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geeglm crashes with unstructured cov. and using "waves" #10

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Generalized opened this issue May 25, 2022 · 3 comments
Open

geeglm crashes with unstructured cov. and using "waves" #10

Generalized opened this issue May 25, 2022 · 3 comments

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@Generalized
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Generalized commented May 25, 2022 •
edited
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Dear @hojsgaard , I noticed that the problem with crashing geeglm() still exists.

With the following data

data <- structure(list(Timepoint = structure(c(2L, 4L, 1L, 2L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 1L, 3L, 1L, 2L, 4L, 1L, 2L, 4L, 1L, 3L, 
1L, 3L, 4L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 4L, 1L, 4L, 1L, 3L, 
1L, 2L, 1L, 2L, 1L, 1L, 2L, 4L, 1L, 3L, 1L, 2L, 4L, 1L, 2L, 2L, 
4L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 2L, 4L, 1L, 2L, 4L, 1L, 4L, 1L, 
1L, 1L, 1L, 3L, 1L, 3L, 1L, 2L, 4L, 1L, 2L, 4L, 1L, 1L, 2L, 4L, 
1L, 2L, 4L, 1L, 4L, 1L, 1L, 1L, 4L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 
3L, 4L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 1L, 2L, 4L, 1L, 2L, 4L, 
1L, 1L, 3L, 4L, 1L, 1L, 4L, 1L, 2L, 4L, 1L, 4L, 1L, 4L, 1L, 3L, 
4L, 1L, 3L, 1L, 1L, 1L, 3L, 4L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 3L, 
1L, 1L, 4L, 1L, 3L, 4L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 4L, 1L, 4L, 
1L, 1L, 2L, 4L, 1L, 3L, 4L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 2L, 
4L, 1L, 3L, 4L, 1L, 1L, 2L, 3L, 1L, 1L, 3L, 4L, 1L, 1L, 1L, 2L, 
4L, 1L, 1L, 3L, 1L, 1L, 4L, 1L, 1L, 2L, 4L, 1L, 3L, 1L, 3L, 4L, 
1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 4L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 
3L, 1L, 1L, 2L, 1L, 2L, 1L, 4L, 1L, 1L, 3L, 1L, 3L, 1L, 2L, 1L, 
1L, 1L, 3L, 1L, 2L, 1L), .Label = c("Baseline", "Time 1", 
"Time 2", "Time 3"), class = "factor"), Result = c(-2.4, 
-2.2, -2.3, -2.6, -2.9, -3.7, -2.9, -4.2, -3.7, -2.8, -3.8, -0.8, 
-2.2, -2, -2.6, -2.6, -2.7, -3.6, -1.2, -1.6, -2.7, -2.7, -2.7, 
-3.3, -3.1, -3.4, -3.3, -4.5, -2.4, -3.5, -2.2, -2.5, -1.7, -0.4, 
-2.1, -1.8, -2, -2, -2.6, -2.5, -2.6, -2, -3.3, -1.7, -2.6, -2.5, 
-2.7, -2.4, -2.6, -2, -2.1, -2.4, -2.5, -2.5, -2.5, -2.7, -1.1, 
-2.9, -3.5, -2.9, -2.7, -2.9, -1.7, -1.3, -2, -1.4, -1.7, -1.5, 
-1.3, -1.5, -2.6, -2.2, -2, -2.5, -2.9, -2.6, -2.6, -2.4, -2.3, 
-3.3, -2.6, -2.4, -2.7, -2.2, -2.1, -2.1, -3.1, -1.3, -1.2, -1.4, 
-0.3, -1.1, -2, -1.5, -1.6, -1.8, -2.7, -2.9, -2.4, -1.9, -2.7, 
-1.4, -1.9, -1.7, -2, -2.5, -2.8, -1.2, -1.3, -2.7, -2.5, -3.4, 
-2.9, -2.8, -2.7, -2.8, -1.7, -2.7, -3, -3.3, -2.3, -2.5, -3.3, 
-2.2, -2.3, -2.3, -2.8, -3, -2.9, -2.3, -2.4, -1.6, -1.8, -1.5, 
-1.4, -2.2, -2.4, -2.3, -2.7, -2.8, -2.6, -2.7, -3.6, -2.8, -2.9, 
-2.4, -2.5, -1.8, -2.3, -2.6, -2.7, -2, -2.3, -2.3, -1.5, -1.4, 
-1.5, -2, -2.7, -2.7, -2.8, -2.4, -2.7, -3.5, -4.3, -3, -3.2, 
-2.6, -2.5, -2.1, -3.2, -3.3, -2.7, -3.5, -3, -2.8, -2.4, -2.3, 
-2.3, -2.5, -3.2, -2.5, -2.5, -3.3, -3.9, -3.5, -4.7, -2.6, -2.5, 
-2.5, -3.6, -2.5, -1.7, -1.7, -1.8, -2.2, -3.3, -3.1, -3.2, -4.5, 
-1.8, -1.1, -1.6, -2.3, -2.6, -3.2, -2.6, -2.2, -2.1, -3.4, -2.4, 
-2.4, -1.1, -1.1, -1.4, -1.2, -1.8, -0.5, -2.3, -2.7, -2.8, -2.1, 
-2, -2.1, -1.6, -2.9, -2.1, -2.2, -2.6, -2.4, -1.6, -2.5, -4, 
-2.4, -2.7, -4, -3.64, -2.8, -3.1, -2.3, -1.9, -1.9, -2.5, -2.8, 
-2.4, -2.6, -2.2, -2.7, -3.1, -3, -2, -2.2), wave = c(2, 4, 1, 
2, 1, 2, 3, 1, 2, 3, 1, 2, 1, 3, 1, 2, 4, 1, 2, 4, 1, 3, 1, 3, 
4, 1, 3, 1, 3, 1, 3, 1, 3, 4, 1, 4, 1, 3, 1, 2, 1, 2, 1, 1, 2, 
4, 1, 3, 1, 2, 4, 1, 2, 2, 4, 1, 1, 1, 1, 2, 4, 1, 2, 4, 1, 2, 
4, 1, 4, 1, 1, 1, 1, 3, 1, 3, 1, 2, 4, 1, 2, 4, 1, 1, 2, 4, 1, 
2, 4, 1, 4, 1, 1, 1, 4, 1, 4, 1, 1, 1, 1, 1, 3, 4, 1, 1, 1, 1, 
1, 2, 4, 1, 1, 2, 4, 1, 2, 4, 1, 1, 3, 4, 1, 1, 4, 1, 2, 4, 1, 
4, 1, 4, 1, 3, 4, 1, 3, 1, 1, 1, 3, 4, 1, 1, 1, 3, 1, 2, 1, 3, 
1, 1, 4, 1, 3, 4, 1, 1, 1, 1, 1, 3, 1, 4, 1, 4, 1, 1, 2, 4, 1, 
3, 4, 1, 1, 1, 1, 1, 2, 4, 1, 2, 4, 1, 3, 4, 1, 1, 2, 3, 1, 1, 
3, 4, 1, 1, 1, 2, 4, 1, 1, 3, 1, 1, 4, 1, 1, 2, 4, 1, 3, 1, 3, 
4, 1, 1, 1, 1, 3, 1, 1, 2, 4, 1, 1, 1, 1, 2, 1, 1, 3, 1, 1, 2, 
1, 2, 1, 4, 1, 1, 3, 1, 3, 1, 2, 1, 1, 1, 3, 1, 2, 1), ID = structure(c(9L, 
9L, 9L, 10L, 10L, 45L, 45L, 45L, 46L, 46L, 46L, 11L, 12L, 13L, 
13L, 47L, 47L, 47L, 48L, 48L, 48L, 25L, 25L, 26L, 26L, 26L, 49L, 
49L, 50L, 50L, 97L, 97L, 27L, 27L, 27L, 28L, 28L, 29L, 29L, 98L, 
98L, 51L, 51L, 135L, 82L, 82L, 82L, 83L, 83L, 14L, 14L, 14L, 
15L, 84L, 84L, 84L, 99L, 100L, 101L, 30L, 30L, 30L, 31L, 31L, 
31L, 16L, 16L, 16L, 17L, 17L, 102L, 103L, 32L, 85L, 85L, 104L, 
104L, 52L, 52L, 52L, 18L, 18L, 18L, 70L, 53L, 53L, 53L, 33L, 
33L, 33L, 54L, 54L, 105L, 106L, 19L, 19L, 34L, 34L, 86L, 107L, 
108L, 109L, 87L, 87L, 87L, 110L, 71L, 111L, 35L, 55L, 55L, 55L, 
112L, 88L, 88L, 88L, 56L, 56L, 56L, 72L, 57L, 57L, 57L, 136L, 
132L, 132L, 36L, 36L, 36L, 113L, 113L, 114L, 114L, 58L, 58L, 
58L, 115L, 115L, 89L, 116L, 59L, 59L, 59L, 20L, 117L, 118L, 118L, 
90L, 90L, 119L, 119L, 21L, 133L, 133L, 37L, 37L, 37L, 120L, 121L, 
122L, 1L, 38L, 38L, 60L, 60L, 123L, 123L, 124L, 61L, 61L, 61L, 
2L, 2L, 2L, 125L, 126L, 3L, 4L, 62L, 62L, 62L, 63L, 63L, 63L, 
64L, 64L, 64L, 73L, 65L, 65L, 65L, 74L, 5L, 5L, 5L, 75L, 76L, 
66L, 66L, 66L, 77L, 22L, 22L, 6L, 134L, 134L, 39L, 67L, 67L, 
67L, 40L, 40L, 7L, 7L, 7L, 8L, 127L, 68L, 23L, 23L, 78L, 91L, 
91L, 91L, 69L, 128L, 79L, 92L, 92L, 41L, 93L, 93L, 80L, 94L, 
94L, 24L, 24L, 95L, 95L, 129L, 42L, 42L, 43L, 43L, 96L, 96L, 
130L, 81L, 131L, 131L, 44L, 44L), .Label = c("86729771001", "86729771002", 
"86729771003", "86729771004", "86729771005", "86729771006", "86729771007", 
"86729771008", "86729772001", "86729772002", "86729772005", "86729772006", 
"86729772007", "86729772008", "86729772009", "86729772010", "86729772011", 
"86729772012", "86729772013", "86729772014", "86729772015", "86729772016", 
"86729772017", "86729772018", "86729773001", "86729773002", "86729773004", 
"86729773005", "86729773006", "86729773007", "86729773008", "86729773009", 
"86729773010", "86729773011", "86729773012", "86729773013", "86729773014", 
"86729773015", "86729773016", "86729773017", "86729773018", "86729773019", 
"86729773020", "86729773021", "86729774001", "86729774002", "86729774003", 
"86729774004", "86729774005", "86729774006", "86729774007", "86729774008", 
"86729774009", "86729774010", "86729774011", "86729774012", "86729774013", 
"86729774014", "86729774015", "86729774016", "86729774017", "86729774018", 
"86729774019", "86729774020", "86729774021", "86729774022", "86729774023", 
"86729775008", "86729775010", "86729776001", "86729776002", "86729776003", 
"86729776004", "86729776005", "86729776006", "86729776007", "86729776008", 
"86729776009", "86729776010", "86729776011", "86729776012", "86729777001", 
"86729777002", "86729777003", "86729777004", "86729777007", "86729777008", 
"86729777009", "86729777010", "86729777011", "86729777012", "86729777013", 
"86729777014", "86729777015", "86729777016", "86729777017", "86729778001", 
"86729778002", "86729778003", "86729778004", "86729778005", "86729778006", 
"86729778007", "86729778008", "86729778009", "86729778010", "86729778011", 
"86729778012", "86729778013", "86729778014", "86729778015", "86729778016", 
"86729778017", "86729778018", "86729778019", "86729778020", "86729778021", 
"86729778022", "86729778023", "86729778024", "86729778025", "86729778026", 
"86729778027", "86729778028", "86729778029", "86729778030", "86729778031", 
"86729778032", "86729778033", "86729778034", "86729778035", "86729779001", 
"86729779002", "86729779004", "86729780001", "86729780002"), class = "factor")), row.names = c(NA, 
-252L), class = "data.frame")

The geeglm works fine with this command:

mgee <- geeglm(Result ~ Timepoint,
               data = data, 
               id = ID, 
               family = gaussian,
               waves = wave,
               corstr = "exchangeable")

> coef(summary(mgee))
                Estimate Std.err    Wald  Pr(>|W|)
(Intercept)      -2.5708 0.06321 1653.88 0.000e+00
TimepointTime 1   0.4331 0.07225   35.94 2.038e-09
TimepointTime 2   0.3956 0.06580   36.14 1.835e-09
TimepointTime 3   0.4757 0.05645   71.00 0.000e+00

but crashes with changing the correlation structure to "unstructured"

# DON'T RUN (or it may crash your session)

mgee <- geeglm(Result ~ Timepoint,
               data = data, 
               id = ID, 
               family = gaussian,
               waves = wave,
               corstr = "unstructured")

I use R 3.6.3
geepack: 1.3.3

EDIT: Maybe it will help you - I re-run the analysis with the geeM package. It returned the following message:

> geem(Result ~ Timepoint,
+        data = data, 
+        id = ID, 
+        family = gaussian,
+        waves = wave,
+        corstr = "unstructured")

Error in updateAlphaUnstruc(Y, mu, VarFun, phi, id, len, StdErr, Resid,  : 
  Number of clusters of largest size is less than p.
@Generalized
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Author

Dear @hojsgaard , @ekstroem , @thomas-fung
Do you have any ideas on how to solve this problem? Let me provide another example:

Each subject has a sequence of measurements, at several timepoints, like Week 1, Week 2... Week 32.
I want to assess the change from baseline (already modelled as change) at each timepoint.

This one works, but subjects may have gaps in the measurements(!):

> mg <- geepack::geeglm(change ~ Time, data=x, 
+              family = "gaussian",
+              id = x$ID, 
+              corstr = "unstructured")
> 
> emg <- emmeans(mg, specs = ~Time)
> update(emg, infer = c(FALSE, TRUE), adjust="mvt")
 Time    emmean    SE  df z.ratio p.value
 Week 1  -0.317 0.132 Inf  -2.393  0.0717
 Week 2  -0.633 0.136 Inf  -4.643  <.0001
 Week 4  -0.719 0.178 Inf  -4.033  0.0004
 Week 8  -0.984 0.166 Inf  -5.929  <.0001
 Week 16 -1.198 0.162 Inf  -7.413  <.0001
 Week 32 -1.456 0.290 Inf  -5.021  <.0001

Covariance estimate used: vbeta 
P value adjustment: mvt method for 6 tests

So let's address it using Waves (just numerical representation of the factor Time: 1, 2, 3... 6
This will CRASH your session:

> mg <- geepack::geeglm(change ~ Time, data=x, 
+              family = "gaussian",
+              id = x$ID, 
+              waves = x$Wave,
+              corstr = "unstructured")

It will work for AR1 and independence.

Data:

x <- structure(list(ID = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 
5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 9L, 9L, 
9L, 9L, 9L, 9L, 26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 
27L, 27L, 28L, 28L, 28L, 28L, 28L, 29L, 29L, 29L, 29L, 29L, 30L, 
30L, 30L, 30L, 30L, 31L, 31L, 32L, 32L, 32L, 32L, 32L, 32L, 33L, 
33L, 33L, 33L, 33L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 
14L, 14L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 
17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 
19L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 
22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 
24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 34L, 34L, 34L, 34L, 34L, 
34L, 35L, 35L, 35L, 35L, 35L, 36L, 36L, 36L, 36L, 36L, 36L, 37L, 
37L, 37L, 37L, 37L, 38L, 38L, 38L, 38L, 38L, 39L, 39L, 39L, 39L, 
39L, 39L, 40L, 40L, 40L, 40L, 40L, 41L, 41L, 41L, 41L, 41L, 42L, 
42L, 42L, 42L, 43L, 43L, 43L, 44L, 44L, 44L, 44L, 44L, 45L, 45L, 
46L, 46L, 46L, 46L, 46L, 46L, 47L, 47L, 47L, 47L, 47L, 48L, 48L, 
48L, 48L, 48L, 48L, 49L, 49L, 49L, 49L, 49L, 49L, 50L, 50L, 50L, 
50L, 50L, 50L, 51L, 51L, 51L, 51L, 51L, 51L, 52L, 52L, 52L, 52L, 
53L, 53L, 53L, 53L, 53L, 54L, 54L, 54L, 54L, 54L, 55L, 55L, 55L, 
55L, 55L, 56L, 56L, 56L, 56L, 56L, 56L, 57L, 57L, 57L, 57L, 57L, 
57L, 64L, 64L, 64L, 64L, 64L, 65L, 65L, 65L, 65L, 65L, 65L, 66L, 
66L, 66L, 66L, 66L, 58L, 58L, 58L, 59L, 59L, 59L, 59L, 59L, 59L, 
60L, 60L, 60L, 60L, 60L, 61L, 61L, 61L, 61L, 61L, 61L, 62L, 62L, 
62L, 62L, 62L, 63L, 63L, 63L, 63L, 63L, 63L, 67L, 67L, 67L, 67L, 
67L, 67L, 68L, 68L, 68L, 68L, 68L, 68L, 69L, 69L, 69L, 69L, 70L, 
70L, 70L, 70L, 70L), levels = c("5501551", "5501552", "5501553", 
"5501554", "5501555", "5501556", "5501557", "5501558", "5501559", 
"5502010", "5502011", "5502012", "5502013", "5502014", "5502015", 
"5502016", "5502017", "5502018", "5502019", "5502020", "5502022", 
"5502023", "5502024", "5502025", "5502026", "5502551", "5502552", 
"5502554", "5502555", "5502556", "5502557", "5502558", "5502559", 
"5504551", "5504552", "5506551", "5506552", "5506553", "5506554", 
"5506555", "5506556", "5506557", "5506558", "5507551", "5507552", 
"5508551", "5508552", "5508553", "5508554", "5508555", "5508556", 
"5508557", "5508558", "5509551", "5509552", "5509553", "5509554", 
"5512551", "5513551", "5513552", "5513553", "5513554", "5513555", 
"5515501", "5515502", "5515503", "5516551", "5516552", "5517551", 
"5518551"), class = "factor"), Value = c(2.6, 2.6, 2.4, 1.2, 
0.8, 0.6, 3.4, 3.4, 1.4, 0.8, 0.8, 4.4, 2.6, 3, 2.6, 2.2, 2.6, 
2.2, 3.8, 3.2, 3.6, 3.8, 2.6, 2.8, 2.2, 1.6, 1.6, 1.2, 0.6, 0.6, 
1, 1, 0.4, 0.4, 0, 0, 1.2, 0.8, 1.8, 0.2, 0.2, 0.2, 0.4, 0.2, 
5.6, 5.4, 6, 5.4, 4.8, 2.4, 1.8, 1.6, 1.8, 0.6, 0.6, 2.2, 2.2, 
4, 2, 2.8, 3.4, 2.8, 2.6, 4.2, 3, 4, 2.4, 2.2, 3, 3, 3, 2.2, 
4.6, 4.4, 1.8, 1.4, 2.4, 4, 5.2, 5, 3.6, 3.4, 2.4, 3, 2.8, 3.2, 
3.2, 2.8, 2.6, 2.4, 1.6, 1.4, 2.8, 2.2, 2.2, 2.2, 3.6, 3.6, 3.6, 
3, 1.2, 0.2, 3.2, 3, 3, 3.2, 3.2, 0, 0, 0.2, 0, 0, 1.8, 0.2, 
0.2, 0, 0, 0, 4.2, 4.2, 4.6, 3.2, 3.6, 2.8, 2.6, 3, 2.8, 2.8, 
2.8, 3, 3.4, 2.8, 4.8, 3.4, 3.4, 3.2, 2.8, 2, 2.4, 2, 0.4, 0, 
2.8, 0.2, 0.2, 0.4, 0.4, 0.2, 2.8, 2.8, 2.2, 2.2, 2.4, 2.8, 1, 
0.8, 0.6, 0.2, 0.2, 0.2, 3, 2.8, 3.2, 3, 4.2, 1, 0.8, 0.8, 0.8, 
0.6, 0.6, 2.8, 3.4, 3.2, 3.2, 0.6, 2, 0.8, 1.4, 1, 1.8, 0.4, 
1.4, 2.2, 0.6, 3.6, 0.8, 1.2, 1, 0.8, 0.4, 0, 1.8, 2.2, 1.4, 
1.2, 0.6, 0.6, 3.4, 3, 2, 1.2, 0.6, 1.2, 1, 0.8, 0.2, 0.6, 2, 
2.6, 2.6, 2.4, 0.6, 0.8, 1.6, 0.8, 1, 1, 1, 0.2, 3.2, 3.4, 0.8, 
0.6, 0.8, 1.4, 0.6, 0.6, 4.2, 3.8, 3.8, 3.8, 3.8, 0.8, 0.8, 1.4, 
1.4, 0.6, 0, 2.8, 3.4, 3, 3.2, 1.6, 1.6, 0.8, 0.8, 0.4, 0.4, 
0.4, 0.4, 1.4, 0.8, 1, 0.8, 1.2, 1, 0, 1.6, 1.6, 1.2, 1.4, 0.8, 
0, 0, 0.6, 0.2, 0.2, 0.4, 0.4, 0, 1.8, 1.4, 1.8, 0.8, 1.6, 0.8, 
1, 0.6, 0.2, 0.2, 0.4, 1.2, 0.4, 0.2, 0.2, 0, 0, 3.8, 2.6, 2, 
1.4, 1.8, 1.6, 1.8, 2.4, 2.2, 2.4, 1.4, 2.6, 1.8, 1.2, 2, 2.2, 
3.8, 3.8, 3.4, 3.4, 2, 2.8, 2, 1.4, 0.4, 2.2, 3.2, 3.4, 1.6, 
1, 2.2, 0.8, 1, 0.6, 1.2, 2, 5.6, 5.4, 4.6, 4.2, 4, 0.8, 0.2, 
0.2, 0.4, 0, 0, 0.6, 0.6, 4.4, 0.8, 1.4, 0.8, 0.8, 0.6, 0.8, 
0.2, 0, 0.2, 1.8, 1.2, 1.6, 0.8, 0, 0, 0.4, 0.2, 0), change = c(-0.8, 
-0.8, -1, -2.2, -2.6, -2.8, -0.6, -0.6, -2.6, -3.2, -3.2, 1.4, 
-0.4, 0, -0.4, -0.8, 0.2, -0.2, 1.4, 0.8, 1.2, -1.2, -2.4, -2.2, 
-2.8, -3.4, -0.4, -0.8, -1.4, -1.4, -0.4, -0.4, -1, -1, -1.4, 
-1.4, -1.6, -2, 0, -1.6, -1.6, -1.6, -1.4, -1.6, 0.6, 0.4, 1, 
0.4, -0.2, 0.4, -0.2, -0.4, -0.2, -1.4, -1.4, 0.6, 0.6, 2.4, 
0.4, 1.2, -0.6, -1.2, -1.4, 0.2, -1, 0, -1.6, -1.8, -1, -1, 0.2, 
-0.6, 1.6, 1.4, -1.2, -1.6, -0.6, 1, 1.2, 1, -0.4, -0.6, -1.6, 
-0.2, -0.4, 0, 0, 1.4, 1.2, 1, 0.2, 0, -0.6, -1.2, -1.2, -1.2, 
-0.6, -0.6, -0.6, -1.2, -3, -4, -0.2, -0.4, -0.4, -0.2, -0.2, 
-1.2, -1.2, -1, -1.2, -1.2, 1.4, -0.2, -0.2, -0.4, -0.4, -0.4, 
0.2, 0.2, 0.6, -0.8, -0.4, 0, -0.2, 0.2, 0, 0.6, 0.6, 0.8, 1.2, 
0.6, -1.2, -2.6, -2.6, -2.8, -0.8, -1.6, -1.2, -1.6, -3.2, -3.6, 
0, -2.6, -2.6, -2.4, -2.4, -2.6, 0.6, 0.6, 0, 0, 0.2, 0.6, -0.4, 
-0.6, -0.8, -1.2, -1.2, -1.2, -0.2, -0.4, 0, -0.2, 1, -0.4, -0.6, 
-0.6, -0.6, -0.8, -0.8, -0.2, 0.4, 0.2, 0.2, -2.4, 0.8, -0.4, 
0.2, -0.2, 0.6, -0.8, -1.4, -0.6, -2.2, 0.8, -2, 0.6, 0.4, 0.2, 
-0.2, -0.6, -0.8, -0.4, -1.2, -1.4, -2, -2, -0.4, -0.8, -1.8, 
-2.6, -3.2, -2.2, -2.4, -2.6, -3.2, -2.8, -0.6, 0, 0, -0.2, -1, 
-0.8, 0, -3.2, -3, -3, -3, -3.8, 0, 0.2, -0.6, -0.8, -0.6, 0, 
-0.8, -0.8, 1.4, 1, 1, 1, 1, -2.4, -2.4, -1.8, -1.8, -2.6, -3.2, 
0.2, 0.8, 0.4, 0.6, -1, -1, -1, -1, -1.4, -1.4, -1.4, -1.4, -1.2, 
-1.8, -1.6, -1.8, -1.4, -1.6, -1.8, -0.2, -0.2, -0.6, -1.6, -2.2, 
-3, -3, -2.4, 0.2, 0.2, 0.4, 0.4, 0, 0.4, 0, 0.4, -0.6, 0.2, 
-1.4, -1.2, -1.6, -2, -2, -1.8, 0.2, -0.6, -0.8, -0.8, -1, -1, 
0.2, -1, -1.6, -2.2, -1.8, -0.8, -0.6, 0, -0.2, 0, -1, 0.8, 0, 
-0.6, 0.2, 0.4, 0.2, 0.2, -0.2, -0.4, -1.8, -1, -1.8, -2.4, -3.4, 
0.4, 1.4, 1.6, -0.2, -0.8, -3.2, -4.6, -4.4, -4.8, -4.2, -3.4, 
0.6, 0.4, -0.4, -0.8, -1, -0.8, -1.4, -1.4, -1.2, -1.6, -1.6, 
-1.2, -1.2, 2.6, -1, -0.4, -1, -0.4, -0.6, -0.4, -1, -1.2, -1, 
-0.8, -1.4, -1, -1.8, -2, -2, -1.6, -1.8, -2), Wave = c(1, 2, 
3, 4, 5, 6, 1, 2, 3, 4, 5, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 1, 2, 
3, 4, 6, 1, 2, 4, 6, 1, 2, 3, 4, 5, 6, 3, 5, 1, 2, 3, 4, 5, 6, 
1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 1, 2, 3, 4, 6, 
1, 3, 4, 5, 6, 1, 2, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 6, 1, 2, 3, 
4, 1, 2, 3, 4, 6, 1, 2, 3, 4, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 
1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 1, 2, 3, 4, 1, 
2, 3, 4, 5, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 
2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 
6, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 1, 2, 3, 4, 
5, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 
1, 3, 4, 1, 2, 3, 4, 5, 1, 2, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 
1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 
4, 5, 6, 1, 2, 3, 4, 1, 2, 3, 4, 6, 1, 2, 3, 4, 5, 1, 2, 3, 4, 
5, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 1, 2, 3, 
4, 5, 6, 1, 2, 3, 4, 5, 1, 2, 3, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 
6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 6, 1, 2, 3, 
4, 5, 6, 1, 2, 3, 4, 5, 6, 3, 4, 5, 6, 1, 2, 3, 4, 5), Time = structure(c(1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 4L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 3L, 5L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 6L, 
1L, 3L, 4L, 5L, 6L, 1L, 2L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 6L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 1L, 2L, 
3L, 4L, 5L, 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, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 
2L, 3L, 4L, 1L, 3L, 4L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 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, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 1L, 
2L, 3L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 
3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 3L, 4L, 5L, 6L, 1L, 2L, 
3L, 4L, 5L), levels = c("Week 1", "Week 2", "Week 4", "Week 8", 
"Week 16", "Week 32"), class = "factor")), row.names = c(NA, 
-356L), class = c("tbl_df", "tbl", "data.frame"))

@Generalized
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Hello? Is this repository still active?

@ekstroem
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Yes it is.

However, we are currently busy with other parts of life. It hasn't been forgotten

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