.558,
66.536, 69.83, 77.101, 65.494, 62.129, 71.86, 85.406, 70.619,
79.225, 71.806, 62.86, 79.615, 65.049, 63.472, 63.46, 80.215,
74.033, 58.949, 69.519, 66.39, 68.751, 69.356, 75.201, 54.815,
50.592, 76.019, 62.177, 66.677, 69.495, 82.428, 70.743, 59.458,
75.326, 69.615, 67.157, 69.793, 66.205, 75.328, 76.311, 64.36,
62.207, 74.16, 51.799, 66.378, 67.964, 66.937, 59.779, 59.735,
64.992, 66.817, 60.595, 65.388, 50.404, 61.742, 65.789, 77.047,
56.86, 69.756, 66.338, 57.149, 63.407, 73.601, 66.042, 67.421,
51.208, 47.265, 69.465, 63.471, 75.845, 74.938, 69.009, 81.807,
81.195, 64.288, 77.085, 64.698, 65.747), var4 = c(143.048,
139.381, 152.849, 163.726, 155.712, 146.957, 140.231, 148.321,
141.917, 156.873, 165.845, 158.824, 157.305, 144.521, 145.253,
134.892, 153.597, 124.482, 153.096, 134.954, 171.238, 147.43,
150.983, 129.945, 150.652, 142.259, 156.631, 152.215, 152.196,
153.54, 139.712, 168.495, 152.043, 148.538, 164.067, 172.445,
144.175, 144.015, 149.093, 135.921, 156.787, 148.292, 144.958,
137.332, 148.185, 151.678, 160.774, 168.479, 175.839, 148.211,
163.747, 145.416, 131.502, 154.997, 152.35, 125.955, 167.116,
141.734, 135.958, 162.752, 139.482, 149.315, 144.425, 144.654,
160.05, 153.767, 148.532, 143.102, 135.553, 143.305, 153.021,
127.982, 165.047, 144.142, 136.568, 145.793, 143.913, 158.002,
154.043, 167.63, 165.472, 143.681, 166.417, 152.012, 145.843,
144.448, 138.782, 141.475, 150.717, 140.765, 164.657, 133.655,
151.908, 160.084, 138.25, 155.93, 142.756, 170.935, 128.346,
149.986, 154.099, 141.688, 150.616, 149.999, 139.195, 160.921,
148.261, 144.611, 154.183, 157.2, 130.512, 155.004, 125.064,
124.813, 150.074, 145.615, 152.238, 144.241, 149.079, 159.354,
153.398, 146.609, 155.032, 145.522, 143.296, 139.502, 148.944,
143.446, 155.094, 148.208, 154.417, 153.249, 154.544, 140.746,
156.798, 146.069, 134.905, 145.318, 137.198, 142.087, 137.117,
151.782, 147.245, 148.583, 152.535, 151.265, 152.272, 162.014,
150.055, 154.343, 139.717, 148.752, 140.933, 146.761, 145.311,
146.361, 155.264, 154.984, 168.623, 129.31, 147.492, 139.828,
161.22, 145.89, 139.146, 138.104, 148.543, 155.836, 143.594,
159.32, 163.648, 152.058, 160.412, 133, 155.305, 144.094),
var5 = c(135.794, 136.92, 131.548, 132.091, 143.587, 130.393,
133.678, 143.005, 136.036, 128.161, 134.036, 144.237, 125.767,
133.32, 137.047, 140.3, 137.92, 144.883, 140.901, 140.632,
136.315, 138.138, 140.855, 124.721, 143.153, 144.514, 142.092,
144.765, 139.205, 134.905, 141.815, 122.992, 138.249, 125.918,
125.236, 136.503, 137.462, 130.757, 135.141, 135.413, 134.991,
137.143, 153.329, 148.779, 138.249, 133.978, 132.454, 139.955,
122.84, 135.984, 140.941, 134.203, 146.091, 146.766, 136.259,
151.474, 139.683, 132.913, 142.39, 134.625, 124.717, 145.74,
135.647, 135.119, 144.182, 133.961, 130.387, 141.314, 129.26,
127.154, 154.74, 142.327, 142.23, 139.433, 148.245, 132.123,
127.374, 134.518, 133.422, 143.66, 129.306, 138.31, 147.787,
137.415, 138.686, 143.896, 140.973, 128.592, 139.53, 146.29,
145, 129.926, 132.338, 155.193, 132.286, 140.86, 135.343,
137.037, 140.147, 131.872, 137.858, 142.137, 131.587, 121.455,
142.264, 138.533, 138.769, 136.296, 141.055, 133.475, 148.136,
136.175, 143.947, 152.026, 137.649, 142.466, 144.819, 153.745,
138.95, 138.146, 133.404, 147.928, 145.928, 146.421, 145.156,
134.289, 127.955, 148.223, 129.987, 139.295, 141.035, 150.867,
136.412, 139.705, 142.013, 129.377, 143.521, 145.595, 142.936,
137.703, 133.344, 144.46, 143.001, 137.017, 149.985, 144.242,
158.029, 151.835, 133.262, 123.893, 136.338, 143.951, 136.71,
136.007, 147.102, 134.472, 128.058, 145.521, 142.379, 135.869,
144.135, 141.297, 149.133, 134.008, 138.566, 117.773, 151.701,
140.288, 144.17, 140.886, 151.585, 136.438, 150.399, 140.535,
142.601, 136.449)), row.names = c(NA, 176L), class = c("nfnGroupedData",
"nfGroupedData", "groupedData", "data.frame"), outer = ~Diet, formula = weight ~
Time | Rat, labels = list(x = "Time", y = "Body weight"), units = list(
x = "(days)", y = "(g)"), FUN = structure(function (x)
max(x, na.rm = TRUE), source = "function(x) max(x, na.rm = TRUE)"), order.groups = TRUE)

The problem (i.e., difference between what you get in your original model and what you get in the other example you posted) is not because of the levelling of the factors, but because of a difference in model specification. In the first model results you posted you include Time + Time:Diet - the "main effect" of Time and the product regressors with Diet. However in the second set of models - the ones with the dummy data you provided - you do not include the main effect in the model. This is what produces the difference. I show the difference below. First, load the data.
BodyWeight <- structure(list(weight = c(240, 250, 255, 260, 262, 258, 266,
266, 265, 272, 278, 225, 230, 230, 232, 240, 240, 243, 244, 238,
247, 245, 245, 250, 250, 255, 262, 265, 267, 267, 264, 268, 269,
260, 255, 255, 265, 265, 268, 270, 272, 274, 273, 275, 255, 260,
255, 270, 270, 273, 274, 273, 276, 278, 280, 260, 265, 270, 275,
275, 277, 278, 278, 284, 279, 281, 275, 275, 260, 270, 273, 274,
276, 271, 282, 281, 284, 245, 255, 260, 268, 270, 265, 265, 267,
273, 274, 278, 410, 415, 425, 428, 438, 443, 442, 446, 456, 468,
478, 405, 420, 430, 440, 448, 460, 458, 464, 475, 484, 496, 445,
445, 450, 452, 455, 455, 451, 450, 462, 466, 472, 555, 560, 565,
580, 590, 597, 595, 595, 612, 618, 628, 470, 465, 475, 485, 487,
493, 493, 504, 507, 518, 525, 535, 525, 530, 533, 535, 540, 525,
530, 543, 544, 559, 520, 525, 530, 540, 543, 546, 538, 544, 553,
555, 548, 510, 510, 520, 515, 530, 538, 535, 542, 550, 553, 569
), Time = c(1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15,
22, 29, 36, 43, 44, 50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44,
50, 57, 64, 1, 8, 15, 22, 29, 36, 43, 44, 50, 57, 64), Rat = structure(c(4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L), levels = c("2", "3", "4", "1", "8", "5", "6", "7", "11",
"9", "10", "12", "13", "15", "14", "16"), class = c("ordered",
"factor")), Diet = 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,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 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), levels = c("1", "2", "3"), class = "factor"),
var1 = c(46.804, 45.792, 43.572, 50.587, 52.268, 45.402,
45.996, 48.566, 47.933, 48.336, 43.964, 52.441, 45.077, 45.699,
51.348, 47.888, 50.245, 48.111, 42.373, 44.231, 46.461, 45.639,
44.993, 50.391, 47.997, 48.368, 44.939, 47.805, 49.195, 51.419,
50.242, 45.856, 48.115, 48.053, 44.318, 50.493, 40.48, 47.418,
49.441, 49.414, 49.776, 49.043, 41.456, 48.52, 46.079, 43.058,
43.649, 43.967, 44.799, 48.081, 50.728, 51.207, 50.01, 47.346,
43.816, 52.575, 47.962, 49.061, 45.438, 43.822, 43.022, 48.864,
41.669, 45.58, 42.206, 42.788, 43.988, 46.636, 45.651, 42.502,
42.93, 49.196, 48.314, 46.412, 49.436, 47.193, 48.806, 45.142,
48.4, 48.419, 47.937, 42.605, 46.027, 45.325, 43.811, 45.922,
47.262, 46.566, 47.016, 46.111, 50.573, 47.436, 47.859, 47.839,
48.239, 46.762, 46.581, 55.9, 42.816, 49.636, 47.514, 39.489,
46.513, 49.323, 51.504, 45.558, 42.402, 45.153, 48.632, 47.908,
46.033, 44.963, 49.312, 45.733, 51.149, 45.755, 47.247, 46.674,
49.121, 43.411, 43.735, 44.777, 44.942, 48.207, 50.962, 47.1,
44.167, 50.007, 48.531, 46.749, 47.69, 47.396, 45.074, 49.203,
42.869, 47.713, 46.186, 52.191, 46.809, 47.578, 45.365, 53.228,
45.896, 45.428, 42.702, 47.328, 46.108, 49.857, 45.128, 43.772,
46.479, 51.573, 51.255, 45.33, 45.651, 48.521, 49.006, 51.906,
48.924, 53.693, 48.555, 48.367, 49.659, 47.839, 47.829, 48.212,
45.867, 51.965, 48.275, 49.091, 44.54, 44.978, 50.431, 48.915,
51.569, 46.008), var2 = c(53.273, 35.7, 44.406, 62.644, 42.971,
49.393, 52.585, 30.572, 52.156, 58.566, 44.631, 50.967, 48.583,
60.878, 50.712, 48.741, 47.584, 54.774, 62.288, 49.952, 33.225,
39.251, 53.158, 46.74, 46.796, 40.343, 58.491, 48.12, 41.854,
55.315, 44.553, 55.674, 47.404, 56.085, 53.443, 56.382, 74.641,
44.822, 73.191, 46.409, 66.453, 60.929, 51.394, 56.812, 53.244,
48.993, 54.763, 35.624, 63.346, 38.389, 48.288, 61.422, 45.707,
50.213, 42.629, 64.381, 39.218, 58.923, 61.569, 39.279, 54.748,
63.149, 54.639, 56.494, 66.907, 36.303, 52.297, 46.288, 62.852,
53.063, 43.096, 70.983, 49.175, 43.663, 44.456, 55.17, 51.383,
56.945, 51.471, 49.053, 63.062, 46.386, 48.094, 43.282, 35.509,
48.079, 48.223, 34.605, 42.669, 40.146, 53.089, 40.911, 39.396,
39.507, 41.481, 41.729, 28.367, 62.781, 59.154, 50.788, 84.282,
58.214, 53.404, 46.312, 36.983, 61.712, 53.109, 37.285, 50.834,
32.306, 47.969, 36.232, 46.744, 37.876, 50.369, 59.684, 40.427,
44.2, 46.955, 56.388, 46.667, 46.373, 49.932, 60.3, 27.732,
56.772, 54.569, 45.164, 37.747, 57.827, 49.998, 23.109, 53.339,
51.024, 58.875, 55.315, 40.637, 37.781, 47.209, 57.51, 46.956,
66.715, 51.034, 52.54, 60.342, 34.439, 58.08, 64.298, 56.374,
64.829, 49.392, 48.939, 38.746, 40.048, 46.856, 61.697, 51.253,
54.024, 61.166, 45.834, 43.578, 39.212, 67.632, 35.208, 23.676,
50.577, 46.832, 42.866, 57.808, 53.292, 42.474, 62.779, 34.747,
55.367, 27.928, 57.481), var3 = c(67.483, 67.977, 70.749,
76.851, 77.351, 59.821, 67.203, 72.635, 67.767, 67.627, 64.729,
70.388, 71.889, 61.95, 54.182, 71.049, 56.33, 68.982, 70.615,
68.416, 60.661, 73.205, 68.086, 72.611, 62.082, 74.026, 69.073,
67.488, 79.656, 79.612, 71.016, 61.284, 76.297, 62.713, 79.335,
68.155, 67.184, 73.889, 61.59, 68.273, 57.056, 66.95, 60.504,
77.121, 73.056, 60.739, 75.891, 74.363, 82.829, 86.902, 59.38,
77.63, 67.613, 72.666, 68.903, 71.631, 68.86, 57.653, 65.119,
60.825, 63.301, 64.945, 65.89, 80.092, 79.951, 65.475, 66.491,
72.355, 71.909, 70.339, 83.508, 59.686, 62.102, 67.291, 52.908,
70.508, 63.2, 66.62, 75.138, 72.311, 66.394, 64.812, 74.063,
51.087, 55.095, 56.906, 55.88, 71.916, 81.805, 67.339, 66.459,
70.005, 82.891, 65.456, 86.611, 65.401, 72.647, 59.198, 73.558,
66.536, 69.83, 77.101, 65.494, 62.129, 71.86, 85.406, 70.619,
79.225, 71.806, 62.86, 79.615, 65.049, 63.472, 63.46, 80.215,
74.033, 58.949, 69.519, 66.39, 68.751, 69.356, 75.201, 54.815,
50.592, 76.019, 62.177, 66.677, 69.495, 82.428, 70.743, 59.458,
75.326, 69.615, 67.157, 69.793, 66.205, 75.328, 76.311, 64.36,
62.207, 74.16, 51.799, 66.378, 67.964, 66.937, 59.779, 59.735,
64.992, 66.817, 60.595, 65.388, 50.404, 61.742, 65.789, 77.047,
56.86, 69.756, 66.338, 57.149, 63.407, 73.601, 66.042, 67.421,
51.208, 47.265, 69.465, 63.471, 75.845, 74.938, 69.009, 81.807,
81.195, 64.288, 77.085, 64.698, 65.747), var4 = c(143.048,
139.381, 152.849, 163.726, 155.712, 146.957, 140.231, 148.321,
141.917, 156.873, 165.845, 158.824, 157.305, 144.521, 145.253,
134.892, 153.597, 124.482, 153.096, 134.954, 171.238, 147.43,
150.983, 129.945, 150.652, 142.259, 156.631, 152.215, 152.196,
153.54, 139.712, 168.495, 152.043, 148.538, 164.067, 172.445,
144.175, 144.015, 149.093, 135.921, 156.787, 148.292, 144.958,
137.332, 148.185, 151.678, 160.774, 168.479, 175.839, 148.211,
163.747, 145.416, 131.502, 154.997, 152.35, 125.955, 167.116,
141.734, 135.958, 162.752, 139.482, 149.315, 144.425, 144.654,
160.05, 153.767, 148.532, 143.102, 135.553, 143.305, 153.021,
127.982, 165.047, 144.142, 136.568, 145.793, 143.913, 158.002,
154.043, 167.63, 165.472, 143.681, 166.417, 152.012, 145.843,
144.448, 138.782, 141.475, 150.717, 140.765, 164.657, 133.655,
151.908, 160.084, 138.25, 155.93, 142.756, 170.935, 128.346,
149.986, 154.099, 141.688, 150.616, 149.999, 139.195, 160.921,
148.261, 144.611, 154.183, 157.2, 130.512, 155.004, 125.064,
124.813, 150.074, 145.615, 152.238, 144.241, 149.079, 159.354,
153.398, 146.609, 155.032, 145.522, 143.296, 139.502, 148.944,
143.446, 155.094, 148.208, 154.417, 153.249, 154.544, 140.746,
156.798, 146.069, 134.905, 145.318, 137.198, 142.087, 137.117,
151.782, 147.245, 148.583, 152.535, 151.265, 152.272, 162.014,
150.055, 154.343, 139.717, 148.752, 140.933, 146.761, 145.311,
146.361, 155.264, 154.984, 168.623, 129.31, 147.492, 139.828,
161.22, 145.89, 139.146, 138.104, 148.543, 155.836, 143.594,
159.32, 163.648, 152.058, 160.412, 133, 155.305, 144.094),
var5 = c(135.794, 136.92, 131.548, 132.091, 143.587, 130.393,
133.678, 143.005, 136.036, 128.161, 134.036, 144.237, 125.767,
133.32, 137.047, 140.3, 137.92, 144.883, 140.901, 140.632,
136.315, 138.138, 140.855, 124.721, 143.153, 144.514, 142.092,
144.765, 139.205, 134.905, 141.815, 122.992, 138.249, 125.918,
125.236, 136.503, 137.462, 130.757, 135.141, 135.413, 134.991,
137.143, 153.329, 148.779, 138.249, 133.978, 132.454, 139.955,
122.84, 135.984, 140.941, 134.203, 146.091, 146.766, 136.259,
151.474, 139.683, 132.913, 142.39, 134.625, 124.717, 145.74,
135.647, 135.119, 144.182, 133.961, 130.387, 141.314, 129.26,
127.154, 154.74, 142.327, 142.23, 139.433, 148.245, 132.123,
127.374, 134.518, 133.422, 143.66, 129.306, 138.31, 147.787,
137.415, 138.686, 143.896, 140.973, 128.592, 139.53, 146.29,
145, 129.926, 132.338, 155.193, 132.286, 140.86, 135.343,
137.037, 140.147, 131.872, 137.858, 142.137, 131.587, 121.455,
142.264, 138.533, 138.769, 136.296, 141.055, 133.475, 148.136,
136.175, 143.947, 152.026, 137.649, 142.466, 144.819, 153.745,
138.95, 138.146, 133.404, 147.928, 145.928, 146.421, 145.156,
134.289, 127.955, 148.223, 129.987, 139.295, 141.035, 150.867,
136.412, 139.705, 142.013, 129.377, 143.521, 145.595, 142.936,
137.703, 133.344, 144.46, 143.001, 137.017, 149.985, 144.242,
158.029, 151.835, 133.262, 123.893, 136.338, 143.951, 136.71,
136.007, 147.102, 134.472, 128.058, 145.521, 142.379, 135.869,
144.135, 141.297, 149.133, 134.008, 138.566, 117.773, 151.701,
140.288, 144.17, 140.886, 151.585, 136.438, 150.399, 140.535,
142.601, 136.449)), row.names = c(NA, 176L), class = c("nfnGroupedData",
"nfGroupedData", "groupedData", "data.frame"), outer = ~Diet, formula = weight ~
Time | Rat, labels = list(x = "Time", y = "Body weight"), units = list(
x = "(days)", y = "(g)"), FUN = structure(function (x)
max(x, na.rm = TRUE), source = "function(x) max(x, na.rm = TRUE)"), order.groups = TRUE)
Here is the model without the main effect of Time. You can see, you get the desired result - coefficients for each value of Diet.
library(name)
mod0 <- lme(var1 ~ Time:Diet + weight, random= ~ Time|Rat , method="REML", data=BodyWeight, control = lmeControl(opt = "optim"), na.action = na.omit)
summary(mod0)
#> Linear mixed-effects model fit by REML
#> Data: BodyWeight
#> AIC BIC logLik
#> 905.3992 933.6741 -443.6996
#>
#> Random effects:
#> Formula: ~Time | Rat
#> Structure: General positive-definite, Log-Cholesky parametrization
#> StdDev Corr
#> (Intercept) 0.73280912 (Intr)
#> Time 0.02632007 -0.912
#> Residual 2.73566273
#>
#> Fixed effects: var1 ~ Time:Diet + weight
#> Value Std.Error DF t-value p-value
#> (Intercept) 46.94072 1.3142431 156 35.71692 0.0000
#> weight 0.00016 0.0033845 156 0.04685 0.9627
#> Time:Diet1 -0.00470 0.0160669 156 -0.29282 0.7701
#> Time:Diet2 0.00369 0.0194774 156 0.18956 0.8499
#> Time:Diet3 0.02582 0.0213189 156 1.21119 0.2277
#> Correlation:
#> (Intr) weight Tm:Dt1 Tm:Dt2
#> weight -0.940
#> Time:Diet1 -0.698 0.497
#> Time:Diet2 0.311 -0.533 0.109
#> Time:Diet3 0.422 -0.635 0.026 0.620
#>
#> Standardized Within-Group Residuals:
#> Min Q1 Med Q3 Max
#> -2.75835293 -0.70479591 0.02987446 0.58694850 3.08058470
#>
#> Number of Observations: 176
#> Number of Groups: 16
Now, let's look at the model specified with the Time main effect as well. Here, you can see that you get the Time coefficient and one for all but the reference level of Diet interacted with Time. The omission comes because Time = Time:Diet1 + Time:Diet2 + Time:Diet3, so these four terms are perfectly collinear.
mod1 <- lme(var1 ~ Time + Time:Diet + weight, random= ~ Time|Rat , method="REML", data=BodyWeight, control = lmeControl(opt = "optim"), na.action = na.omit)
summary(mod1)
#> Linear mixed-effects model fit by REML
#> Data: BodyWeight
#> AIC BIC logLik
#> 905.3992 933.6741 -443.6996
#>
#> Random effects:
#> Formula: ~Time | Rat
#> Structure: General positive-definite, Log-Cholesky parametrization
#> StdDev Corr
#> (Intercept) 0.73280912 (Intr)
#> Time 0.02632007 -0.912
#> Residual 2.73566273
#>
#> Fixed effects: var1 ~ Time + Time:Diet + weight
#> Value Std.Error DF t-value p-value
#> (Intercept) 46.94072 1.3142431 156 35.71692 0.0000
#> Time -0.00470 0.0160669 156 -0.29282 0.7701
#> weight 0.00016 0.0033845 156 0.04685 0.9627
#> Time:Diet2 0.00840 0.0238626 156 0.35188 0.7254
#> Time:Diet3 0.03053 0.0263585 156 1.15811 0.2486
#> Correlation:
#> (Intr) Time weight Tm:Dt2
#> Time -0.698
#> weight -0.940 0.497
#> Time:Diet2 0.724 -0.585 -0.770
#> Time:Diet3 0.767 -0.588 -0.816 0.752
#>
#> Standardized Within-Group Residuals:
#> Min Q1 Med Q3 Max
#> -2.75835293 -0.70479591 0.02987446 0.58694850 3.08058470
#>
#> Number of Observations: 176
#> Number of Groups: 16
You could recover all the same information from the second model. The effect of Time for Diet1 is just the coefficient on Time. The effect of Time for Diet2 is the effect of Time plus the effect of Time:Diet2 and its standard error is the square root of the variance of the time effect, plus the variance of the interaction coefficient plus two times the covariance of the two coefficients (this comes from the equation for the calculation of the variance of a linear combination of random variables).
b <- fixef(mod1)
V <- vcov(mod1)
## Time:Diet1
d1 <- c(b[2], sqrt(V[2,2]))
d1 <- c(d1, d1[1]/d1[2])
d1 <- c(d1, 2*pt(abs(d1[3]), 156, lower.tail=FALSE))
## Time:Diet2
d2 <- c(b[2] + b[4], sqrt(V[2,2] + V[4,4] + 2*V[2,4]))
d2 <- c(d2, d2[1]/d2[2])
d2 <- c(d2, 2*pt(abs(d2[3]), 156, lower.tail=FALSE))
## Time:Diet2
d3 <- c(b[2] + b[5], sqrt(V[2,2] + V[5,5] + 2*V[2,5]))
d3 <- c(d3, d3[1]/d3[2])
d3 <- c(d3, 2*pt(abs(d3[3]), 156, lower.tail=FALSE))
d <- rbind(d1, d2, d3)
colnames(d) <- c("Estimate", "SE", "t-stat", "p-value")
rownames(d) <- c("Time:Diet1", "Time:Diet2", "Time:Diet3")
d
#> Estimate SE t-stat p-value
#> Time:Diet1 -0.004704694 0.01606695 -0.2928182 0.7700503
#> Time:Diet2 0.003692093 0.01947738 0.1895580 0.8499019
#> Time:Diet3 0.025821390 0.02131895 1.2111944 0.2276525
Compare this result with the one from the first model above you can see that they are the same:
#> Estimate SE DF t-stat p-value
#> Time:Diet1 -0.00470 0.0160669 156 -0.29282 0.7701
#> Time:Diet2 0.00369 0.0194774 156 0.18956 0.8499
#> Time:Diet3 0.02582 0.0213189 156 1.21119 0.2277
Created on 2023-02-01 by the reprex package (v2.0.1)
So, the answer to how you get a coefficient for the interaction of each level of Diet and Time is to leave out the main effect of Time. That said, I would encourage you to include the main effect of Diet in the model as well - this would allow the intercepts to vary according to Diet.

Simulating data from a fitted generalised linear binomial model

I have asked this question on the r-sig-mixed-models mailing list but have received no helpful answers, so I am trying here. Fingers crossed.
I apologise for the length/prolixity of my question, but the structure is complicated and I see no way to shorten my discussion.
Although this is not relevant to the question I am asking, my ultimate objective is to simulate new data from a fitted model, but with the experimental design changed from that of the
data set to which the model was fitted. The changes involve altering the number of replicates (replicates being the underlying random effect) and altering the number of binomial trials associated with each observation. The final goal is to examine, via simulation, the relative impact of the number of replicates and the number of binomial trials on the precision with which a certain (intricate) parameter is estimated.
I am working with the lme4 R package.
I had hoped to be able to adjust the experimental design from which the simulated data are generated by means of the "newdata" argument of the function simulate.merMod(), but I cannot get this to work. I am probably misunderstanding something. I am too slow and stupid to follow the help for simulate.merMod().
Since I could not accomplish my aim with simulate.merMod() I decided to try a roll-your-own approach. The problem here is that I need to be sure that this roll-your-own approach is correct. As a check, I tried comparing the roll-your-own results with results from simulate.merMod() (with seeds set appropriately so that the "random" results should be the same).
Basically my roll-your-own procedure consists of constructing a linear predictor using
* fitted fixed effect coefficients from the fitted model
* random effects simulated on the basis of the fitted
random effects variances and covarances
I then form probabilities as the logistic function of the linear
predictor, and finally use rbinom() to generate a random sample
from these probabilities and the desired "sizes".
I get agreement when there is a single random effect in the model, but I cannot get agreement when there are two random effects, and cannot see what I am doing wrong.
For the single random effect setting I made use of the "cbpp" data set from the lme4 package. The code that I used is as follows:
library(lme4)
fit <- glmer(cbind(incidence, size - incidence) ~ 0 + period + (1 | herd),
family = binomial, data = cbpp)
ccc <- getME(fit,"beta")
sigma <- getME(fit,"theta")
# Roll-your-own:
set.seed(101)
Z <- rnorm(length(levels(cbpp$herd)),0,sigma)
lnpr <- with(cbpp,ccc[period] + Z[herd])
p <- 1/(1+exp(-lnpr))
s.ryo <- rbinom(nrow(cbpp),cbpp$size,p)
# Using simulate.merMod:
set.seed(101)
s.mer <- simulate(fit)
s.mer <- s.mer[,1][,1]
# Check for equality:
print(all.equal(s.ryo,s.mer))
The two simulated results agree exactly.
However the setting in which I am really interested involves two random effects (random intercepts and random "slopes", i.e. random coefficients for the numeric predictor). Following is code that I tried to use in this
setting. In this case I cannot get the results from roll-your-own and simulate.merMod() to agree. The data set that I use in this case is not contained in the lme4 package.
This data set is provided after the illustrative code.
Sorry for the rather extreme length of the display.
I would appreciate any advice as to what I am doing wrong,
and comments as to whether my roll-your-own approach makes any sense at all.
Code:
library(lme4)
fit <- glmer(cbind(Good, Bad) ~ (trtmnt+0)/x + (x | batch),
family = binomial, data = Dat) # See below for "Dat"
ccc <- fixef(fit)
Sigma <- VarCorr(fit)[[1]]
# Roll-your-own:
set.seed(101)
nrep <- length(levels(Dat$batch))
Z <- MASS::mvrnorm(nrep,c(0,0),Sigma)
beta0 <- ccc[1:4][as.numeric(Dat$trtmnt)]
beta1 <- ccc[5:8][as.numeric(Dat$trtmnt)]
lnpr <- with(Dat,beta0 + beta1*x + Z[batch,1] + Z[batch,2]*x)
p <- 1/(1+exp(-lnpr)) # Inverse of logit link.
size <- with(Dat,Good+Bad)
s.ryo <- rbinom(nrow(Dat),size,p)
# Using simulate.merMod:
set.seed(101)
s.mer <- simulate(fit)
s.mer <- s.mer[,1][,1]
# The results are not equal.
Data:
Dat <- structure(list(Good = c(87, 137, 194, 211, 250, 259, 272, 277,
279, 279, 279, 279, 76, 134, 216, 229, 253, 264, 275, 282, 286,
287, 287, 287, 90, 109, 209, 219, 228, 245, 240, 244, 247, 247,
247, 247, 113, 147, 230, 257, 283, 287, 290, 295, 298, 298, 298,
298, 60, 105, 175, 203, 237, 250, 263, 268, 267, 268, 268, 268,
32, 71, 163, 184, 206, 220, 231, 242, 243, 245, 245, 245, 104,
138, 254, 265, 261, 284, 282, 283, 285, 286, 286, 286, 57, 89,
155, 193, 210, 214, 219, 229, 231, 231, 231, 231, 42, 71, 136,
154, 197, 211, 226, 228, 229, 229, 229, 229, 47, 69, 140, 167,
208, 215, 235, 241, 244, 246, 246, 246, 49, 79, 138, 179, 198,
213, 214, 220, 220, 221, 221, 221, 57, 85, 170, 186, 224, 221,
231, 232, 234, 235, 235, 235, 57, 92, 162, 185, 219, 234, 240,
243, 242, 243, 243, 243, 55, 95, 178, 205, 237, 255, 263, 274,
274, 276, 278, 278, 53, 81, 183, 236, 243, 279, 285, 290, 288,
291, 291, 291, 72, 101, 166, 209, 223, 219, 238, 236, 238, 238,
238, 238, 49, 77, 135, 171, 182, 195, 205, 211, 213, 214, 214,
214, 214, 28, 63, 123, 144, 160, 182, 187, 198, 201, 203, 204,
205, 205, 36, 64, 169, 183, 190, 206, 211, 213, 216, 217, 218,
218, 218, 58, 76, 163, 182, 194, 198, 204, 203, 207, 207, 208,
208, 208, 41, 82, 145, 163, 195, 213, 221, 226, 228, 229, 229,
229, 229, 42, 66, 121, 153, 179, 187, 203, 214, 216, 216, 218,
218, 218, 57, 71, 132, 185, 190, 198, 205, 207, 209, 210, 210,
210, 210, 43, 68, 132, 174, 186, 190, 199, 206, 206, 208, 208,
208, 208, 38, 65, 115, 126, 178, 190, 203, 211, 212, 213, 213,
213, 213, 34, 66, 124, 144, 183, 199, 224, 227, 232, 234, 235,
235, 235, 37, 81, 132, 166, 182, 190, 212, 218, 218, 220, 220,
220, 220, 42, 73, 129, 180, 207, 214, 242, 245, 247, 247, 248,
248, 248), Bad = c(192, 142, 85, 68, 29, 20, 7, 2, 0, 0, 0, 0,
211, 153, 71, 58, 34, 23, 12, 5, 1, 0, 0, 0, 157, 138, 38, 28,
19, 2, 7, 3, 0, 0, 0, 0, 185, 151, 68, 41, 15, 11, 8, 3, 0, 0,
0, 0, 208, 163, 93, 65, 31, 18, 5, 0, 1, 0, 0, 0, 213, 174, 82,
61, 39, 25, 14, 3, 2, 0, 0, 0, 182, 148, 32, 21, 25, 2, 4, 3,
1, 0, 0, 0, 174, 142, 76, 38, 21, 17, 12, 2, 0, 0, 0, 0, 187,
158, 93, 75, 32, 18, 3, 1, 0, 0, 0, 0, 199, 177, 106, 79, 38,
31, 11, 5, 2, 0, 0, 0, 172, 142, 83, 42, 23, 8, 7, 1, 1, 0, 0,
0, 178, 150, 65, 49, 11, 14, 4, 3, 1, 0, 0, 0, 186, 151, 81,
58, 24, 9, 3, 0, 1, 0, 0, 0, 223, 183, 100, 73, 41, 23, 15, 4,
4, 2, 0, 0, 238, 210, 108, 55, 48, 12, 6, 1, 3, 0, 0, 0, 166,
137, 72, 29, 15, 19, 0, 2, 0, 0, 0, 0, 165, 137, 79, 43, 32,
19, 9, 3, 1, 0, 0, 0, 0, 177, 142, 82, 61, 45, 23, 18, 7, 4,
2, 1, 0, 0, 182, 154, 49, 35, 28, 12, 7, 5, 2, 1, 0, 0, 0, 150,
132, 45, 26, 14, 10, 4, 5, 1, 1, 0, 0, 0, 188, 147, 84, 66, 34,
16, 8, 3, 1, 0, 0, 0, 0, 176, 152, 97, 65, 39, 31, 15, 4, 2,
2, 0, 0, 0, 153, 139, 78, 25, 20, 12, 5, 3, 1, 0, 0, 0, 0, 165,
140, 76, 34, 22, 18, 9, 2, 2, 0, 0, 0, 0, 175, 148, 98, 87, 35,
23, 10, 2, 1, 0, 0, 0, 0, 201, 169, 111, 91, 52, 36, 11, 8, 3,
1, 0, 0, 0, 183, 139, 88, 54, 38, 30, 8, 2, 2, 0, 0, 0, 0, 206,
175, 119, 68, 41, 34, 6, 3, 1, 1, 0, 0, 0), x = c(1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 12, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12,
14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 12, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 12, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 12, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12,
14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 12, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 12, 14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 12, 14, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
12, 14, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 12, 14, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 12, 14, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14,
16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 1, 2, 3, 4, 5,
6, 7, 8, 9, 10, 12, 14, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12,
14, 16), batch = 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 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, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
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), levels = c("1", "2", "3", "4", "5", "6", "7"
), class = "factor"), trtmnt = 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, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 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, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 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, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 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, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 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, 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, 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, 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, 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, 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), levels = c("A", "B", "C", "D"), class = "factor")), row.names = c(NA,
348L), class = "data.frame")

tl;dr I believe the results of these different approaches are equivalent (i.e. lead to the same distribution of results); they are not equal because the multivariate Normal values are chosen in a different way.
MASS::mvrnorm() computes the eigenvalues and eigenvectors of the covariance matrix; picks a set of standard Normal deviates; scales them by the square root of the eigenvalues; and multiplies by the eigenvector matrix
lme4::.simulateFun() (the workhorse function behind the simulate() method) computes the Cholesky factor of the covariance matrix of the random effects directly; picks a set of standard Normal deviates; and multiplies
For a simple case, #RolfTurner's roll-your-own code is much easier to understand; the advantage of the more abstract approach used in .simulateFun(), illustrated further below, is that it generalizes to an arbitrarily complex random effects model (multiple random effects, complicated vector-valued REs, etc.) without any further tweaking.
These two approaches give the same distributions, but not precisely the same answer (I hope that's convincing at a mathematical level: I don't really want to get farther into the details of exactly which linear combinations of standard normal deviates are used, or whether it's possible to permute the standard-normal-deviate vector so these two approaches are equivalent at the level of computed values, not just distributions ...)
Here is a simplified illustration of the two deviate-generation processes:
Sigma <- VarCorr(fit)[[1]]
set.seed(102)
b1 <- MASS::mvrnorm(1, mu = rep(0, 2), Sigma = Sigma)
## (Intercept) x
## -0.04550951 -0.01789231
set.seed(101)
b2 <- drop(t(chol(Sigma)) %*% rnorm(2))
## (Intercept) x
## -0.08147349 0.01498128
Here is (approximately) the code that lme4::.simulateFun uses internally (the code for MASS::mvrnorm() is much shorter and easier to read).
set.seed(101)
nsim <- 1
newRE <- mkNewReTrms(fit, newdata = Dat, ~x|batch)
## Lambdat is the (transpose of) the Cholesky factor of the RE covariance matrix
## Zt is the (transpose of) the RE model matrix
## (equivalent to converting the `Z[batch,]` vector to `Z[batch,1] + Z[batch,2]*x`)
U <- t(newRE$Lambdat %*% newRE$Zt)
u <- rnorm(ncol(U) * nsim)
b2 <- drop(as(U %*% matrix(u, ncol = nsim), "matrix"))
Here is an experimental demonstration (not proof) that the results are equivalent: simulate 500 draws using each approach and plot them against each other:
## NOT standalone: needs the code in the original post to be run first
ryo_fun <- function() {
b <- MASS::mvrnorm(nrep,c(0,0), Sigma)
lnpr <- with(Dat,beta0 + beta1*x + b[batch,1] + b[batch,2]*x)
p <- 1/(1+exp(-lnpr)) # Inverse of logit link.
size <- with(Dat,Good+Bad)
s.ryo <- rbinom(nrow(Dat),size,p)
return(s.ryo)
}
set.seed(101)
ryo_mat <- replicate(500, ryo_fun())
# Using simulate.merMod:
set.seed(101)
mer_mat <- sapply(simulate(fit, nsim = 500), function(x) x[,1])
png("SO73439211.png")
par(las=1, bty = "l")
plot(ryo_mat, mer_mat, col = adjustcolor("black", alpha.f = 0.1), pch = 16,
xlab = "roll your own", ylab = "simulate()")
abline(a=0, b=1, col = 2, lwd = 2)
dev.off()

putting segments on a plot with multiple plots and y variables

Is there a way to put arrows on a plot with multiple plots with different y axes? I would like to put arrows across the time series on the same x axis locations but different y locations. I cant just use "annotate("segment", x = 37, xend = 84, y = 0.0, yend = 0.0,colour = "black", size = 1, arrow = arrow(ends='both'))" because then it puts them at 0 on the y axis for all variables when I actually want to just put the arrows at the bottom of the y axis which is different for every variable.
Current code:
fin_plot <- ggplot(melted_data, aes(x = `Distance`, y = value, group = variable)) + geom_line() + theme_bw() + labs(y="", x= "") + theme_classic() + theme(text=element_text(size=16, family="serif", face = "bold", color = "black")) +
facet_wrap(variable~., scales = "free_y",ncol=2) +
scale_x_continuous(limits = c(0, 250),labels = scales::number_format(accuracy = 1)) + theme(axis.line = element_line(colour = 'black', size = 1)) +
theme(axis.ticks = element_line(colour = "black", size = 1)) + scale_y_continuous(labels = scales::number_format(accuracy = 0.1))+ theme(axis.ticks.length = unit(.3, "cm")) + coord_capped_cart(bottom='right', left='none', gap = 0.15) + geom_vline(xintercept=c(58, 132, 204, 250, 309), linetype='dashed', col = 'black')
Current output
desired output
data:
melted_data <- structure(list(Distance = 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,
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106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,
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132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144,
145, 146, 147, 148, 149, 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,
107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,
120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132,
133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,
146, 147, 148, 149, 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, 107,
108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,
121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,
147, 148, 149, 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,
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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, 107, 108,
109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134,
135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,
148, 149, 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, 107, 108,
109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134,
135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,
148, 149), variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
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0.59150912, 0.491623628, 0.646548159, 0.559243084, 0.554057512,
0.542344646, 0.583808567, 0.623315676, 0.521008383, 0.511710892,
0.633820855, 0.529775704, 0.590383598, 0.500021436, 0.602344336,
0.499887402, 0.534870849, 0.583225149, 0.623554367, 0.62596102,
0.585378422, 0.648988779, 0.577416685, 0.632021029, 0.644454559,
0.684966009, 0.595845502, 2.479315993, 2.683540753, 2.424790513,
2.556904106, 2.454032378, 2.486582811, 2.485804182, 2.625597071,
2.444459365, 2.649813652, 2.686066928, 3.124873535, 3.077318299,
3.297830917, 3.344358668, 3.589441204, 3.566707313, 3.968369009,
3.932341434, 4.08973781, 4.374551474, 4.54266808, 4.97884528,
4.932211371, 5.310903272, 5.372904082, 5.231493496, 5.123516042,
5.393849098, 5.276658613, 4.970827822, 4.972075355, 4.608769407,
4.214216452, 4.232190208, 4.539424798, 4.266998558, 3.933891331,
3.898577905, 3.758409871, 3.707152695, 3.544143355, 3.234304675,
3.312782898, 3.363897722, 3.32751203, 3.063968711, 3.396338279,
3.110947858, 3.27642981, 2.802338511, 2.972332411, 2.999566144,
2.860636811, 2.88545135, 2.715249006, 2.805430479, 2.734554555,
2.721654986, 2.81795618, 2.810857383, 2.829266791, 3.020586802,
3.108527475, 2.923112037, 2.898589704, 2.977292189, 2.961041296,
3.065747444, 2.883958043, 2.837869726, 2.918189185, 2.936651583,
2.760674734, 2.997230073, 2.888064962, 2.972304014, 3.162708107,
3.42147456, 3.577994842, 3.897689363, 4.134240754, 4.19746467,
4.937297252, 4.909702892, 4.974867813, 4.740338415, 4.369505261,
4.634231316, 4.530190201, 4.380129066, 4.246648651, 4.003376949,
4.261248528, 4.228186763, 4.190890809, 3.896217461, 4.019225536,
3.980007369, 3.985014169, 3.698733958, 3.417194347, 3.50155334,
3.527485148, 3.272718395, 3.228503258, 3.353819869, 3.104831527,
3.419528222, 3.010592683, 3.256523555, 3.020944643, 3.139582776,
2.872858156, 3.135211633, 3.047270457, 3.038848701, 2.843214189,
3.123247632, 2.958537301, 3.257263308, 3.138521527, 3.248321146,
2.963340122, 3.076476029, 2.987721452, 3.004584487, 2.906910601,
2.973867453, 3.0761696, 2.869900334, 2.78054149, 3.25876542,
2.978797901, 3.041764942, 3.029872905, 3.052446623, 2.856505763,
2.9962536, 3.015603327, 3.111149077, 2.9885447, 2.993520426,
3.176541902, 3.037954707, 2.975005669, 3.278917742, 3.137024394,
3.117943428)), row.names = c(NA, -745L), class = "data.frame")

Use geom_segment - this allows you to make use of the faceting variable. You will then want to pass a data frame with the respective x/xend/y/yend.
library(dplyr)
## create a data frame first for the segments
## it makes sense to use the mininimum of your y for each facet
annot_df <- melted_data %>%
group_by(variable) %>%
summarise(y = min(value), yend = min(value), x = 25, xend = 75)
ggplot(melted_data, aes(x = Distance, y = value, group = variable)) +
geom_line() +
## now use the new data frame for geom_segment
geom_segment(data = annot_df, aes(x = x, xend = xend, y = y, yend = yend),
arrow = arrow(ends = "both", length = unit(5, "pt"))) +
facet_wrap(variable~., scales = "free_y",ncol=2)
Created on 2022-07-14 by the reprex package (v2.0.1)

Boxplot colour mismatch

I would like to color the dots of the plot with the legend Episode values. What I am missing?
I replaced fill with color still is not the plot that I would like to have
Sample code:
(p <- ggplot(df, aes(x=Type, y=Value, fill=Episode, group=Type)) +
geom_boxplot()+
geom_line()+
geom_dotplot(binaxis='y', stackdir='center',
position=position_dodge(0.8))+
theme_bw())
Sample data:
df<-structure(list(Type = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L,
7L, 7L, 7L), .Label = c("A", "B", "C", "D", "E", "F", "G"), class = "factor"),
Episode = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L), .Label = c("t1", "t2", "t3", "t4", "t5", "t6"
), class = "factor"), Value = c(32, 36, 57, 83, 88, 40, 40,
44, 67, 77, 66, 45, 88, 46, 56, 99, 65, 0, 66, 46, 59, 77,
74, 79, 38, 45, 60, 78, 66, 75, 45, 55, 68, 77, 88, 35, 36,
118, 80, 73, 71, 0)), row.names = c(NA, -42L), class = "data.frame")

I am not entirely sure if I understand your question correctly, but for me, the cleanest plot would be:
library(ggplot2)
df<-structure(list(Type = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L,
7L, 7L, 7L), .Label = c("A", "B", "C", "D", "E", "F", "G"), class = "factor"),
Episode = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L), .Label = c("t1", "t2", "t3", "t4", "t5", "t6"
), class = "factor"), Value = c(32, 36, 57, 83, 88, 40, 40,
44, 67, 77, 66, 45, 88, 46, 56, 99, 65, 0, 66, 46, 59, 77,
74, 79, 38, 45, 60, 78, 66, 75, 45, 55, 68, 77, 88, 35, 36,
118, 80, 73, 71, 0)), row.names = c(NA, -42L), class = "data.frame")
p <- ggplot(df, aes(x=Type, y=Value, group = Type)) +
geom_boxplot() +
geom_line() +
geom_point(aes(col=Episode))
p
Created on 2021-04-15 by the reprex package (v0.3.0)

R get linear regression equation for boxplots

I didn´t found a sufficient answer in this forum yet, so I decided to raise my own question.
I want to get the linear regression equation of a linear fit from a boxplot. I have this data:
library(ggplot2)
data <- structure(list(x = 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, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 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, 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, 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
), .Label = c("1", "2", "3", "4", "5", "6"), class = "factor"),
y = c(169, 79.5, 78.5, 75, 99.5, 68, 14, 30.5, 107.5, 51,
43, 33, 21.5, 35, 11, 1, 38, 54.5, 26.5, 143, 158, 171, 31.5,
67.5, 1, 57.5, 12, 36.5, 1, 23.5, 22.5, 71, 141, 218, 7.5,
1, 129, 144.5, 76, 46.5, 75.5, 45, 12, 24, 67, 65.5, 44.5,
37.5, 25.5, 19, 15, 1, 17.5, 50, 22.5, 90, 226, 220, 32,
69.5, 1, 79.5, 7, 44, 1, 15.5, 22, 75.5, 178, 153, 4.5, 1,
159, 89, 57, 71, 98.5, 47.5, 18.5, 30, 119, 57.5, 41, 33.5,
30, 31, 10, 1, 12, 43.5, 20.5, 98, 146.5, 145, 34, 64.5,
1, 40.5, 17, 41, 1, 14.5, 16.5, 71, 181, 168, 2, 1, 159,
103, 69, 65.5, 97.5, 37.5, 21, 15.5, 120.5, 46, 27, 29.5,
16.5, 20, 7.5, 1, 15.5, 42.5, 21.5, 111, 102.5, 124, 20.5,
51.5, 1, 22.5, 15, 42, 1, 13, 13.5, 64.5, 138, 155, 4.9,
1, 190, 89.5, 74.5, 79, 78, 59.5, 19.5, 21, 88.5, 44, 18,
19, 10, 13, 4, 1, 9.5, 44, 17, 140.5, 98, 112.5, 29.5, 62.56,
1, 31, 11.5, 49.5, 1, 10, 8.5, 40.5, 121, 141, 2.5, 1, 170,
87.5, 92, 77, 65, 34, 8, 26, 98, 51.5, 26, 19, 9, 8.5, 7.5,
1, 4.5, 0, 15.5, 80, 69, 59, 28, 44.5, 1, 38.5, 10, 51.5,
1, 3, 5, 65, 107, 152, 5, 1)), row.names = c(NA, -216L), class = "data.frame")
p <- ggplot(data = data) +
aes(x = x,
y = y) +
geom_boxplot(outlier.shape = NA) + geom_jitter(shape = 1, position = position_jitter(0.1)) +
ylim(0, NA) +
theme_light() +
geom_smooth(method = "lm",se = TRUE, formula = y ~ x, aes(group = 1))
print(p)
fit <- lm(y ~ x, data = data)
fit
which results in this output:
How can I extract the regression equation for this dataset? The function fit <- lm(y ~ x, data = data) just gives me one intercept and 5 coefficients, which is not my desired output. I want a simple regression equation in the form of y = a + bx.
How can I put this equation into the diagramm? I´ve already looked into ggpmisc::stat_poly_eq(), but this doesn´t seem to work with boxplot linear regression.
Can you guys help me out?

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