Temporal Dynamics of NREM EEG Microstates across
the Night

Lorena R.R. Gianotti, André Minder, Mirjam Studler, Daria Knoch

Department of Social Neuroscience and Social Psychology,
Institute of Psychology, University of Bern, Switzerland

Last edited: 2026-01-21

---

Preparation

# Load packages
library(janitor)
library(ggthemes)
library(readxl)
library(lmerTest)
library(kableExtra)
library(emmeans)
library(hms)
library(sjPlot)
library(lme4)
library(nlme)
library(tidyverse)
library(gridExtra)
library(ggplot2)

# Define parameters
size_big   <- 14
size_small <- 12

# Load microstates data
data_ms <- readr::read_csv("data/cycles/TemporalParameters_5 classes_GrandGrandMeanTemplate.csv",
                           show_col_types = FALSE)

data_ms_clean <- data_ms |>
janitor::clean_names() |>
mutate(subject = parse_number(dataset),
.before = subject,
.keep = "unused") |>
rename(ID = subject)

# Pivot to long format

## duration

data_long_duration <- data_ms_clean |>
select(ID, group, condition, contains("mean_duration"), -contains("all")) |>
pivot_longer(
cols = -c("ID", "group", "condition"),
names_to = "class",
values_to = "duration"
) |>
mutate(condition = stringr::str_sub(condition, 6, -1)) |>
rename(cycle = condition,
stage = group) |>
mutate(
class = stringr::str_sub(class, -1, -1) |> toupper(),
cycle = as.factor(cycle),
class = as.factor(class),
stage = as.factor(stage),
ID    = as.factor(ID),
duration = duration * 1000,
stage = forcats::fct_relevel(stage, c("N1", "N2", "N3")),
class = forcats::fct_relevel(class, c("A", "B", "C", "D", "E"))
)

## occurrence

data_long_occurrence <- data_ms_clean |>
select(ID, group, condition, contains("mean_occurrence"), -contains("all")) |>
pivot_longer(
cols = -c("ID", "group", "condition"),
names_to = "class",
values_to = "occurrence"
) |>
mutate(condition = stringr::str_sub(condition, 6, -1)) |>
rename(cycle = condition,
stage = group) |>
mutate(
class = stringr::str_sub(class, -1, -1) |> toupper(),
cycle = as.factor(cycle),
class = as.factor(class),
stage = as.factor(stage),
ID    = as.factor(ID),
stage = forcats::fct_relevel(stage, c("N1", "N2", "N3")),
class = forcats::fct_relevel(class, c("A", "B", "C", "D", "E"))
)

## coverage

data_long_coverage <- data_ms_clean |>
select(ID, group, condition, contains("coverage")) |>
pivot_longer(
cols = -c("ID", "group", "condition"),
names_to = "class",
values_to = "coverage"
) |>
mutate(condition = stringr::str_sub(condition, 6, -1)) |>
rename(cycle = condition,
stage = group) |>
mutate(
class = stringr::str_sub(class, -1, -1) |> toupper(),
cycle = as.factor(cycle),
class = as.factor(class),
stage = as.factor(stage),
ID    = as.factor(ID),
stage = forcats::fct_relevel(stage, c("N1", "N2", "N3")),
class = forcats::fct_relevel(class, c("A", "B", "C", "D", "E"))
)

Descriptive Statistics

Time segments analysed for each of the 12 combinations of sleep stages and sleep cycles

# count

tab_n <- data_ms_clean |>
count(group, condition, name = "n")

# compute mean and SD (seconds) + round

tab_time <- data_ms_clean |>
group_by(group, condition) |>
summarise(
mean_total_time = mean(total_time, na.rm = TRUE),
sd_total_time   = sd(total_time, na.rm = TRUE),
.groups = "drop"
) |>
mutate(
mean_total_time = round(mean_total_time, 0),
sd_total_time   = round(sd_total_time, 0),
time_summary = paste0(mean_total_time, " (± ", sd_total_time, ")")
) |>
select(group, condition, time_summary)

# merge

table_final <- tab_n |>
left_join(tab_time, by = c("group", "condition")) |>
arrange(group, condition)

# create table

table_final |>
knitr::kable(
format   = "html",
booktabs = TRUE,
caption  = NULL,
col.names = c("Sleep stage", "Sleep cycle", "n",
"Time segments (mean ± SD) in seconds"),
align = c("l","l","c","c"),
escape = TRUE
) |>
kableExtra::kable_styling(
full_width = FALSE,
position = "left",
bootstrap_options = c(),
htmltable_class = "apa-table"
)

Sleep stage	Sleep cycle	n	Time segments (mean ± SD) in seconds
N1	cycle1	48	200 (± 157)
N1	cycle2	37	178 (± 286)
N1	cycle3	34	123 (± 108)
N1	cycle4	31	123 (± 128)
N2	cycle1	54	1446 (± 602)
N2	cycle2	54	2030 (± 1057)
N2	cycle3	53	2348 (± 785)
N2	cycle4	52	2390 (± 632)
N3	cycle1	53	2608 (± 900)
N3	cycle2	51	1627 (± 710)
N3	cycle3	49	1105 (± 710)
N3	cycle4	36	583 (± 538)

Inference Statistics of the Microstate Temporal Characteristics

Duration

Model

duration_model <- lmer(
duration ~ cycle * stage * class +
(1|ID) + (1|cycle:ID) + (1|stage:ID) + (1|class:ID),
data = data_long_duration, REML = TRUE
)

sjPlot::tab_model(duration_model)

	duration
Predictors	Estimates	CI	p
(Intercept)	48.86	45.38 – 52.34	<0.001
cycle [2]	10.42	6.20 – 14.64	<0.001
cycle [3]	5.23	0.89 – 9.57	0.018
cycle [4]	2.39	-2.07 – 6.86	0.293
stage [N2]	15.79	11.99 – 19.59	<0.001
stage [N3]	61.22	57.41 – 65.03	<0.001
class [B]	-3.08	-6.88 – 0.71	0.111
class [C]	1.69	-2.10 – 5.49	0.383
class [D]	-5.08	-8.88 – -1.29	0.009
class [E]	-7.83	-11.63 – -4.04	<0.001
cycle [2] × stage [N2]	-8.06	-13.05 – -3.07	0.002
cycle [3] × stage [N2]	-6.22	-11.32 – -1.12	0.017
cycle [4] × stage [N2]	-5.87	-11.08 – -0.66	0.027
cycle [2] × stage [N3]	-19.32	-24.36 – -14.28	<0.001
cycle [3] × stage [N3]	-18.97	-24.12 – -13.82	<0.001
cycle [4] × stage [N3]	-27.07	-32.55 – -21.60	<0.001
cycle [2] × class [B]	-0.88	-6.14 – 4.38	0.742
cycle [3] × class [B]	-0.52	-5.92 – 4.89	0.852
cycle [4] × class [B]	-0.35	-5.92 – 5.22	0.901
cycle [2] × class [C]	15.97	10.70 – 21.23	<0.001
cycle [3] × class [C]	8.32	2.91 – 13.73	0.003
cycle [4] × class [C]	8.38	2.81 – 13.95	0.003
cycle [2] × class [D]	6.57	1.31 – 11.83	0.014
cycle [3] × class [D]	8.60	3.19 – 14.01	0.002
cycle [4] × class [D]	6.87	1.30 – 12.44	0.016
cycle [2] × class [E]	0.90	-4.36 – 6.16	0.737
cycle [3] × class [E]	2.65	-2.76 – 8.05	0.338
cycle [4] × class [E]	1.50	-4.07 – 7.07	0.597
stage [N2] × class [B]	-1.45	-6.20 – 3.30	0.549
stage [N3] × class [B]	-6.43	-11.20 – -1.66	0.008
stage [N2] × class [C]	9.97	5.22 – 14.73	<0.001
stage [N3] × class [C]	29.62	24.85 – 34.39	<0.001
stage [N2] × class [D]	10.64	5.89 – 15.39	<0.001
stage [N3] × class [D]	15.25	10.48 – 20.02	<0.001
stage [N2] × class [E]	5.75	1.00 – 10.50	0.018
stage [N3] × class [E]	1.40	-3.37 – 6.17	0.566
(cycle [2] × stage [N2]) × class [B]	0.49	-6.50 – 7.47	0.892
(cycle [3] × stage [N2]) × class [B]	0.60	-6.51 – 7.72	0.868
(cycle [4] × stage [N2]) × class [B]	1.06	-6.18 – 8.31	0.773
(cycle [2] × stage [N3]) × class [B]	2.66	-4.39 – 9.71	0.460
(cycle [3] × stage [N3]) × class [B]	2.80	-4.39 – 9.98	0.445
(cycle [4] × stage [N3]) × class [B]	2.12	-5.48 – 9.72	0.584
(cycle [2] × stage [N2]) × class [C]	-14.44	-21.43 – -7.45	<0.001
(cycle [3] × stage [N2]) × class [C]	-8.26	-15.37 – -1.14	0.023
(cycle [4] × stage [N2]) × class [C]	-8.60	-15.85 – -1.35	0.020
(cycle [2] × stage [N3]) × class [C]	-19.94	-26.99 – -12.89	<0.001
(cycle [3] × stage [N3]) × class [C]	-15.60	-22.79 – -8.42	<0.001
(cycle [4] × stage [N3]) × class [C]	-21.91	-29.51 – -14.31	<0.001
(cycle [2] × stage [N2]) × class [D]	-6.27	-13.26 – 0.72	0.078
(cycle [3] × stage [N2]) × class [D]	-9.24	-16.36 – -2.13	0.011
(cycle [4] × stage [N2]) × class [D]	-6.45	-13.69 – 0.80	0.081
(cycle [2] × stage [N3]) × class [D]	-8.28	-15.33 – -1.23	0.021
(cycle [3] × stage [N3]) × class [D]	-13.25	-20.43 – -6.07	<0.001
(cycle [4] × stage [N3]) × class [D]	-10.19	-17.79 – -2.59	0.009
(cycle [2] × stage [N2]) × class [E]	-0.71	-7.70 – 6.27	0.841
(cycle [3] × stage [N2]) × class [E]	-1.51	-8.63 – 5.60	0.676
(cycle [4] × stage [N2]) × class [E]	0.49	-6.76 – 7.74	0.894
(cycle [2] × stage [N3]) × class [E]	0.79	-6.26 – 7.83	0.827
(cycle [3] × stage [N3]) × class [E]	0.03	-7.15 – 7.22	0.992
(cycle [4] × stage [N3]) × class [E]	2.41	-5.19 – 10.01	0.534
Random Effects
σ2	74.25
τ00 class:ID	16.90
τ00 cycle:ID	24.38
τ00 stage:ID	20.99
τ00 ID	21.43
ICC	0.53
N ID	54
N cycle	4
N stage	3
N class	5
Observations	2760
Marginal R2 / Conditional R2	0.778 / 0.896

Type III Test

anova_duration <- anova(duration_model)  # lmerTest gives Type III by default in many setups; see note below
knitr::kable(anova_duration, digits = 2, caption = "F-Tests for Duration Model") |>
kableExtra::kable_classic(full_width = FALSE)

F-Tests for Duration Model

	Sum Sq	Mean Sq	NumDF	DenDF	F value	Pr(>F)
cycle	7636.27	2545.42	3	159.85	34.28	0
stage	193400.41	96700.20	2	103.66	1302.30	0
class	46906.02	11726.51	4	205.19	157.93	0
cycle:stage	66072.30	11012.05	6	2311.75	148.30	0
cycle:class	2730.65	227.55	12	2194.83	3.06	0
stage:class	26314.89	3289.36	8	2188.36	44.30	0
cycle:stage:class	7216.20	300.68	24	2180.24	4.05	0

Post-hoc Tests

emm_duration <- emmeans(duration_model, pairwise ~ cycle * stage | class)
emm_duration

$emmeans
class = A:
 cycle stage emmean   SE   df lower.CL upper.CL
 1     N1      48.9 1.77  712     45.4     52.3
 2     N1      59.3 1.93  925     55.5     63.1
 3     N1      54.1 1.99 1005     50.2     58.0
 4     N1      51.3 2.06 1100     47.2     55.3
 1     N2      64.6 1.71  633     61.3     68.0
 2     N2      67.0 1.71  633     63.7     70.4
 3     N2      63.7 1.72  645     60.3     67.0
 4     N2      61.2 1.73  657     57.8     64.6
 1     N3     110.1 1.72  645    106.7    113.5
 2     N3     101.2 1.74  670     97.8    104.6
 3     N3      96.3 1.76  698     92.9     99.8
 4     N3      85.4 1.95  951     81.6     89.2

class = B:
 cycle stage emmean   SE   df lower.CL upper.CL
 1     N1      45.8 1.77  712     42.3     49.3
 2     N1      55.3 1.93  925     51.5     59.1
 3     N1      50.5 1.99 1005     46.6     54.4
 4     N1      47.8 2.06 1100     43.8     51.9
 1     N2      60.1 1.71  633     56.8     63.5
 2     N2      62.1 1.71  633     58.7     65.4
 3     N2      59.2 1.72  645     55.8     62.6
 4     N2      57.3 1.73  657     53.9     60.8
 1     N3     100.6 1.72  645     97.2    104.0
 2     N3      93.4 1.74  670     90.0     96.9
 3     N3      89.1 1.76  698     85.7     92.6
 4     N3      77.7 1.95  951     73.8     81.5

class = C:
 cycle stage emmean   SE   df lower.CL upper.CL
 1     N1      50.6 1.77  712     47.1     54.0
 2     N1      76.9 1.93  925     73.2     80.7
 3     N1      64.1 1.99 1005     60.2     68.0
 4     N1      61.3 2.06 1100     57.3     65.4
 1     N2      76.3 1.71  633     73.0     79.7
 2     N2      80.2 1.71  633     76.8     83.6
 3     N2      75.4 1.72  645     72.0     78.8
 4     N2      72.6 1.73  657     69.2     76.0
 1     N3     141.4 1.72  645    138.0    144.8
 2     N3     128.5 1.74  670    125.1    131.9
 3     N3     120.4 1.76  698    116.9    123.8
 4     N3     103.2 1.95  951     99.4    107.0

class = D:
 cycle stage emmean   SE   df lower.CL upper.CL
 1     N1      43.8 1.77  712     40.3     47.3
 2     N1      60.8 1.93  925     57.0     64.6
 3     N1      57.6 1.99 1005     53.7     61.5
 4     N1      53.0 2.06 1100     49.0     57.1
 1     N2      70.2 1.71  633     66.8     73.6
 2     N2      72.9 1.71  633     69.5     76.2
 3     N2      68.6 1.72  645     65.2     72.0
 4     N2      67.2 1.73  657     63.7     70.6
 1     N3     120.3 1.72  645    116.9    123.6
 2     N3     109.6 1.74  670    106.2    113.1
 3     N3     101.9 1.76  698     98.4    105.3
 4     N3      92.2 1.95  951     88.4     96.1

class = E:
 cycle stage emmean   SE   df lower.CL upper.CL
 1     N1      41.0 1.77  712     37.5     44.5
 2     N1      52.3 1.93  925     48.6     56.1
 3     N1      48.9 1.99 1005     45.0     52.8
 4     N1      44.9 2.06 1100     40.9     49.0
 1     N2      62.6 1.71  633     59.2     65.9
 2     N2      65.1 1.71  633     61.8     68.5
 3     N2      62.7 1.72  645     59.3     66.1
 4     N2      61.1 1.73  657     57.7     64.5
 1     N3     103.6 1.72  645    100.3    107.0
 2     N3      96.4 1.74  670     93.0     99.8
 3     N3      92.6 1.76  698     89.1     96.1
 4     N3      82.9 1.95  951     79.1     86.7

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$contrasts
class = A:
 contrast              estimate   SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1  -10.420 2.15 1522  -4.844 <0.0001
 cycle1 N1 - cycle3 N1   -5.229 2.21 1588  -2.362  0.4331
 cycle1 N1 - cycle4 N1   -2.394 2.28 1637  -1.051  0.9964
 cycle1 N1 - cycle1 N2  -15.788 1.94 1116  -8.152 <0.0001
 cycle1 N1 - cycle2 N2  -18.150 2.16 1062  -8.413 <0.0001
 cycle1 N1 - cycle3 N2  -14.795 2.17 1068  -6.827 <0.0001
 cycle1 N1 - cycle4 N2  -12.311 2.18 1074  -5.656 <0.0001
 cycle1 N1 - cycle1 N3  -61.223 1.94 1123 -31.519 <0.0001
 cycle1 N1 - cycle2 N3  -52.320 2.18 1093 -23.999 <0.0001
 cycle1 N1 - cycle3 N3  -47.482 2.20 1111 -21.579 <0.0001
 cycle1 N1 - cycle4 N3  -36.544 2.35 1294 -15.541 <0.0001
 cycle2 N1 - cycle3 N1    5.191 2.33 1726   2.225  0.5317
 cycle2 N1 - cycle4 N1    8.026 2.39 1760   3.356  0.0385
 cycle2 N1 - cycle1 N2   -5.368 2.29 1226  -2.348  0.4426
 cycle2 N1 - cycle2 N2   -7.730 2.08 1296  -3.718  0.0113
 cycle2 N1 - cycle3 N2   -4.375 2.30 1232  -1.906  0.7557
 cycle2 N1 - cycle4 N2   -1.891 2.30 1238  -0.820  0.9996
 cycle2 N1 - cycle1 N3  -50.804 2.29 1236 -22.150 <0.0001
 cycle2 N1 - cycle2 N3  -41.901 2.11 1332 -19.894 <0.0001
 cycle2 N1 - cycle3 N3  -37.063 2.33 1267 -15.938 <0.0001
 cycle2 N1 - cycle4 N3  -26.124 2.47 1452 -10.567 <0.0001
 cycle3 N1 - cycle4 N1    2.835 2.42 1776   1.169  0.9911
 cycle3 N1 - cycle1 N2  -10.560 2.34 1281  -4.520  0.0004
 cycle3 N1 - cycle2 N2  -12.921 2.34 1281  -5.531 <0.0001
 cycle3 N1 - cycle3 N2   -9.566 2.14 1370  -4.475  0.0005
 cycle3 N1 - cycle4 N2   -7.082 2.35 1286  -3.012  0.1061
 cycle3 N1 - cycle1 N3  -55.995 2.34 1290 -23.890 <0.0001
 cycle3 N1 - cycle2 N3  -47.092 2.36 1311 -19.974 <0.0001
 cycle3 N1 - cycle3 N3  -42.254 2.16 1401 -19.526 <0.0001
 cycle3 N1 - cycle4 N3  -31.315 2.52 1481 -12.449 <0.0001
 cycle4 N1 - cycle1 N2  -13.394 2.40 1343  -5.590 <0.0001
 cycle4 N1 - cycle2 N2  -15.756 2.40 1343  -6.576 <0.0001
 cycle4 N1 - cycle3 N2  -12.400 2.40 1345  -5.161 <0.0001
 cycle4 N1 - cycle4 N2   -9.917 2.21 1456  -4.495  0.0005
 cycle4 N1 - cycle1 N3  -58.829 2.40 1352 -24.476 <0.0001
 cycle4 N1 - cycle2 N3  -49.926 2.42 1372 -20.656 <0.0001
 cycle4 N1 - cycle3 N3  -45.088 2.43 1385 -18.521 <0.0001
 cycle4 N1 - cycle4 N3  -34.149 2.36 1628 -14.457 <0.0001
 cycle1 N2 - cycle2 N2   -2.362 1.91 1189  -1.236  0.9860
 cycle1 N2 - cycle3 N2    0.994 1.92 1189   0.517  1.0000
 cycle1 N2 - cycle4 N2    3.478 1.93 1189   1.799  0.8185
 cycle1 N2 - cycle1 N3  -45.435 1.89 1065 -24.079 <0.0001
 cycle1 N2 - cycle2 N3  -36.532 2.13 1035 -17.167 <0.0001
 cycle1 N2 - cycle3 N3  -31.694 2.15 1052 -14.752 <0.0001
 cycle1 N2 - cycle4 N3  -20.755 2.30 1243  -9.013 <0.0001
 cycle2 N2 - cycle3 N2    3.355 1.92 1189   1.746  0.8465
 cycle2 N2 - cycle4 N2    5.839 1.93 1189   3.021  0.1037
 cycle2 N2 - cycle1 N3  -43.073 2.11 1014 -20.388 <0.0001
 cycle2 N2 - cycle2 N3  -34.170 1.90 1089 -17.946 <0.0001
 cycle2 N2 - cycle3 N3  -29.332 2.15 1052 -13.653 <0.0001
 cycle2 N2 - cycle4 N3  -18.393 2.30 1243  -7.987 <0.0001
 cycle3 N2 - cycle4 N2    2.484 1.94 1189   1.280  0.9815
 cycle3 N2 - cycle1 N3  -46.429 2.12 1019 -21.874 <0.0001
 cycle3 N2 - cycle2 N3  -37.526 2.14 1040 -17.554 <0.0001
 cycle3 N2 - cycle3 N3  -32.688 1.93 1126 -16.931 <0.0001
 cycle3 N2 - cycle4 N3  -21.749 2.31 1246  -9.416 <0.0001
 cycle4 N2 - cycle1 N3  -48.913 2.13 1025 -22.939 <0.0001
 cycle4 N2 - cycle2 N3  -40.010 2.15 1046 -18.631 <0.0001
 cycle4 N2 - cycle3 N3  -35.172 2.17 1062 -16.242 <0.0001
 cycle4 N2 - cycle4 N3  -24.233 2.10 1363 -11.513 <0.0001
 cycle1 N3 - cycle2 N3    8.903 1.95 1237   4.577  0.0003
 cycle1 N3 - cycle3 N3   13.741 1.97 1250   6.983 <0.0001
 cycle1 N3 - cycle4 N3   24.680 2.14 1454  11.554 <0.0001
 cycle2 N3 - cycle3 N3    4.838 1.98 1275   2.438  0.3812
 cycle2 N3 - cycle4 N3   15.777 2.15 1478   7.327 <0.0001
 cycle3 N3 - cycle4 N3   10.939 2.17 1486   5.038 <0.0001

class = B:
 contrast              estimate   SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1   -9.537 2.15 1522  -4.433  0.0006
 cycle1 N1 - cycle3 N1   -4.713 2.21 1588  -2.129  0.6020
 cycle1 N1 - cycle4 N1   -2.041 2.28 1637  -0.896  0.9992
 cycle1 N1 - cycle1 N2  -14.337 1.94 1116  -7.402 <0.0001
 cycle1 N1 - cycle2 N2  -16.301 2.16 1062  -7.556 <0.0001
 cycle1 N1 - cycle3 N2  -13.431 2.17 1068  -6.198 <0.0001
 cycle1 N1 - cycle4 N2  -11.572 2.18 1074  -5.316 <0.0001
 cycle1 N1 - cycle1 N3  -54.796 1.94 1123 -28.210 <0.0001
 cycle1 N1 - cycle2 N3  -47.669 2.18 1093 -21.865 <0.0001
 cycle1 N1 - cycle3 N3  -43.337 2.20 1111 -19.695 <0.0001
 cycle1 N1 - cycle4 N3  -31.886 2.35 1294 -13.561 <0.0001
 cycle2 N1 - cycle3 N1    4.824 2.33 1726   2.067  0.6465
 cycle2 N1 - cycle4 N1    7.495 2.39 1760   3.134  0.0754
 cycle2 N1 - cycle1 N2   -4.800 2.29 1226  -2.100  0.6231
 cycle2 N1 - cycle2 N2   -6.764 2.08 1296  -3.253  0.0533
 cycle2 N1 - cycle3 N2   -3.894 2.30 1232  -1.696  0.8700
 cycle2 N1 - cycle4 N2   -2.035 2.30 1238  -0.883  0.9993
 cycle2 N1 - cycle1 N3  -45.259 2.29 1236 -19.733 <0.0001
 cycle2 N1 - cycle2 N3  -38.132 2.11 1332 -18.105 <0.0001
 cycle2 N1 - cycle3 N3  -33.800 2.33 1267 -14.535 <0.0001
 cycle2 N1 - cycle4 N3  -22.349 2.47 1452  -9.041 <0.0001
 cycle3 N1 - cycle4 N1    2.672 2.42 1776   1.102  0.9946
 cycle3 N1 - cycle1 N2   -9.624 2.34 1281  -4.119  0.0024
 cycle3 N1 - cycle2 N2  -11.588 2.34 1281  -4.960 <0.0001
 cycle3 N1 - cycle3 N2   -8.718 2.14 1370  -4.078  0.0028
 cycle3 N1 - cycle4 N2   -6.859 2.35 1286  -2.917  0.1360
 cycle3 N1 - cycle1 N3  -50.083 2.34 1290 -21.368 <0.0001
 cycle3 N1 - cycle2 N3  -42.956 2.36 1311 -18.220 <0.0001
 cycle3 N1 - cycle3 N3  -38.623 2.16 1401 -17.848 <0.0001
 cycle3 N1 - cycle4 N3  -27.173 2.52 1481 -10.803 <0.0001
 cycle4 N1 - cycle1 N2  -12.296 2.40 1343  -5.132 <0.0001
 cycle4 N1 - cycle2 N2  -14.260 2.40 1343  -5.951 <0.0001
 cycle4 N1 - cycle3 N2  -11.390 2.40 1345  -4.741  0.0001
 cycle4 N1 - cycle4 N2   -9.530 2.21 1456  -4.319  0.0010
 cycle4 N1 - cycle1 N3  -52.755 2.40 1352 -21.949 <0.0001
 cycle4 N1 - cycle2 N3  -45.628 2.42 1372 -18.877 <0.0001
 cycle4 N1 - cycle3 N3  -41.295 2.43 1385 -16.963 <0.0001
 cycle4 N1 - cycle4 N3  -29.845 2.36 1628 -12.634 <0.0001
 cycle1 N2 - cycle2 N2   -1.964 1.91 1189  -1.028  0.9971
 cycle1 N2 - cycle3 N2    0.906 1.92 1189   0.471  1.0000
 cycle1 N2 - cycle4 N2    2.766 1.93 1189   1.431  0.9574
 cycle1 N2 - cycle1 N3  -40.459 1.89 1065 -21.441 <0.0001
 cycle1 N2 - cycle2 N3  -33.332 2.13 1035 -15.663 <0.0001
 cycle1 N2 - cycle3 N3  -28.999 2.15 1052 -13.498 <0.0001
 cycle1 N2 - cycle4 N3  -17.549 2.30 1243  -7.620 <0.0001
 cycle2 N2 - cycle3 N2    2.870 1.92 1189   1.493  0.9425
 cycle2 N2 - cycle4 N2    4.730 1.93 1189   2.447  0.3751
 cycle2 N2 - cycle1 N3  -38.495 2.11 1014 -18.220 <0.0001
 cycle2 N2 - cycle2 N3  -31.368 1.90 1089 -16.474 <0.0001
 cycle2 N2 - cycle3 N3  -27.035 2.15 1052 -12.583 <0.0001
 cycle2 N2 - cycle4 N3  -15.585 2.30 1243  -6.768 <0.0001
 cycle3 N2 - cycle4 N2    1.859 1.94 1189   0.959  0.9984
 cycle3 N2 - cycle1 N3  -41.365 2.12 1019 -19.489 <0.0001
 cycle3 N2 - cycle2 N3  -34.238 2.14 1040 -16.016 <0.0001
 cycle3 N2 - cycle3 N3  -29.906 1.93 1126 -15.490 <0.0001
 cycle3 N2 - cycle4 N3  -18.455 2.31 1246  -7.990 <0.0001
 cycle4 N2 - cycle1 N3  -43.224 2.13 1025 -20.272 <0.0001
 cycle4 N2 - cycle2 N3  -36.097 2.15 1046 -16.809 <0.0001
 cycle4 N2 - cycle3 N3  -31.765 2.17 1062 -14.669 <0.0001
 cycle4 N2 - cycle4 N3  -20.314 2.10 1363  -9.652 <0.0001
 cycle1 N3 - cycle2 N3    7.127 1.95 1237   3.664  0.0137
 cycle1 N3 - cycle3 N3   11.459 1.97 1250   5.823 <0.0001
 cycle1 N3 - cycle4 N3   22.910 2.14 1454  10.725 <0.0001
 cycle2 N3 - cycle3 N3    4.332 1.98 1275   2.183  0.5624
 cycle2 N3 - cycle4 N3   15.783 2.15 1478   7.330 <0.0001
 cycle3 N3 - cycle4 N3   11.451 2.17 1486   5.273 <0.0001

class = C:
 contrast              estimate   SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1  -26.386 2.15 1522 -12.266 <0.0001
 cycle1 N1 - cycle3 N1  -13.553 2.21 1588  -6.121 <0.0001
 cycle1 N1 - cycle4 N1  -10.770 2.28 1637  -4.730  0.0002
 cycle1 N1 - cycle1 N2  -25.763 1.94 1116 -13.302 <0.0001
 cycle1 N1 - cycle2 N2  -29.648 2.16 1062 -13.743 <0.0001
 cycle1 N1 - cycle3 N2  -24.837 2.17 1068 -11.461 <0.0001
 cycle1 N1 - cycle4 N2  -22.062 2.18 1074 -10.135 <0.0001
 cycle1 N1 - cycle1 N3  -90.843 1.94 1123 -46.768 <0.0001
 cycle1 N1 - cycle2 N3  -77.964 2.18 1093 -35.761 <0.0001
 cycle1 N1 - cycle3 N3  -69.823 2.20 1111 -31.732 <0.0001
 cycle1 N1 - cycle4 N3  -52.626 2.35 1294 -22.381 <0.0001
 cycle2 N1 - cycle3 N1   12.833 2.33 1726   5.500 <0.0001
 cycle2 N1 - cycle4 N1   15.615 2.39 1760   6.529 <0.0001
 cycle2 N1 - cycle1 N2    0.623 2.29 1226   0.272  1.0000
 cycle2 N1 - cycle2 N2   -3.263 2.08 1296  -1.569  0.9199
 cycle2 N1 - cycle3 N2    1.549 2.30 1232   0.675  0.9999
 cycle2 N1 - cycle4 N2    4.324 2.30 1238   1.876  0.7742
 cycle2 N1 - cycle1 N3  -64.457 2.29 1236 -28.103 <0.0001
 cycle2 N1 - cycle2 N3  -51.579 2.11 1332 -24.489 <0.0001
 cycle2 N1 - cycle3 N3  -43.437 2.33 1267 -18.680 <0.0001
 cycle2 N1 - cycle4 N3  -26.241 2.47 1452 -10.615 <0.0001
 cycle3 N1 - cycle4 N1    2.782 2.42 1776   1.148  0.9924
 cycle3 N1 - cycle1 N2  -12.210 2.34 1281  -5.226 <0.0001
 cycle3 N1 - cycle2 N2  -16.096 2.34 1281  -6.889 <0.0001
 cycle3 N1 - cycle3 N2  -11.284 2.14 1370  -5.279 <0.0001
 cycle3 N1 - cycle4 N2   -8.509 2.35 1286  -3.619  0.0160
 cycle3 N1 - cycle1 N3  -77.290 2.34 1290 -32.976 <0.0001
 cycle3 N1 - cycle2 N3  -64.412 2.36 1311 -27.321 <0.0001
 cycle3 N1 - cycle3 N3  -56.270 2.16 1401 -26.003 <0.0001
 cycle3 N1 - cycle4 N3  -39.074 2.52 1481 -15.534 <0.0001
 cycle4 N1 - cycle1 N2  -14.993 2.40 1343  -6.257 <0.0001
 cycle4 N1 - cycle2 N2  -18.878 2.40 1343  -7.879 <0.0001
 cycle4 N1 - cycle3 N2  -14.066 2.40 1345  -5.855 <0.0001
 cycle4 N1 - cycle4 N2  -11.291 2.21 1456  -5.118 <0.0001
 cycle4 N1 - cycle1 N3  -80.072 2.40 1352 -33.315 <0.0001
 cycle4 N1 - cycle2 N3  -67.194 2.42 1372 -27.800 <0.0001
 cycle4 N1 - cycle3 N3  -59.052 2.43 1385 -24.257 <0.0001
 cycle4 N1 - cycle4 N3  -41.856 2.36 1628 -17.719 <0.0001
 cycle1 N2 - cycle2 N2   -3.885 1.91 1189  -2.033  0.6708
 cycle1 N2 - cycle3 N2    0.926 1.92 1189   0.482  1.0000
 cycle1 N2 - cycle4 N2    3.701 1.93 1189   1.915  0.7499
 cycle1 N2 - cycle1 N3  -65.080 1.89 1065 -34.490 <0.0001
 cycle1 N2 - cycle2 N3  -52.201 2.13 1035 -24.531 <0.0001
 cycle1 N2 - cycle3 N3  -44.060 2.15 1052 -20.507 <0.0001
 cycle1 N2 - cycle4 N3  -26.863 2.30 1243 -11.665 <0.0001
 cycle2 N2 - cycle3 N2    4.812 1.92 1189   2.503  0.3387
 cycle2 N2 - cycle4 N2    7.586 1.93 1189   3.925  0.0052
 cycle2 N2 - cycle1 N3  -61.194 2.11 1014 -28.965 <0.0001
 cycle2 N2 - cycle2 N3  -48.316 1.90 1089 -25.376 <0.0001
 cycle2 N2 - cycle3 N3  -40.174 2.15 1052 -18.699 <0.0001
 cycle2 N2 - cycle4 N3  -22.978 2.30 1243  -9.978 <0.0001
 cycle3 N2 - cycle4 N2    2.775 1.94 1189   1.430  0.9575
 cycle3 N2 - cycle1 N3  -66.006 2.12 1019 -31.098 <0.0001
 cycle3 N2 - cycle2 N3  -53.128 2.14 1040 -24.852 <0.0001
 cycle3 N2 - cycle3 N3  -44.986 1.93 1126 -23.301 <0.0001
 cycle3 N2 - cycle4 N3  -27.790 2.31 1246 -12.032 <0.0001
 cycle4 N2 - cycle1 N3  -68.781 2.13 1025 -32.257 <0.0001
 cycle4 N2 - cycle2 N3  -55.903 2.15 1046 -26.032 <0.0001
 cycle4 N2 - cycle3 N3  -47.761 2.17 1062 -22.056 <0.0001
 cycle4 N2 - cycle4 N3  -30.564 2.10 1363 -14.522 <0.0001
 cycle1 N3 - cycle2 N3   12.878 1.95 1237   6.620 <0.0001
 cycle1 N3 - cycle3 N3   21.020 1.97 1250  10.682 <0.0001
 cycle1 N3 - cycle4 N3   38.216 2.14 1454  17.891 <0.0001
 cycle2 N3 - cycle3 N3    8.142 1.98 1275   4.102  0.0025
 cycle2 N3 - cycle4 N3   25.338 2.15 1478  11.768 <0.0001
 cycle3 N3 - cycle4 N3   17.196 2.17 1486   7.919 <0.0001

class = D:
 contrast              estimate   SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1  -16.991 2.15 1522  -7.899 <0.0001
 cycle1 N1 - cycle3 N1  -13.829 2.21 1588  -6.246 <0.0001
 cycle1 N1 - cycle4 N1   -9.263 2.28 1637  -4.068  0.0029
 cycle1 N1 - cycle1 N2  -26.428 1.94 1116 -13.645 <0.0001
 cycle1 N1 - cycle2 N2  -29.089 2.16 1062 -13.483 <0.0001
 cycle1 N1 - cycle3 N2  -24.791 2.17 1068 -11.440 <0.0001
 cycle1 N1 - cycle4 N2  -23.373 2.18 1074 -10.738 <0.0001
 cycle1 N1 - cycle1 N3  -76.473 1.94 1123 -39.370 <0.0001
 cycle1 N1 - cycle2 N3  -65.859 2.18 1093 -30.209 <0.0001
 cycle1 N1 - cycle3 N3  -58.081 2.20 1111 -26.396 <0.0001
 cycle1 N1 - cycle4 N3  -48.468 2.35 1294 -20.613 <0.0001
 cycle2 N1 - cycle3 N1    3.163 2.33 1726   1.355  0.9714
 cycle2 N1 - cycle4 N1    7.728 2.39 1760   3.231  0.0566
 cycle2 N1 - cycle1 N2   -9.437 2.29 1226  -4.128  0.0023
 cycle2 N1 - cycle2 N2  -12.098 2.08 1296  -5.818 <0.0001
 cycle2 N1 - cycle3 N2   -7.800 2.30 1232  -3.398  0.0339
 cycle2 N1 - cycle4 N2   -6.381 2.30 1238  -2.769  0.1949
 cycle2 N1 - cycle1 N3  -59.481 2.29 1236 -25.934 <0.0001
 cycle2 N1 - cycle2 N3  -48.868 2.11 1332 -23.202 <0.0001
 cycle2 N1 - cycle3 N3  -41.089 2.33 1267 -17.670 <0.0001
 cycle2 N1 - cycle4 N3  -31.477 2.47 1452 -12.733 <0.0001
 cycle3 N1 - cycle4 N1    4.566 2.42 1776   1.883  0.7697
 cycle3 N1 - cycle1 N2  -12.599 2.34 1281  -5.393 <0.0001
 cycle3 N1 - cycle2 N2  -15.260 2.34 1281  -6.532 <0.0001
 cycle3 N1 - cycle3 N2  -10.963 2.14 1370  -5.128 <0.0001
 cycle3 N1 - cycle4 N2   -9.544 2.35 1286  -4.059  0.0030
 cycle3 N1 - cycle1 N3  -62.644 2.34 1290 -26.727 <0.0001
 cycle3 N1 - cycle2 N3  -52.031 2.36 1311 -22.069 <0.0001
 cycle3 N1 - cycle3 N3  -44.252 2.16 1401 -20.449 <0.0001
 cycle3 N1 - cycle4 N3  -34.639 2.52 1481 -13.771 <0.0001
 cycle4 N1 - cycle1 N2  -17.165 2.40 1343  -7.164 <0.0001
 cycle4 N1 - cycle2 N2  -19.826 2.40 1343  -8.274 <0.0001
 cycle4 N1 - cycle3 N2  -15.528 2.40 1345  -6.463 <0.0001
 cycle4 N1 - cycle4 N2  -14.110 2.21 1456  -6.395 <0.0001
 cycle4 N1 - cycle1 N3  -67.210 2.40 1352 -27.963 <0.0001
 cycle4 N1 - cycle2 N3  -56.597 2.42 1372 -23.416 <0.0001
 cycle4 N1 - cycle3 N3  -48.818 2.43 1385 -20.053 <0.0001
 cycle4 N1 - cycle4 N3  -39.205 2.36 1628 -16.597 <0.0001
 cycle1 N2 - cycle2 N2   -2.661 1.91 1189  -1.392  0.9651
 cycle1 N2 - cycle3 N2    1.637 1.92 1189   0.852  0.9995
 cycle1 N2 - cycle4 N2    3.056 1.93 1189   1.581  0.9159
 cycle1 N2 - cycle1 N3  -50.045 1.89 1065 -26.522 <0.0001
 cycle1 N2 - cycle2 N3  -39.431 2.13 1035 -18.530 <0.0001
 cycle1 N2 - cycle3 N3  -31.652 2.15 1052 -14.732 <0.0001
 cycle1 N2 - cycle4 N3  -22.040 2.30 1243  -9.571 <0.0001
 cycle2 N2 - cycle3 N2    4.298 1.92 1189   2.236  0.5237
 cycle2 N2 - cycle4 N2    5.716 1.93 1189   2.957  0.1227
 cycle2 N2 - cycle1 N3  -47.384 2.11 1014 -22.428 <0.0001
 cycle2 N2 - cycle2 N3  -36.770 1.90 1089 -19.312 <0.0001
 cycle2 N2 - cycle3 N3  -28.992 2.15 1052 -13.494 <0.0001
 cycle2 N2 - cycle4 N3  -19.379 2.30 1243  -8.415 <0.0001
 cycle3 N2 - cycle4 N2    1.419 1.94 1189   0.731  0.9999
 cycle3 N2 - cycle1 N3  -51.681 2.12 1019 -24.349 <0.0001
 cycle3 N2 - cycle2 N3  -41.068 2.14 1040 -19.211 <0.0001
 cycle3 N2 - cycle3 N3  -33.289 1.93 1126 -17.243 <0.0001
 cycle3 N2 - cycle4 N3  -23.677 2.31 1246 -10.251 <0.0001
 cycle4 N2 - cycle1 N3  -53.100 2.13 1025 -24.903 <0.0001
 cycle4 N2 - cycle2 N3  -42.487 2.15 1046 -19.785 <0.0001
 cycle4 N2 - cycle3 N3  -34.708 2.17 1062 -16.028 <0.0001
 cycle4 N2 - cycle4 N3  -25.095 2.10 1363 -11.923 <0.0001
 cycle1 N3 - cycle2 N3   10.613 1.95 1237   5.456 <0.0001
 cycle1 N3 - cycle3 N3   18.392 1.97 1250   9.346 <0.0001
 cycle1 N3 - cycle4 N3   28.005 2.14 1454  13.110 <0.0001
 cycle2 N3 - cycle3 N3    7.779 1.98 1275   3.920  0.0053
 cycle2 N3 - cycle4 N3   17.392 2.15 1478   8.077 <0.0001
 cycle3 N3 - cycle4 N3    9.613 2.17 1486   4.427  0.0006

class = E:
 contrast              estimate   SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1  -11.321 2.15 1522  -5.263 <0.0001
 cycle1 N1 - cycle3 N1   -7.874 2.21 1588  -3.557  0.0198
 cycle1 N1 - cycle4 N1   -3.897 2.28 1637  -1.712  0.8631
 cycle1 N1 - cycle1 N2  -21.538 1.94 1116 -11.120 <0.0001
 cycle1 N1 - cycle2 N2  -24.087 2.16 1062 -11.165 <0.0001
 cycle1 N1 - cycle3 N2  -21.675 2.17 1068 -10.002 <0.0001
 cycle1 N1 - cycle4 N2  -20.054 2.18 1074  -9.213 <0.0001
 cycle1 N1 - cycle1 N3  -62.620 1.94 1123 -32.238 <0.0001
 cycle1 N1 - cycle2 N3  -55.403 2.18 1093 -25.413 <0.0001
 cycle1 N1 - cycle3 N3  -51.559 2.20 1111 -23.432 <0.0001
 cycle1 N1 - cycle4 N3  -41.855 2.35 1294 -17.800 <0.0001
 cycle2 N1 - cycle3 N1    3.446 2.33 1726   1.477  0.9468
 cycle2 N1 - cycle4 N1    7.423 2.39 1760   3.104  0.0822
 cycle2 N1 - cycle1 N2  -10.217 2.29 1226  -4.469  0.0005
 cycle2 N1 - cycle2 N2  -12.766 2.08 1296  -6.140 <0.0001
 cycle2 N1 - cycle3 N2  -10.355 2.30 1232  -4.511  0.0004
 cycle2 N1 - cycle4 N2   -8.734 2.30 1238  -3.789  0.0087
 cycle2 N1 - cycle1 N3  -51.299 2.29 1236 -22.366 <0.0001
 cycle2 N1 - cycle2 N3  -44.083 2.11 1332 -20.930 <0.0001
 cycle2 N1 - cycle3 N3  -40.238 2.33 1267 -17.304 <0.0001
 cycle2 N1 - cycle4 N3  -30.534 2.47 1452 -12.351 <0.0001
 cycle3 N1 - cycle4 N1    3.977 2.42 1776   1.640  0.8939
 cycle3 N1 - cycle1 N2  -13.663 2.34 1281  -5.848 <0.0001
 cycle3 N1 - cycle2 N2  -16.212 2.34 1281  -6.939 <0.0001
 cycle3 N1 - cycle3 N2  -13.801 2.14 1370  -6.456 <0.0001
 cycle3 N1 - cycle4 N2  -12.180 2.35 1286  -5.181 <0.0001
 cycle3 N1 - cycle1 N3  -54.745 2.34 1290 -23.357 <0.0001
 cycle3 N1 - cycle2 N3  -47.529 2.36 1311 -20.160 <0.0001
 cycle3 N1 - cycle3 N3  -43.684 2.16 1401 -20.187 <0.0001
 cycle3 N1 - cycle4 N3  -33.980 2.52 1481 -13.509 <0.0001
 cycle4 N1 - cycle1 N2  -17.640 2.40 1343  -7.362 <0.0001
 cycle4 N1 - cycle2 N2  -20.189 2.40 1343  -8.426 <0.0001
 cycle4 N1 - cycle3 N2  -17.778 2.40 1345  -7.399 <0.0001
 cycle4 N1 - cycle4 N2  -16.157 2.21 1456  -7.323 <0.0001
 cycle4 N1 - cycle1 N3  -58.722 2.40 1352 -24.432 <0.0001
 cycle4 N1 - cycle2 N3  -51.506 2.42 1372 -21.309 <0.0001
 cycle4 N1 - cycle3 N3  -47.662 2.43 1385 -19.578 <0.0001
 cycle4 N1 - cycle4 N3  -37.958 2.36 1628 -16.069 <0.0001
 cycle1 N2 - cycle2 N2   -2.549 1.91 1189  -1.334  0.9746
 cycle1 N2 - cycle3 N2   -0.138 1.92 1189  -0.072  1.0000
 cycle1 N2 - cycle4 N2    1.483 1.93 1189   0.767  0.9998
 cycle1 N2 - cycle1 N3  -41.082 1.89 1065 -21.772 <0.0001
 cycle1 N2 - cycle2 N3  -33.866 2.13 1035 -15.914 <0.0001
 cycle1 N2 - cycle3 N3  -30.021 2.15 1052 -13.973 <0.0001
 cycle1 N2 - cycle4 N3  -20.317 2.30 1243  -8.823 <0.0001
 cycle2 N2 - cycle3 N2    2.411 1.92 1189   1.254  0.9842
 cycle2 N2 - cycle4 N2    4.032 1.93 1189   2.086  0.6329
 cycle2 N2 - cycle1 N3  -38.533 2.11 1014 -18.239 <0.0001
 cycle2 N2 - cycle2 N3  -31.317 1.90 1089 -16.448 <0.0001
 cycle2 N2 - cycle3 N3  -27.472 2.15 1052 -12.787 <0.0001
 cycle2 N2 - cycle4 N3  -17.768 2.30 1243  -7.716 <0.0001
 cycle3 N2 - cycle4 N2    1.621 1.94 1189   0.836  0.9996
 cycle3 N2 - cycle1 N3  -40.944 2.12 1019 -19.290 <0.0001
 cycle3 N2 - cycle2 N3  -33.728 2.14 1040 -15.777 <0.0001
 cycle3 N2 - cycle3 N3  -29.883 1.93 1126 -15.479 <0.0001
 cycle3 N2 - cycle4 N3  -20.179 2.31 1246  -8.737 <0.0001
 cycle4 N2 - cycle1 N3  -42.565 2.13 1025 -19.963 <0.0001
 cycle4 N2 - cycle2 N3  -35.349 2.15 1046 -16.461 <0.0001
 cycle4 N2 - cycle3 N3  -31.504 2.17 1062 -14.549 <0.0001
 cycle4 N2 - cycle4 N3  -21.800 2.10 1363 -10.358 <0.0001
 cycle1 N3 - cycle2 N3    7.216 1.95 1237   3.710  0.0116
 cycle1 N3 - cycle3 N3   11.061 1.97 1250   5.621 <0.0001
 cycle1 N3 - cycle4 N3   20.765 2.14 1454   9.721 <0.0001
 cycle2 N3 - cycle3 N3    3.844 1.98 1275   1.937  0.7356
 cycle2 N3 - cycle4 N3   13.549 2.15 1478   6.292 <0.0001
 cycle3 N3 - cycle4 N3    9.704 2.17 1486   4.469  0.0005

Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 12 estimates 

duration_model_emm <- as.data.frame(emm_duration$emmeans)

Plot

duration_emm_plot <- duration_model_emm |> 
  select(class, cycle, stage, emmean, lower.CL, upper.CL) |> 
  slice(1:60) |> 
  ggplot(aes(group = stage, color = stage, y = emmean, x = cycle)) +
  geom_line(fill = cycle, alpha = 1) +
  geom_point(stat="identity", size = 3, alpha = 1) +
  geom_errorbar(aes(ymin=lower.CL, ymax=upper.CL), width=.2, alpha = 1) +
  ggtitle("Microstate Classes") +
  facet_wrap( ~ class, ncol = 5) +
  theme_bw() +
  theme(
    plot.title = element_text(size = size_big, hjust= 0.5),
    axis.text = element_text(size = size_small, family = "sans"),
    axis.title = element_text(size = size_big, family = "sans"),
    strip.text.x = element_text(size = size_big, family = "sans"),
    strip.background = element_blank(),
    legend.title = element_blank(),
    legend.text = element_text(size = size_small),
    legend.position="right",
  ) +
  labs(color = "Sleep Stages") +
  scale_y_continuous(name = "Duration (ms)", limits = c(0,150), expand = c(0,0)) +
  scale_x_discrete(name= "Sleep Cycles") +
  scale_color_manual(values = c("#1b9e77","#d95f02","#7570b3")) #color-blind friendly colors

duration_emm_plot

ggsave("duration_plot.png",
        plot = duration_emm_plot,
        width = 9,
        height = 7, 
        dpi = 1200,
        bg = "transparent",
        path = "R:/MicrostateAnalysis/sleepData/MS_SleepMarkers/scripts/graphs/sleep/cycles")

Occurence

Model

occurrence_model <- lmer(
occurrence ~ cycle * stage * class +
(1|ID) + (1|cycle:ID) + (1|stage:ID) + (1|class:ID),
data = data_long_occurrence, REML = TRUE
)

sjPlot::tab_model(occurrence_model)

	occurrence
Predictors	Estimates	CI	p
(Intercept)	4.65	4.52 – 4.79	<0.001
cycle [2]	-1.23	-1.38 – -1.09	<0.001
cycle [3]	-1.02	-1.17 – -0.87	<0.001
cycle [4]	-0.81	-0.96 – -0.66	<0.001
stage [N2]	-1.80	-1.93 – -1.67	<0.001
stage [N3]	-3.02	-3.15 – -2.89	<0.001
class [B]	-0.15	-0.29 – -0.01	0.036
class [C]	0.28	0.14 – 0.42	<0.001
class [D]	-0.63	-0.77 – -0.48	<0.001
class [E]	-0.87	-1.01 – -0.73	<0.001
cycle [2] × stage [N2]	1.12	0.96 – 1.29	<0.001
cycle [3] × stage [N2]	1.03	0.86 – 1.20	<0.001
cycle [4] × stage [N2]	0.85	0.68 – 1.02	<0.001
cycle [2] × stage [N3]	1.44	1.27 – 1.61	<0.001
cycle [3] × stage [N3]	1.31	1.14 – 1.48	<0.001
cycle [4] × stage [N3]	1.40	1.21 – 1.58	<0.001
cycle [2] × class [B]	-0.09	-0.27 – 0.08	0.288
cycle [3] × class [B]	-0.10	-0.28 – 0.08	0.263
cycle [4] × class [B]	-0.19	-0.37 – -0.00	0.047
cycle [2] × class [C]	0.42	0.25 – 0.59	<0.001
cycle [3] × class [C]	0.40	0.22 – 0.58	<0.001
cycle [4] × class [C]	0.42	0.23 – 0.60	<0.001
cycle [2] × class [D]	0.70	0.53 – 0.88	<0.001
cycle [3] × class [D]	0.86	0.68 – 1.04	<0.001
cycle [4] × class [D]	0.79	0.61 – 0.98	<0.001
cycle [2] × class [E]	0.30	0.13 – 0.47	0.001
cycle [3] × class [E]	0.35	0.17 – 0.53	<0.001
cycle [4] × class [E]	0.29	0.11 – 0.48	0.002
stage [N2] × class [B]	-0.11	-0.27 – 0.05	0.168
stage [N3] × class [B]	-0.05	-0.21 – 0.11	0.530
stage [N2] × class [C]	0.32	0.17 – 0.48	<0.001
stage [N3] × class [C]	0.20	0.04 – 0.36	0.014
stage [N2] × class [D]	0.94	0.78 – 1.10	<0.001
stage [N3] × class [D]	0.82	0.67 – 0.98	<0.001
stage [N2] × class [E]	0.87	0.71 – 1.02	<0.001
stage [N3] × class [E]	0.81	0.65 – 0.97	<0.001
(cycle [2] × stage [N2]) × class [B]	0.08	-0.15 – 0.32	0.479
(cycle [3] × stage [N2]) × class [B]	0.09	-0.15 – 0.32	0.467
(cycle [4] × stage [N2]) × class [B]	0.21	-0.03 – 0.45	0.093
(cycle [2] × stage [N3]) × class [B]	0.10	-0.14 – 0.33	0.420
(cycle [3] × stage [N3]) × class [B]	0.12	-0.12 – 0.36	0.332
(cycle [4] × stage [N3]) × class [B]	0.15	-0.10 – 0.40	0.243
(cycle [2] × stage [N2]) × class [C]	-0.41	-0.65 – -0.18	<0.001
(cycle [3] × stage [N2]) × class [C]	-0.34	-0.57 – -0.10	0.005
(cycle [4] × stage [N2]) × class [C]	-0.28	-0.52 – -0.04	0.024
(cycle [2] × stage [N3]) × class [C]	-0.45	-0.68 – -0.22	<0.001
(cycle [3] × stage [N3]) × class [C]	-0.34	-0.58 – -0.10	0.005
(cycle [4] × stage [N3]) × class [C]	-0.37	-0.62 – -0.12	0.004
(cycle [2] × stage [N2]) × class [D]	-0.75	-0.99 – -0.52	<0.001
(cycle [3] × stage [N2]) × class [D]	-0.87	-1.11 – -0.64	<0.001
(cycle [4] × stage [N2]) × class [D]	-0.66	-0.90 – -0.42	<0.001
(cycle [2] × stage [N3]) × class [D]	-0.75	-0.98 – -0.51	<0.001
(cycle [3] × stage [N3]) × class [D]	-0.88	-1.12 – -0.64	<0.001
(cycle [4] × stage [N3]) × class [D]	-0.74	-1.00 – -0.49	<0.001
(cycle [2] × stage [N2]) × class [E]	-0.27	-0.50 – -0.03	0.024
(cycle [3] × stage [N2]) × class [E]	-0.26	-0.49 – -0.02	0.032
(cycle [4] × stage [N2]) × class [E]	-0.13	-0.37 – 0.11	0.294
(cycle [2] × stage [N3]) × class [E]	-0.31	-0.54 – -0.07	0.010
(cycle [3] × stage [N3]) × class [E]	-0.26	-0.49 – -0.02	0.035
(cycle [4] × stage [N3]) × class [E]	-0.24	-0.49 – 0.02	0.067
Random Effects
σ2	0.08
τ00 class:ID	0.05
τ00 cycle:ID	0.03
τ00 stage:ID	0.03
τ00 ID	0.03
ICC	0.64
N ID	54
N cycle	4
N stage	3
N class	5
Observations	2760
Marginal R2 / Conditional R2	0.760 / 0.914

Type III Test

anova_occurrence <- anova(occurrence_model)
knitr::kable(anova_occurrence, digits = 3, caption = "F-Tests for the Occurrence Model") |>
kableExtra::kable_classic(full_width = FALSE)

F-Tests for the Occurrence Model

	Sum Sq	Mean Sq	NumDF	DenDF	F value	Pr(>F)
cycle	8.548	2.849	3	159.652	34.946	0
stage	184.376	92.188	2	97.841	1130.624	0
class	34.116	8.529	4	216.065	104.601	0
cycle:stage	102.094	17.016	6	2306.098	208.685	0
cycle:class	7.912	0.659	12	2181.896	8.086	0
stage:class	38.692	4.837	8	2178.565	59.317	0
cycle:stage:class	11.794	0.491	24	2174.557	6.027	0

Post-hoc Tests

emm_occurrence <- emmeans(occurrence_model, pairwise ~ cycle * stage | class)
emm_occurrence

$emmeans
class = A:
 cycle stage emmean     SE  df lower.CL upper.CL
 1     N1      4.65 0.0667 571     4.52     4.79
 2     N1      3.42 0.0714 721     3.28     3.56
 3     N1      3.63 0.0732 780     3.49     3.78
 4     N1      3.84 0.0754 852     3.69     3.99
 1     N2      2.85 0.0648 517     2.73     2.98
 2     N2      2.74 0.0648 517     2.61     2.87
 3     N2      2.86 0.0652 526     2.73     2.99
 4     N2      2.89 0.0655 535     2.76     3.02
 1     N3      1.64 0.0651 525     1.51     1.76
 2     N3      1.84 0.0656 542     1.71     1.97
 3     N3      1.92 0.0664 562     1.79     2.05
 4     N3      2.22 0.0720 740     2.08     2.36

class = B:
 cycle stage emmean     SE  df lower.CL upper.CL
 1     N1      4.50 0.0667 571     4.37     4.63
 2     N1      3.17 0.0714 721     3.03     3.31
 3     N1      3.38 0.0732 780     3.24     3.52
 4     N1      3.50 0.0754 852     3.36     3.65
 1     N2      2.59 0.0648 517     2.46     2.72
 2     N2      2.47 0.0648 517     2.34     2.60
 3     N2      2.58 0.0652 526     2.46     2.71
 4     N2      2.65 0.0655 535     2.52     2.78
 1     N3      1.43 0.0651 525     1.30     1.56
 2     N3      1.64 0.0656 542     1.51     1.77
 3     N3      1.73 0.0664 562     1.60     1.86
 4     N3      1.98 0.0720 740     1.84     2.12

class = C:
 cycle stage emmean     SE  df lower.CL upper.CL
 1     N1      4.93 0.0667 571     4.80     5.06
 2     N1      4.12 0.0714 721     3.98     4.26
 3     N1      4.31 0.0732 780     4.16     4.45
 4     N1      4.53 0.0754 852     4.39     4.68
 1     N2      3.45 0.0648 517     3.33     3.58
 2     N2      3.35 0.0648 517     3.22     3.47
 3     N2      3.52 0.0652 526     3.39     3.65
 4     N2      3.63 0.0655 535     3.50     3.76
 1     N3      2.11 0.0651 525     1.98     2.24
 2     N3      2.28 0.0656 542     2.15     2.41
 3     N3      2.45 0.0664 562     2.32     2.59
 4     N3      2.74 0.0720 740     2.60     2.88

class = D:
 cycle stage emmean     SE  df lower.CL upper.CL
 1     N1      4.03 0.0667 571     3.90     4.16
 2     N1      3.50 0.0714 721     3.36     3.64
 3     N1      3.87 0.0732 780     3.73     4.01
 4     N1      4.01 0.0754 852     3.86     4.16
 1     N2      3.17 0.0648 517     3.04     3.30
 2     N2      3.01 0.0648 517     2.88     3.13
 3     N2      3.17 0.0652 526     3.04     3.29
 4     N2      3.33 0.0655 535     3.21     3.46
 1     N3      1.83 0.0651 525     1.71     1.96
 2     N3      1.99 0.0656 542     1.86     2.12
 3     N3      2.10 0.0664 562     1.97     2.23
 4     N3      2.46 0.0720 740     2.32     2.60

class = E:
 cycle stage emmean     SE  df lower.CL upper.CL
 1     N1      3.78 0.0667 571     3.65     3.91
 2     N1      2.85 0.0714 721     2.71     2.99
 3     N1      3.11 0.0732 780     2.97     3.25
 4     N1      3.26 0.0754 852     3.11     3.41
 1     N2      2.85 0.0648 517     2.72     2.98
 2     N2      2.77 0.0648 517     2.64     2.90
 3     N2      2.95 0.0652 526     2.82     3.08
 4     N2      3.05 0.0655 535     2.92     3.18
 1     N3      1.57 0.0651 525     1.44     1.70
 2     N3      1.77 0.0656 542     1.64     1.90
 3     N3      1.95 0.0664 562     1.82     2.08
 4     N3      2.21 0.0720 740     2.07     2.35

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$contrasts
class = A:
 contrast              estimate     SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1  1.23476 0.0725 1401  17.041 <0.0001
 cycle1 N1 - cycle3 N1  1.02062 0.0746 1468  13.678 <0.0001
 cycle1 N1 - cycle4 N1  0.81288 0.0767 1517  10.595 <0.0001
 cycle1 N1 - cycle1 N2  1.80035 0.0671  834  26.836 <0.0001
 cycle1 N1 - cycle2 N2  1.91261 0.0751  878  25.465 <0.0001
 cycle1 N1 - cycle3 N2  1.79254 0.0754  884  23.762 <0.0001
 cycle1 N1 - cycle4 N2  1.76246 0.0758  890  23.264 <0.0001
 cycle1 N1 - cycle1 N3  3.01953 0.0673  840  44.892 <0.0001
 cycle1 N1 - cycle2 N3  2.81586 0.0758  904  37.129 <0.0001
 cycle1 N1 - cycle3 N3  2.73354 0.0765  921  35.726 <0.0001
 cycle1 N1 - cycle4 N3  2.43611 0.0814 1085  29.930 <0.0001
 cycle2 N1 - cycle3 N1 -0.21414 0.0786 1607  -2.726  0.2145
 cycle2 N1 - cycle4 N1 -0.42187 0.0805 1644  -5.240 <0.0001
 cycle2 N1 - cycle1 N2  0.56559 0.0793 1022   7.135 <0.0001
 cycle2 N1 - cycle2 N2  0.67786 0.0717  994   9.452 <0.0001
 cycle2 N1 - cycle3 N2  0.55778 0.0796 1028   7.008 <0.0001
 cycle2 N1 - cycle4 N2  0.52770 0.0799 1034   6.604 <0.0001
 cycle2 N1 - cycle1 N3  1.78477 0.0795 1030  22.446 <0.0001
 cycle2 N1 - cycle2 N3  1.58111 0.0726 1027  21.779 <0.0001
 cycle2 N1 - cycle3 N3  1.49878 0.0805 1060  18.608 <0.0001
 cycle2 N1 - cycle4 N3  1.20135 0.0853 1229  14.077 <0.0001
 cycle3 N1 - cycle4 N1 -0.20774 0.0815 1663  -2.548  0.3109
 cycle3 N1 - cycle1 N2  0.77973 0.0809 1072   9.637 <0.0001
 cycle3 N1 - cycle2 N2  0.89199 0.0809 1072  11.025 <0.0001
 cycle3 N1 - cycle3 N2  0.77192 0.0736 1062  10.486 <0.0001
 cycle3 N1 - cycle4 N2  0.74184 0.0814 1079   9.115 <0.0001
 cycle3 N1 - cycle1 N3  1.99890 0.0812 1081  24.631 <0.0001
 cycle3 N1 - cycle2 N3  1.79524 0.0816 1099  22.001 <0.0001
 cycle3 N1 - cycle3 N3  1.71291 0.0744 1091  23.009 <0.0001
 cycle3 N1 - cycle4 N3  1.41549 0.0867 1262  16.322 <0.0001
 cycle4 N1 - cycle1 N2  0.98746 0.0829 1131  11.918 <0.0001
 cycle4 N1 - cycle2 N2  1.09973 0.0829 1131  13.272 <0.0001
 cycle4 N1 - cycle3 N2  0.97966 0.0831 1135  11.794 <0.0001
 cycle4 N1 - cycle4 N2  0.94957 0.0758 1143  12.520 <0.0001
 cycle4 N1 - cycle1 N3  2.20664 0.0831 1140  26.553 <0.0001
 cycle4 N1 - cycle2 N3  2.00298 0.0835 1158  23.976 <0.0001
 cycle4 N1 - cycle3 N3  1.92065 0.0841 1172  22.834 <0.0001
 cycle4 N1 - cycle4 N3  1.62322 0.0809 1317  20.070 <0.0001
 cycle1 N2 - cycle2 N2  0.11226 0.0645 1076   1.740  0.8490
 cycle1 N2 - cycle3 N2 -0.00781 0.0649 1076  -0.120  1.0000
 cycle1 N2 - cycle4 N2 -0.03789 0.0652 1075  -0.581  1.0000
 cycle1 N2 - cycle1 N3  1.21918 0.0654  791  18.632 <0.0001
 cycle1 N2 - cycle2 N3  1.01551 0.0741  855  13.700 <0.0001
 cycle1 N2 - cycle3 N3  0.93319 0.0748  872  12.475 <0.0001
 cycle1 N2 - cycle4 N3  0.63576 0.0798 1040   7.968 <0.0001
 cycle2 N2 - cycle3 N2 -0.12007 0.0649 1076  -1.851  0.7892
 cycle2 N2 - cycle4 N2 -0.15015 0.0652 1075  -2.301  0.4762
 cycle2 N2 - cycle1 N3  1.10691 0.0736  838  15.032 <0.0001
 cycle2 N2 - cycle2 N3  0.90325 0.0660  810  13.689 <0.0001
 cycle2 N2 - cycle3 N3  0.82092 0.0748  872  10.975 <0.0001
 cycle2 N2 - cycle4 N3  0.52349 0.0798 1040   6.561 <0.0001
 cycle3 N2 - cycle4 N2 -0.03008 0.0655 1075  -0.459  1.0000
 cycle3 N2 - cycle1 N3  1.22698 0.0740  844  16.588 <0.0001
 cycle3 N2 - cycle2 N3  1.02332 0.0745  861  13.744 <0.0001
 cycle3 N2 - cycle3 N3  0.94099 0.0668  841  14.078 <0.0001
 cycle3 N2 - cycle4 N3  0.64357 0.0800 1044   8.044 <0.0001
 cycle4 N2 - cycle1 N3  1.25707 0.0743  850  16.921 <0.0001
 cycle4 N2 - cycle2 N3  1.05340 0.0748  867  14.087 <0.0001
 cycle4 N2 - cycle3 N3  0.97108 0.0754  882  12.887 <0.0001
 cycle4 N2 - cycle4 N3  0.67365 0.0725 1049   9.294 <0.0001
 cycle1 N3 - cycle2 N3 -0.20366 0.0656 1122  -3.103  0.0827
 cycle1 N3 - cycle3 N3 -0.28599 0.0664 1134  -4.307  0.0011
 cycle1 N3 - cycle4 N3 -0.58342 0.0720 1335  -8.103 <0.0001
 cycle2 N3 - cycle3 N3 -0.08233 0.0670 1159  -1.230  0.9866
 cycle2 N3 - cycle4 N3 -0.37975 0.0726 1358  -5.233 <0.0001
 cycle3 N3 - cycle4 N3 -0.29743 0.0732 1368  -4.064  0.0030

class = B:
 contrast              estimate     SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1  1.32932 0.0725 1401  18.346 <0.0001
 cycle1 N1 - cycle3 N1  1.12321 0.0746 1468  15.053 <0.0001
 cycle1 N1 - cycle4 N1  1.00006 0.0767 1517  13.034 <0.0001
 cycle1 N1 - cycle1 N2  1.91110 0.0671  834  28.487 <0.0001
 cycle1 N1 - cycle2 N2  2.03417 0.0751  878  27.083 <0.0001
 cycle1 N1 - cycle3 N2  1.91829 0.0754  884  25.429 <0.0001
 cycle1 N1 - cycle4 N2  1.85443 0.0758  890  24.478 <0.0001
 cycle1 N1 - cycle1 N3  3.07017 0.0673  840  45.645 <0.0001
 cycle1 N1 - cycle2 N3  2.86486 0.0758  904  37.775 <0.0001
 cycle1 N1 - cycle3 N3  2.76883 0.0765  921  36.188 <0.0001
 cycle1 N1 - cycle4 N3  2.52359 0.0814 1085  31.005 <0.0001
 cycle2 N1 - cycle3 N1 -0.20611 0.0786 1607  -2.624  0.2672
 cycle2 N1 - cycle4 N1 -0.32926 0.0805 1644  -4.090  0.0026
 cycle2 N1 - cycle1 N2  0.58178 0.0793 1022   7.339 <0.0001
 cycle2 N1 - cycle2 N2  0.70485 0.0717  994   9.829 <0.0001
 cycle2 N1 - cycle3 N2  0.58897 0.0796 1028   7.400 <0.0001
 cycle2 N1 - cycle4 N2  0.52511 0.0799 1034   6.571 <0.0001
 cycle2 N1 - cycle1 N3  1.74085 0.0795 1030  21.894 <0.0001
 cycle2 N1 - cycle2 N3  1.53554 0.0726 1027  21.151 <0.0001
 cycle2 N1 - cycle3 N3  1.43951 0.0805 1060  17.872 <0.0001
 cycle2 N1 - cycle4 N3  1.19427 0.0853 1229  13.994 <0.0001
 cycle3 N1 - cycle4 N1 -0.12315 0.0815 1663  -1.510  0.9379
 cycle3 N1 - cycle1 N2  0.78789 0.0809 1072   9.738 <0.0001
 cycle3 N1 - cycle2 N2  0.91096 0.0809 1072  11.259 <0.0001
 cycle3 N1 - cycle3 N2  0.79508 0.0736 1062  10.801 <0.0001
 cycle3 N1 - cycle4 N2  0.73122 0.0814 1079   8.984 <0.0001
 cycle3 N1 - cycle1 N3  1.94696 0.0812 1081  23.991 <0.0001
 cycle3 N1 - cycle2 N3  1.74164 0.0816 1099  21.344 <0.0001
 cycle3 N1 - cycle3 N3  1.64562 0.0744 1091  22.105 <0.0001
 cycle3 N1 - cycle4 N3  1.40037 0.0867 1262  16.148 <0.0001
 cycle4 N1 - cycle1 N2  0.91104 0.0829 1131  10.995 <0.0001
 cycle4 N1 - cycle2 N2  1.03411 0.0829 1131  12.480 <0.0001
 cycle4 N1 - cycle3 N2  0.91822 0.0831 1135  11.054 <0.0001
 cycle4 N1 - cycle4 N2  0.85436 0.0758 1143  11.265 <0.0001
 cycle4 N1 - cycle1 N3  2.07011 0.0831 1140  24.910 <0.0001
 cycle4 N1 - cycle2 N3  1.86479 0.0835 1158  22.322 <0.0001
 cycle4 N1 - cycle3 N3  1.76877 0.0841 1172  21.028 <0.0001
 cycle4 N1 - cycle4 N3  1.52352 0.0809 1317  18.837 <0.0001
 cycle1 N2 - cycle2 N2  0.12307 0.0645 1076   1.908  0.7543
 cycle1 N2 - cycle3 N2  0.00718 0.0649 1076   0.111  1.0000
 cycle1 N2 - cycle4 N2 -0.05668 0.0652 1075  -0.869  0.9994
 cycle1 N2 - cycle1 N3  1.15907 0.0654  791  17.713 <0.0001
 cycle1 N2 - cycle2 N3  0.95375 0.0741  855  12.867 <0.0001
 cycle1 N2 - cycle3 N3  0.85773 0.0748  872  11.467 <0.0001
 cycle1 N2 - cycle4 N3  0.61248 0.0798 1040   7.676 <0.0001
 cycle2 N2 - cycle3 N2 -0.11588 0.0649 1076  -1.786  0.8255
 cycle2 N2 - cycle4 N2 -0.17974 0.0652 1075  -2.755  0.2013
 cycle2 N2 - cycle1 N3  1.03600 0.0736  838  14.069 <0.0001
 cycle2 N2 - cycle2 N3  0.83068 0.0660  810  12.589 <0.0001
 cycle2 N2 - cycle3 N3  0.73466 0.0748  872   9.821 <0.0001
 cycle2 N2 - cycle4 N3  0.48942 0.0798 1040   6.134 <0.0001
 cycle3 N2 - cycle4 N2 -0.06386 0.0655 1075  -0.975  0.9982
 cycle3 N2 - cycle1 N3  1.15188 0.0740  844  15.573 <0.0001
 cycle3 N2 - cycle2 N3  0.94657 0.0745  861  12.714 <0.0001
 cycle3 N2 - cycle3 N3  0.85055 0.0668  841  12.725 <0.0001
 cycle3 N2 - cycle4 N3  0.60530 0.0800 1044   7.566 <0.0001
 cycle4 N2 - cycle1 N3  1.21574 0.0743  850  16.365 <0.0001
 cycle4 N2 - cycle2 N3  1.01043 0.0748  867  13.513 <0.0001
 cycle4 N2 - cycle3 N3  0.91441 0.0754  882  12.135 <0.0001
 cycle4 N2 - cycle4 N3  0.66916 0.0725 1049   9.232 <0.0001
 cycle1 N3 - cycle2 N3 -0.20532 0.0656 1122  -3.129  0.0771
 cycle1 N3 - cycle3 N3 -0.30134 0.0664 1134  -4.538  0.0004
 cycle1 N3 - cycle4 N3 -0.54659 0.0720 1335  -7.592 <0.0001
 cycle2 N3 - cycle3 N3 -0.09602 0.0670 1159  -1.434  0.9567
 cycle2 N3 - cycle4 N3 -0.34127 0.0726 1358  -4.702  0.0002
 cycle3 N3 - cycle4 N3 -0.24525 0.0732 1368  -3.351  0.0393

class = C:
 contrast              estimate     SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1  0.81514 0.0725 1401  11.250 <0.0001
 cycle1 N1 - cycle3 N1  0.62316 0.0746 1468   8.351 <0.0001
 cycle1 N1 - cycle4 N1  0.39743 0.0767 1517   5.180 <0.0001
 cycle1 N1 - cycle1 N2  1.47754 0.0671  834  22.024 <0.0001
 cycle1 N1 - cycle2 N2  1.58380 0.0751  878  21.087 <0.0001
 cycle1 N1 - cycle3 N2  1.40901 0.0754  884  18.678 <0.0001
 cycle1 N1 - cycle4 N2  1.30113 0.0758  890  17.175 <0.0001
 cycle1 N1 - cycle1 N3  2.82084 0.0673  840  41.939 <0.0001
 cycle1 N1 - cycle2 N3  2.64838 0.0758  904  34.921 <0.0001
 cycle1 N1 - cycle3 N3  2.47641 0.0765  921  32.366 <0.0001
 cycle1 N1 - cycle4 N3  2.19118 0.0814 1085  26.921 <0.0001
 cycle2 N1 - cycle3 N1 -0.19199 0.0786 1607  -2.444  0.3770
 cycle2 N1 - cycle4 N1 -0.41771 0.0805 1644  -5.189 <0.0001
 cycle2 N1 - cycle1 N2  0.66239 0.0793 1022   8.356 <0.0001
 cycle2 N1 - cycle2 N2  0.76866 0.0717  994  10.718 <0.0001
 cycle2 N1 - cycle3 N2  0.59387 0.0796 1028   7.461 <0.0001
 cycle2 N1 - cycle4 N2  0.48599 0.0799 1034   6.082 <0.0001
 cycle2 N1 - cycle1 N3  2.00570 0.0795 1030  25.225 <0.0001
 cycle2 N1 - cycle2 N3  1.83323 0.0726 1027  25.252 <0.0001
 cycle2 N1 - cycle3 N3  1.66127 0.0805 1060  20.625 <0.0001
 cycle2 N1 - cycle4 N3  1.37604 0.0853 1229  16.124 <0.0001
 cycle3 N1 - cycle4 N1 -0.22572 0.0815 1663  -2.768  0.1946
 cycle3 N1 - cycle1 N2  0.85438 0.0809 1072  10.560 <0.0001
 cycle3 N1 - cycle2 N2  0.96064 0.0809 1072  11.873 <0.0001
 cycle3 N1 - cycle3 N2  0.78586 0.0736 1062  10.676 <0.0001
 cycle3 N1 - cycle4 N2  0.67797 0.0814 1079   8.330 <0.0001
 cycle3 N1 - cycle1 N3  2.19769 0.0812 1081  27.080 <0.0001
 cycle3 N1 - cycle2 N3  2.02522 0.0816 1099  24.819 <0.0001
 cycle3 N1 - cycle3 N3  1.85326 0.0744 1091  24.894 <0.0001
 cycle3 N1 - cycle4 N3  1.56803 0.0867 1262  18.081 <0.0001
 cycle4 N1 - cycle1 N2  1.08010 0.0829 1131  13.036 <0.0001
 cycle4 N1 - cycle2 N2  1.18636 0.0829 1131  14.318 <0.0001
 cycle4 N1 - cycle3 N2  1.01158 0.0831 1135  12.178 <0.0001
 cycle4 N1 - cycle4 N2  0.90369 0.0758 1143  11.915 <0.0001
 cycle4 N1 - cycle1 N3  2.42341 0.0831 1140  29.161 <0.0001
 cycle4 N1 - cycle2 N3  2.25094 0.0835 1158  26.944 <0.0001
 cycle4 N1 - cycle3 N3  2.07898 0.0841 1172  24.716 <0.0001
 cycle4 N1 - cycle4 N3  1.79375 0.0809 1317  22.178 <0.0001
 cycle1 N2 - cycle2 N2  0.10626 0.0645 1076   1.647  0.8909
 cycle1 N2 - cycle3 N2 -0.06852 0.0649 1076  -1.056  0.9963
 cycle1 N2 - cycle4 N2 -0.17641 0.0652 1075  -2.704  0.2257
 cycle1 N2 - cycle1 N3  1.34331 0.0654  791  20.529 <0.0001
 cycle1 N2 - cycle2 N3  1.17084 0.0741  855  15.796 <0.0001
 cycle1 N2 - cycle3 N3  0.99888 0.0748  872  13.354 <0.0001
 cycle1 N2 - cycle4 N3  0.71365 0.0798 1040   8.944 <0.0001
 cycle2 N2 - cycle3 N2 -0.17479 0.0649 1076  -2.694  0.2305
 cycle2 N2 - cycle4 N2 -0.28267 0.0652 1075  -4.332  0.0010
 cycle2 N2 - cycle1 N3  1.23704 0.0736  838  16.799 <0.0001
 cycle2 N2 - cycle2 N3  1.06458 0.0660  810  16.134 <0.0001
 cycle2 N2 - cycle3 N3  0.89261 0.0748  872  11.933 <0.0001
 cycle2 N2 - cycle4 N3  0.60739 0.0798 1040   7.612 <0.0001
 cycle3 N2 - cycle4 N2 -0.10788 0.0655 1075  -1.648  0.8908
 cycle3 N2 - cycle1 N3  1.41183 0.0740  844  19.087 <0.0001
 cycle3 N2 - cycle2 N3  1.23936 0.0745  861  16.646 <0.0001
 cycle3 N2 - cycle3 N3  1.06740 0.0668  841  15.969 <0.0001
 cycle3 N2 - cycle4 N3  0.78217 0.0800 1044   9.777 <0.0001
 cycle4 N2 - cycle1 N3  1.51971 0.0743  850  20.456 <0.0001
 cycle4 N2 - cycle2 N3  1.34725 0.0748  867  18.017 <0.0001
 cycle4 N2 - cycle3 N3  1.17528 0.0754  882  15.597 <0.0001
 cycle4 N2 - cycle4 N3  0.89006 0.0725 1049  12.280 <0.0001
 cycle1 N3 - cycle2 N3 -0.17246 0.0656 1122  -2.628  0.2652
 cycle1 N3 - cycle3 N3 -0.34443 0.0664 1134  -5.187 <0.0001
 cycle1 N3 - cycle4 N3 -0.62966 0.0720 1335  -8.745 <0.0001
 cycle2 N3 - cycle3 N3 -0.17196 0.0670 1159  -2.568  0.2990
 cycle2 N3 - cycle4 N3 -0.45719 0.0726 1358  -6.300 <0.0001
 cycle3 N3 - cycle4 N3 -0.28523 0.0732 1368  -3.898  0.0057

class = D:
 contrast              estimate     SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1  0.53008 0.0725 1401   7.316 <0.0001
 cycle1 N1 - cycle3 N1  0.15941 0.0746 1468   2.136  0.5965
 cycle1 N1 - cycle4 N1  0.02108 0.0767 1517   0.275  1.0000
 cycle1 N1 - cycle1 N2  0.85905 0.0671  834  12.805 <0.0001
 cycle1 N1 - cycle2 N2  1.02097 0.0751  878  13.593 <0.0001
 cycle1 N1 - cycle3 N2  0.86303 0.0754  884  11.440 <0.0001
 cycle1 N1 - cycle4 N2  0.69367 0.0758  890   9.156 <0.0001
 cycle1 N1 - cycle1 N3  2.19551 0.0673  840  32.642 <0.0001
 cycle1 N1 - cycle2 N3  2.03512 0.0758  904  26.834 <0.0001
 cycle1 N1 - cycle3 N3  1.92627 0.0765  921  25.176 <0.0001
 cycle1 N1 - cycle4 N3  1.56479 0.0814 1085  19.225 <0.0001
 cycle2 N1 - cycle3 N1 -0.37067 0.0786 1607  -4.718  0.0002
 cycle2 N1 - cycle4 N1 -0.50900 0.0805 1644  -6.323 <0.0001
 cycle2 N1 - cycle1 N2  0.32896 0.0793 1022   4.150  0.0021
 cycle2 N1 - cycle2 N2  0.49089 0.0717  994   6.845 <0.0001
 cycle2 N1 - cycle3 N2  0.33295 0.0796 1028   4.183  0.0018
 cycle2 N1 - cycle4 N2  0.16358 0.0799 1034   2.047  0.6607
 cycle2 N1 - cycle1 N3  1.66543 0.0795 1030  20.946 <0.0001
 cycle2 N1 - cycle2 N3  1.50504 0.0726 1027  20.731 <0.0001
 cycle2 N1 - cycle3 N3  1.39619 0.0805 1060  17.334 <0.0001
 cycle2 N1 - cycle4 N3  1.03471 0.0853 1229  12.124 <0.0001
 cycle3 N1 - cycle4 N1 -0.13833 0.0815 1663  -1.697  0.8700
 cycle3 N1 - cycle1 N2  0.69964 0.0809 1072   8.647 <0.0001
 cycle3 N1 - cycle2 N2  0.86156 0.0809 1072  10.649 <0.0001
 cycle3 N1 - cycle3 N2  0.70362 0.0736 1062   9.559 <0.0001
 cycle3 N1 - cycle4 N2  0.53426 0.0814 1079   6.564 <0.0001
 cycle3 N1 - cycle1 N3  2.03611 0.0812 1081  25.089 <0.0001
 cycle3 N1 - cycle2 N3  1.87571 0.0816 1099  22.987 <0.0001
 cycle3 N1 - cycle3 N3  1.76686 0.0744 1091  23.733 <0.0001
 cycle3 N1 - cycle4 N3  1.40538 0.0867 1262  16.205 <0.0001
 cycle4 N1 - cycle1 N2  0.83797 0.0829 1131  10.113 <0.0001
 cycle4 N1 - cycle2 N2  0.99989 0.0829 1131  12.068 <0.0001
 cycle4 N1 - cycle3 N2  0.84195 0.0831 1135  10.136 <0.0001
 cycle4 N1 - cycle4 N2  0.67259 0.0758 1143   8.868 <0.0001
 cycle4 N1 - cycle1 N3  2.17444 0.0831 1140  26.165 <0.0001
 cycle4 N1 - cycle2 N3  2.01404 0.0835 1158  24.108 <0.0001
 cycle4 N1 - cycle3 N3  1.90519 0.0841 1172  22.650 <0.0001
 cycle4 N1 - cycle4 N3  1.54371 0.0809 1317  19.086 <0.0001
 cycle1 N2 - cycle2 N2  0.16193 0.0645 1076   2.510  0.3344
 cycle1 N2 - cycle3 N2  0.00399 0.0649 1076   0.061  1.0000
 cycle1 N2 - cycle4 N2 -0.16538 0.0652 1075  -2.535  0.3193
 cycle1 N2 - cycle1 N3  1.33647 0.0654  791  20.424 <0.0001
 cycle1 N2 - cycle2 N3  1.17607 0.0741  855  15.866 <0.0001
 cycle1 N2 - cycle3 N3  1.06723 0.0748  872  14.267 <0.0001
 cycle1 N2 - cycle4 N3  0.70574 0.0798 1040   8.845 <0.0001
 cycle2 N2 - cycle3 N2 -0.15794 0.0649 1076  -2.434  0.3835
 cycle2 N2 - cycle4 N2 -0.32731 0.0652 1075  -5.016 <0.0001
 cycle2 N2 - cycle1 N3  1.17454 0.0736  838  15.951 <0.0001
 cycle2 N2 - cycle2 N3  1.01415 0.0660  810  15.370 <0.0001
 cycle2 N2 - cycle3 N3  0.90530 0.0748  872  12.103 <0.0001
 cycle2 N2 - cycle4 N3  0.54382 0.0798 1040   6.816 <0.0001
 cycle3 N2 - cycle4 N2 -0.16936 0.0655 1075  -2.587  0.2884
 cycle3 N2 - cycle1 N3  1.33248 0.0740  844  18.015 <0.0001
 cycle3 N2 - cycle2 N3  1.17209 0.0745  861  15.743 <0.0001
 cycle3 N2 - cycle3 N3  1.06324 0.0668  841  15.907 <0.0001
 cycle3 N2 - cycle4 N3  0.70176 0.0800 1044   8.771 <0.0001
 cycle4 N2 - cycle1 N3  1.50185 0.0743  850  20.216 <0.0001
 cycle4 N2 - cycle2 N3  1.34145 0.0748  867  17.939 <0.0001
 cycle4 N2 - cycle3 N3  1.23261 0.0754  882  16.358 <0.0001
 cycle4 N2 - cycle4 N3  0.87112 0.0725 1049  12.019 <0.0001
 cycle1 N3 - cycle2 N3 -0.16040 0.0656 1122  -2.444  0.3771
 cycle1 N3 - cycle3 N3 -0.26924 0.0664 1134  -4.055  0.0031
 cycle1 N3 - cycle4 N3 -0.63073 0.0720 1335  -8.760 <0.0001
 cycle2 N3 - cycle3 N3 -0.10885 0.0670 1159  -1.626  0.8996
 cycle2 N3 - cycle4 N3 -0.47033 0.0726 1358  -6.481 <0.0001
 cycle3 N3 - cycle4 N3 -0.36148 0.0732 1368  -4.940 <0.0001

class = E:
 contrast              estimate     SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1  0.93511 0.0725 1401  12.905 <0.0001
 cycle1 N1 - cycle3 N1  0.67300 0.0746 1468   9.019 <0.0001
 cycle1 N1 - cycle4 N1  0.52117 0.0767 1517   6.793 <0.0001
 cycle1 N1 - cycle1 N2  0.93320 0.0671  834  13.910 <0.0001
 cycle1 N1 - cycle2 N2  1.01233 0.0751  878  13.478 <0.0001
 cycle1 N1 - cycle3 N2  0.83621 0.0754  884  11.085 <0.0001
 cycle1 N1 - cycle4 N2  0.73241 0.0758  890   9.668 <0.0001
 cycle1 N1 - cycle1 N3  2.21152 0.0673  840  32.879 <0.0001
 cycle1 N1 - cycle2 N3  2.01612 0.0758  904  26.584 <0.0001
 cycle1 N1 - cycle3 N3  1.83413 0.0765  921  23.972 <0.0001
 cycle1 N1 - cycle4 N3  1.57185 0.0814 1085  19.312 <0.0001
 cycle2 N1 - cycle3 N1 -0.26212 0.0786 1607  -3.336  0.0410
 cycle2 N1 - cycle4 N1 -0.41395 0.0805 1644  -5.142 <0.0001
 cycle2 N1 - cycle1 N2 -0.00191 0.0793 1022  -0.024  1.0000
 cycle2 N1 - cycle2 N2  0.07722 0.0717  994   1.077  0.9956
 cycle2 N1 - cycle3 N2 -0.09890 0.0796 1028  -1.243  0.9854
 cycle2 N1 - cycle4 N2 -0.20271 0.0799 1034  -2.537  0.3182
 cycle2 N1 - cycle1 N3  1.27640 0.0795 1030  16.053 <0.0001
 cycle2 N1 - cycle2 N3  1.08100 0.0726 1027  14.890 <0.0001
 cycle2 N1 - cycle3 N3  0.89902 0.0805 1060  11.162 <0.0001
 cycle2 N1 - cycle4 N3  0.63673 0.0853 1229   7.461 <0.0001
 cycle3 N1 - cycle4 N1 -0.15183 0.0815 1663  -1.862  0.7826
 cycle3 N1 - cycle1 N2  0.26021 0.0809 1072   3.216  0.0598
 cycle3 N1 - cycle2 N2  0.33933 0.0809 1072   4.194  0.0018
 cycle3 N1 - cycle3 N2  0.16322 0.0736 1062   2.217  0.5373
 cycle3 N1 - cycle4 N2  0.05941 0.0814 1079   0.730  0.9999
 cycle3 N1 - cycle1 N3  1.53852 0.0812 1081  18.958 <0.0001
 cycle3 N1 - cycle2 N3  1.34312 0.0816 1099  16.460 <0.0001
 cycle3 N1 - cycle3 N3  1.16114 0.0744 1091  15.597 <0.0001
 cycle3 N1 - cycle4 N3  0.89885 0.0867 1262  10.365 <0.0001
 cycle4 N1 - cycle1 N2  0.41204 0.0829 1131   4.973 <0.0001
 cycle4 N1 - cycle2 N2  0.49116 0.0829 1131   5.928 <0.0001
 cycle4 N1 - cycle3 N2  0.31504 0.0831 1135   3.793  0.0086
 cycle4 N1 - cycle4 N2  0.21124 0.0758 1143   2.785  0.1876
 cycle4 N1 - cycle1 N3  1.69035 0.0831 1140  20.340 <0.0001
 cycle4 N1 - cycle2 N3  1.49495 0.0835 1158  17.895 <0.0001
 cycle4 N1 - cycle3 N3  1.31296 0.0841 1172  15.609 <0.0001
 cycle4 N1 - cycle4 N3  1.05068 0.0809 1317  12.991 <0.0001
 cycle1 N2 - cycle2 N2  0.07913 0.0645 1076   1.227  0.9868
 cycle1 N2 - cycle3 N2 -0.09699 0.0649 1076  -1.495  0.9420
 cycle1 N2 - cycle4 N2 -0.20079 0.0652 1075  -3.077  0.0890
 cycle1 N2 - cycle1 N3  1.27831 0.0654  791  19.536 <0.0001
 cycle1 N2 - cycle2 N3  1.08291 0.0741  855  14.610 <0.0001
 cycle1 N2 - cycle3 N3  0.90093 0.0748  872  12.044 <0.0001
 cycle1 N2 - cycle4 N3  0.63864 0.0798 1040   8.004 <0.0001
 cycle2 N2 - cycle3 N2 -0.17612 0.0649 1076  -2.715  0.2203
 cycle2 N2 - cycle4 N2 -0.27992 0.0652 1075  -4.290  0.0012
 cycle2 N2 - cycle1 N3  1.19919 0.0736  838  16.285 <0.0001
 cycle2 N2 - cycle2 N3  1.00379 0.0660  810  15.213 <0.0001
 cycle2 N2 - cycle3 N3  0.82180 0.0748  872  10.986 <0.0001
 cycle2 N2 - cycle4 N3  0.55952 0.0798 1040   7.012 <0.0001
 cycle3 N2 - cycle4 N2 -0.10380 0.0655 1075  -1.585  0.9143
 cycle3 N2 - cycle1 N3  1.37531 0.0740  844  18.594 <0.0001
 cycle3 N2 - cycle2 N3  1.17990 0.0745  861  15.848 <0.0001
 cycle3 N2 - cycle3 N3  0.99792 0.0668  841  14.930 <0.0001
 cycle3 N2 - cycle4 N3  0.73564 0.0800 1044   9.195 <0.0001
 cycle4 N2 - cycle1 N3  1.47911 0.0743  850  19.910 <0.0001
 cycle4 N2 - cycle2 N3  1.28371 0.0748  867  17.167 <0.0001
 cycle4 N2 - cycle3 N3  1.10172 0.0754  882  14.621 <0.0001
 cycle4 N2 - cycle4 N3  0.83944 0.0725 1049  11.582 <0.0001
 cycle1 N3 - cycle2 N3 -0.19540 0.0656 1122  -2.977  0.1165
 cycle1 N3 - cycle3 N3 -0.37738 0.0664 1134  -5.684 <0.0001
 cycle1 N3 - cycle4 N3 -0.63967 0.0720 1335  -8.884 <0.0001
 cycle2 N3 - cycle3 N3 -0.18198 0.0670 1159  -2.718  0.2185
 cycle2 N3 - cycle4 N3 -0.44427 0.0726 1358  -6.122 <0.0001
 cycle3 N3 - cycle4 N3 -0.26228 0.0732 1368  -3.584  0.0181

Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 12 estimates 

est_means_occurrence <- as.data.frame(emm_occurrence$emmeans)

Plot

est_means_occurrence_plot <- est_means_occurrence |> 
  select(class, cycle, stage, emmean, lower.CL, upper.CL) |> 
  slice(1:60) |> 
  ggplot(aes(group = stage, color = stage, y = emmean, x = cycle)) +
  geom_line(fill = cycle, alpha = 1) +
  geom_point(stat="identity", size = 3, alpha = 1) +
  geom_errorbar(aes(ymin=lower.CL, ymax=upper.CL), width=.2, alpha = 1) +
  ggtitle("Microstate Classes") +
  facet_wrap( ~ class, ncol = 5) +
  theme_bw() +
  theme(
    plot.title = element_text(size = size_big, hjust= 0.5),
    axis.text = element_text(size = size_small, family = "sans"),
    axis.title = element_text(size = size_big, family = "sans"),
    strip.text.x = element_text(size = size_big, family = "sans"),
    strip.background = element_blank(),
    legend.title = element_blank(),
    legend.text = element_text(size = size_small),
    legend.position="right",
  ) +
  labs(color = "Sleep Stages") +
  scale_y_continuous(name = "Occurence (per second)", limits = c(0,6), expand = c(0,0)) +
  scale_x_discrete(name= "Sleep Cycles") +
  scale_color_manual(values = c("#1b9e77","#d95f02","#7570b3")) #color-blind friendly colors

est_means_occurrence_plot

ggsave("occurrence_plot.png",
        plot = est_means_occurrence_plot,
        width = 9,
        height = 7,         
        dpi = 1200,
        bg = "transparent",
        path = "R:/MicrostateAnalysis/sleepData/MS_SleepMarkers/scripts/graphs/sleep/cycles")

Coverage

Model

coverage_model <- lmer(
coverage ~ cycle * stage * class +
(1|ID) + (1|cycle:ID) + (1|stage:ID) + (1|class:ID),
data = data_long_coverage, REML = TRUE
)

sjPlot::tab_model(coverage_model)

	coverage
Predictors	Estimates	CI	p
(Intercept)	22.24	21.09 – 23.39	<0.001
cycle [2]	-2.98	-3.79 – -2.18	<0.001
cycle [3]	-2.94	-3.76 – -2.11	<0.001
cycle [4]	-2.63	-3.49 – -1.78	<0.001
stage [N2]	-3.85	-4.57 – -3.12	<0.001
stage [N3]	-4.34	-5.07 – -3.61	<0.001
class [B]	-1.95	-3.57 – -0.32	0.019
class [C]	2.17	0.54 – 3.79	0.009
class [D]	-4.66	-6.28 – -3.04	<0.001
class [E]	-6.76	-8.39 – -5.14	<0.001
cycle [2] × stage [N2]	2.90	1.83 – 3.97	<0.001
cycle [3] × stage [N2]	2.75	1.66 – 3.83	<0.001
cycle [4] × stage [N2]	1.94	0.83 – 3.04	0.001
cycle [2] × stage [N3]	3.29	2.21 – 4.36	<0.001
cycle [3] × stage [N3]	3.08	1.98 – 4.17	<0.001
cycle [4] × stage [N3]	2.91	1.75 – 4.07	<0.001
cycle [2] × class [B]	-0.59	-1.73 – 0.54	0.307
cycle [3] × class [B]	-0.48	-1.65 – 0.69	0.426
cycle [4] × class [B]	-0.80	-2.01 – 0.40	0.192
cycle [2] × class [C]	7.42	6.29 – 8.56	<0.001
cycle [3] × class [C]	5.35	4.18 – 6.52	<0.001
cycle [4] × class [C]	5.58	4.37 – 6.78	<0.001
cycle [2] × class [D]	5.96	4.83 – 7.10	<0.001
cycle [3] × class [D]	7.22	6.05 – 8.39	<0.001
cycle [4] × class [D]	6.32	5.12 – 7.53	<0.001
cycle [2] × class [E]	2.12	0.98 – 3.26	<0.001
cycle [3] × class [E]	2.60	1.43 – 3.77	<0.001
cycle [4] × class [E]	2.07	0.87 – 3.28	0.001
stage [N2] × class [B]	-0.85	-1.87 – 0.18	0.105
stage [N3] × class [B]	-1.57	-2.60 – -0.55	0.003
stage [N2] × class [C]	5.49	4.46 – 6.51	<0.001
stage [N3] × class [C]	9.37	8.34 – 10.40	<0.001
stage [N2] × class [D]	8.38	7.35 – 9.40	<0.001
stage [N3] × class [D]	8.72	7.69 – 9.75	<0.001
stage [N2] × class [E]	6.23	5.20 – 7.25	<0.001
stage [N3] × class [E]	5.19	4.16 – 6.22	<0.001
(cycle [2] × stage [N2]) × class [B]	0.41	-1.10 – 1.91	0.596
(cycle [3] × stage [N2]) × class [B]	0.41	-1.12 – 1.95	0.597
(cycle [4] × stage [N2]) × class [B]	1.12	-0.44 – 2.69	0.161
(cycle [2] × stage [N3]) × class [B]	0.91	-0.61 – 2.43	0.239
(cycle [3] × stage [N3]) × class [B]	1.02	-0.53 – 2.57	0.198
(cycle [4] × stage [N3]) × class [B]	0.90	-0.74 – 2.54	0.281
(cycle [2] × stage [N2]) × class [C]	-6.88	-8.39 – -5.38	<0.001
(cycle [3] × stage [N2]) × class [C]	-4.91	-6.45 – -3.38	<0.001
(cycle [4] × stage [N2]) × class [C]	-4.82	-6.38 – -3.25	<0.001
(cycle [2] × stage [N3]) × class [C]	-8.60	-10.12 – -7.08	<0.001
(cycle [3] × stage [N3]) × class [C]	-6.37	-7.92 – -4.82	<0.001
(cycle [4] × stage [N3]) × class [C]	-8.17	-9.81 – -6.53	<0.001
(cycle [2] × stage [N2]) × class [D]	-6.20	-7.70 – -4.69	<0.001
(cycle [3] × stage [N2]) × class [D]	-7.50	-9.04 – -5.97	<0.001
(cycle [4] × stage [N2]) × class [D]	-5.43	-6.99 – -3.86	<0.001
(cycle [2] × stage [N3]) × class [D]	-6.81	-8.33 – -5.28	<0.001
(cycle [3] × stage [N3]) × class [D]	-8.55	-10.10 – -7.00	<0.001
(cycle [4] × stage [N3]) × class [D]	-6.48	-8.12 – -4.84	<0.001
(cycle [2] × stage [N2]) × class [E]	-1.83	-3.33 – -0.32	0.018
(cycle [3] × stage [N2]) × class [E]	-1.73	-3.27 – -0.20	0.027
(cycle [4] × stage [N2]) × class [E]	-0.56	-2.12 – 1.01	0.486
(cycle [2] × stage [N3]) × class [E]	-1.95	-3.48 – -0.43	0.012
(cycle [3] × stage [N3]) × class [E]	-1.49	-3.04 – 0.06	0.060
(cycle [4] × stage [N3]) × class [E]	-0.81	-2.46 – 0.83	0.331
Random Effects
σ2	3.44
τ00 class:ID	14.60
τ00 cycle:ID	0.00
τ00 stage:ID	0.00
τ00 ID	0.00
N ID	54
N cycle	4
N stage	3
N class	5
Observations	2760
Marginal R2 / Conditional R2	0.839 / NA

Type III Test

anova_coverage <- anova(coverage_model)
knitr::kable(anova_coverage, digits = 2, caption = "F-Tests for Coverage Model") |>
kableExtra::kable_classic(full_width = FALSE)

F-Tests for Coverage Model

	Sum Sq	Mean Sq	NumDF	DenDF	F value	Pr(>F)
cycle	0.00	0.00	3	2168.75	0.00	1
stage	0.00	0.00	2	2165.43	0.00	1
class	1007.75	251.94	4	15.40	73.15	0
cycle:stage	0.00	0.00	6	2161.67	0.00	1
cycle:class	539.12	44.93	12	2168.75	13.04	0
stage:class	2740.42	342.55	8	2165.43	99.46	0
cycle:stage:class	1424.88	59.37	24	2161.67	17.24	0

Post-hoc Tests

emm_coverage <- emmeans(coverage_model, pairwise ~ cycle * stage | class)
emm_coverage

$emmeans
class = A:
 cycle stage emmean    SE  df lower.CL upper.CL
 1     N1      22.2 0.586 404     21.1     23.4
 2     N1      19.3 0.605 459     18.1     20.4
 3     N1      19.3 0.613 481     18.1     20.5
 4     N1      19.6 0.622 508     18.4     20.8
 1     N2      18.4 0.578 385     17.3     19.5
 2     N2      18.3 0.578 385     17.2     19.4
 3     N2      18.2 0.579 388     17.1     19.3
 4     N2      17.7 0.580 391     16.6     18.8
 1     N3      17.9 0.579 388     16.8     19.0
 2     N3      18.2 0.582 394     17.1     19.3
 3     N3      18.0 0.584 401     16.9     19.2
 4     N3      18.2 0.608 466     17.0     19.4

class = B:
 cycle stage emmean    SE  df lower.CL upper.CL
 1     N1      20.3 0.586 404     19.1     21.4
 2     N1      16.7 0.605 459     15.5     17.9
 3     N1      16.9 0.613 481     15.7     18.1
 4     N1      16.9 0.622 508     15.6     18.1
 1     N2      15.6 0.578 385     14.5     16.7
 2     N2      15.3 0.578 385     14.2     16.5
 3     N2      15.3 0.579 388     14.2     16.5
 4     N2      15.2 0.580 391     14.1     16.4
 1     N3      14.4 0.579 388     13.2     15.5
 2     N3      15.0 0.582 394     13.9     16.1
 3     N3      15.1 0.584 401     13.9     16.2
 4     N3      14.8 0.608 466     13.6     16.0

class = C:
 cycle stage emmean    SE  df lower.CL upper.CL
 1     N1      24.4 0.586 404     23.3     25.6
 2     N1      28.8 0.605 459     27.7     30.0
 3     N1      26.8 0.613 481     25.6     28.0
 4     N1      27.4 0.622 508     26.1     28.6
 1     N2      26.0 0.578 385     24.9     27.2
 2     N2      26.5 0.578 385     25.4     27.6
 3     N2      26.3 0.579 388     25.2     27.4
 4     N2      26.1 0.580 391     25.0     27.2
 1     N3      29.4 0.579 388     28.3     30.6
 2     N3      28.6 0.582 394     27.4     29.7
 3     N3      28.6 0.584 401     27.4     29.7
 4     N3      27.1 0.608 466     25.9     28.3

class = D:
 cycle stage emmean    SE  df lower.CL upper.CL
 1     N1      17.6 0.586 404     16.4     18.7
 2     N1      20.6 0.605 459     19.4     21.8
 3     N1      21.9 0.613 481     20.7     23.1
 4     N1      21.3 0.622 508     20.0     22.5
 1     N2      22.1 0.578 385     21.0     23.2
 2     N2      21.8 0.578 385     20.7     22.9
 3     N2      21.6 0.579 388     20.5     22.8
 4     N2      22.3 0.580 391     21.2     23.4
 1     N3      22.0 0.579 388     20.8     23.1
 2     N3      21.4 0.582 394     20.3     22.6
 3     N3      20.8 0.584 401     19.6     21.9
 4     N3      22.1 0.608 466     20.9     23.3

class = E:
 cycle stage emmean    SE  df lower.CL upper.CL
 1     N1      15.5 0.586 404     14.3     16.6
 2     N1      14.6 0.605 459     13.4     15.8
 3     N1      15.1 0.613 481     13.9     16.3
 4     N1      14.9 0.622 508     13.7     16.1
 1     N2      17.9 0.578 385     16.7     19.0
 2     N2      18.1 0.578 385     16.9     19.2
 3     N2      18.5 0.579 388     17.4     19.7
 4     N2      18.7 0.580 391     17.5     19.8
 1     N3      16.3 0.579 388     15.2     17.5
 2     N3      16.8 0.582 394     15.7     17.9
 3     N3      17.6 0.584 401     16.4     18.7
 4     N3      17.9 0.608 466     16.7     19.1

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$contrasts
class = A:
 contrast              estimate    SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1  2.98309 0.410 2372   7.279 <0.0001
 cycle1 N1 - cycle3 N1  2.93735 0.422 2391   6.960 <0.0001
 cycle1 N1 - cycle4 N1  2.63369 0.435 2397   6.057 <0.0001
 cycle1 N1 - cycle1 N2  3.84847 0.369 2292  10.420 <0.0001
 cycle1 N1 - cycle2 N2  3.93205 0.369 2413  10.646 <0.0001
 cycle1 N1 - cycle3 N2  4.03900 0.371 2414  10.878 <0.0001
 cycle1 N1 - cycle4 N2  4.54666 0.373 2415  12.181 <0.0001
 cycle1 N1 - cycle1 N3  4.34151 0.371 2288  11.708 <0.0001
 cycle1 N1 - cycle2 N3  4.03616 0.375 2415  10.764 <0.0001
 cycle1 N1 - cycle3 N3  4.20131 0.379 2417  11.076 <0.0001
 cycle1 N1 - cycle4 N3  4.06302 0.414 2423   9.804 <0.0001
 cycle2 N1 - cycle3 N1 -0.04574 0.449 2400  -0.102  1.0000
 cycle2 N1 - cycle4 N1 -0.34940 0.460 2402  -0.759  0.9998
 cycle2 N1 - cycle1 N2  0.86539 0.400 2420   2.164  0.5766
 cycle2 N1 - cycle2 N2  0.94896 0.400 2327   2.372  0.4254
 cycle2 N1 - cycle3 N2  1.05591 0.402 2420   2.627  0.2651
 cycle2 N1 - cycle4 N2  1.56357 0.404 2421   3.871  0.0062
 cycle2 N1 - cycle1 N3  1.35843 0.402 2420   3.381  0.0354
 cycle2 N1 - cycle2 N3  1.05307 0.406 2339   2.597  0.2818
 cycle2 N1 - cycle3 N3  1.21822 0.408 2420   2.983  0.1141
 cycle2 N1 - cycle4 N3  1.07994 0.444 2432   2.430  0.3856
 cycle3 N1 - cycle4 N1 -0.30366 0.468 2390  -0.649  1.0000
 cycle3 N1 - cycle1 N2  0.91112 0.411 2421   2.215  0.5384
 cycle3 N1 - cycle2 N2  0.99469 0.411 2421   2.419  0.3936
 cycle3 N1 - cycle3 N2  1.10165 0.412 2337   2.671  0.2416
 cycle3 N1 - cycle4 N2  1.60931 0.414 2420   3.889  0.0058
 cycle3 N1 - cycle1 N3  1.40417 0.413 2422   3.399  0.0333
 cycle3 N1 - cycle2 N3  1.09881 0.417 2424   2.637  0.2597
 cycle3 N1 - cycle3 N3  1.26396 0.419 2332   3.018  0.1038
 cycle3 N1 - cycle4 N3  1.12568 0.452 2427   2.492  0.3455
 cycle4 N1 - cycle1 N2  1.21478 0.424 2422   2.864  0.1547
 cycle4 N1 - cycle2 N2  1.29835 0.424 2422   3.061  0.0924
 cycle4 N1 - cycle3 N2  1.40530 0.425 2422   3.304  0.0452
 cycle4 N1 - cycle4 N2  1.91296 0.427 2349   4.484  0.0005
 cycle4 N1 - cycle1 N3  1.70782 0.426 2423   4.009  0.0036
 cycle4 N1 - cycle2 N3  1.40246 0.430 2425   3.264  0.0511
 cycle4 N1 - cycle3 N3  1.56761 0.433 2425   3.618  0.0159
 cycle4 N1 - cycle4 N3  1.42933 0.461 2356   3.098  0.0833
 cycle1 N2 - cycle2 N2  0.08357 0.357 2328   0.234  1.0000
 cycle1 N2 - cycle3 N2  0.19052 0.359 2330   0.531  1.0000
 cycle1 N2 - cycle4 N2  0.69818 0.361 2332   1.934  0.7379
 cycle1 N2 - cycle1 N3  0.49304 0.359 2278   1.373  0.9685
 cycle1 N2 - cycle2 N3  0.18768 0.363 2413   0.517  1.0000
 cycle1 N2 - cycle3 N3  0.35283 0.367 2414   0.961  0.9984
 cycle1 N2 - cycle4 N3  0.21455 0.404 2422   0.532  1.0000
 cycle2 N2 - cycle3 N2  0.10695 0.359 2330   0.298  1.0000
 cycle2 N2 - cycle4 N2  0.61461 0.361 2332   1.702  0.8675
 cycle2 N2 - cycle1 N3  0.40947 0.359 2411   1.141  0.9928
 cycle2 N2 - cycle2 N3  0.10411 0.363 2284   0.287  1.0000
 cycle2 N2 - cycle3 N3  0.26926 0.367 2414   0.733  0.9999
 cycle2 N2 - cycle4 N3  0.13098 0.404 2422   0.325  1.0000
 cycle3 N2 - cycle4 N2  0.50766 0.362 2330   1.401  0.9636
 cycle3 N2 - cycle1 N3  0.30252 0.361 2412   0.838  0.9996
 cycle3 N2 - cycle2 N3 -0.00284 0.365 2413  -0.008  1.0000
 cycle3 N2 - cycle3 N3  0.16231 0.369 2289   0.440  1.0000
 cycle3 N2 - cycle4 N3  0.02403 0.405 2422   0.059  1.0000
 cycle4 N2 - cycle1 N3 -0.20514 0.363 2412  -0.565  1.0000
 cycle4 N2 - cycle2 N3 -0.51050 0.367 2414  -1.392  0.9652
 cycle4 N2 - cycle3 N3 -0.34535 0.371 2414  -0.932  0.9988
 cycle4 N2 - cycle4 N3 -0.48363 0.406 2335  -1.191  0.9897
 cycle1 N3 - cycle2 N3 -0.30536 0.365 2337  -0.837  0.9996
 cycle1 N3 - cycle3 N3 -0.14021 0.369 2340  -0.380  1.0000
 cycle1 N3 - cycle4 N3 -0.27849 0.405 2370  -0.687  0.9999
 cycle2 N3 - cycle3 N3  0.16515 0.373 2345   0.443  1.0000
 cycle2 N3 - cycle4 N3  0.02687 0.409 2375   0.066  1.0000
 cycle3 N3 - cycle4 N3 -0.13828 0.413 2376  -0.335  1.0000

class = B:
 contrast              estimate    SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1  3.57535 0.410 2372   8.725 <0.0001
 cycle1 N1 - cycle3 N1  3.41249 0.422 2391   8.085 <0.0001
 cycle1 N1 - cycle4 N1  3.43643 0.435 2397   7.903 <0.0001
 cycle1 N1 - cycle1 N2  4.69556 0.369 2292  12.714 <0.0001
 cycle1 N1 - cycle2 N2  4.96372 0.369 2413  13.440 <0.0001
 cycle1 N1 - cycle3 N2  4.94755 0.371 2414  13.325 <0.0001
 cycle1 N1 - cycle4 N2  5.07636 0.373 2415  13.601 <0.0001
 cycle1 N1 - cycle1 N3  5.91584 0.371 2288  15.954 <0.0001
 cycle1 N1 - cycle2 N3  5.28875 0.375 2415  14.104 <0.0001
 cycle1 N1 - cycle3 N3  5.23224 0.379 2417  13.794 <0.0001
 cycle1 N1 - cycle4 N3  5.53691 0.414 2423  13.360 <0.0001
 cycle2 N1 - cycle3 N1 -0.16285 0.449 2400  -0.363  1.0000
 cycle2 N1 - cycle4 N1 -0.13892 0.460 2402  -0.302  1.0000
 cycle2 N1 - cycle1 N2  1.12021 0.400 2420   2.801  0.1802
 cycle2 N1 - cycle2 N2  1.38837 0.400 2327   3.471  0.0264
 cycle2 N1 - cycle3 N2  1.37221 0.402 2420   3.414  0.0318
 cycle2 N1 - cycle4 N2  1.50101 0.404 2421   3.716  0.0112
 cycle2 N1 - cycle1 N3  2.34049 0.402 2420   5.825 <0.0001
 cycle2 N1 - cycle2 N3  1.71341 0.406 2339   4.225  0.0015
 cycle2 N1 - cycle3 N3  1.65690 0.408 2420   4.057  0.0030
 cycle2 N1 - cycle4 N3  1.96157 0.444 2432   4.415  0.0006
 cycle3 N1 - cycle4 N1  0.02394 0.468 2390   0.051  1.0000
 cycle3 N1 - cycle1 N2  1.28307 0.411 2421   3.120  0.0783
 cycle3 N1 - cycle2 N2  1.55122 0.411 2421   3.772  0.0091
 cycle3 N1 - cycle3 N2  1.53506 0.412 2337   3.722  0.0110
 cycle3 N1 - cycle4 N2  1.66387 0.414 2420   4.021  0.0035
 cycle3 N1 - cycle1 N3  2.50335 0.413 2422   6.061 <0.0001
 cycle3 N1 - cycle2 N3  1.87626 0.417 2424   4.502  0.0004
 cycle3 N1 - cycle3 N3  1.81975 0.419 2332   4.346  0.0009
 cycle3 N1 - cycle4 N3  2.12442 0.452 2427   4.703  0.0002
 cycle4 N1 - cycle1 N2  1.25913 0.424 2422   2.968  0.1186
 cycle4 N1 - cycle2 N2  1.52729 0.424 2422   3.601  0.0169
 cycle4 N1 - cycle3 N2  1.51112 0.425 2422   3.553  0.0200
 cycle4 N1 - cycle4 N2  1.63993 0.427 2349   3.844  0.0069
 cycle4 N1 - cycle1 N3  2.47941 0.426 2423   5.821 <0.0001
 cycle4 N1 - cycle2 N3  1.85232 0.430 2425   4.311  0.0010
 cycle4 N1 - cycle3 N3  1.79581 0.433 2425   4.145  0.0021
 cycle4 N1 - cycle4 N3  2.10048 0.461 2356   4.553  0.0003
 cycle1 N2 - cycle2 N2  0.26816 0.357 2328   0.751  0.9998
 cycle1 N2 - cycle3 N2  0.25199 0.359 2330   0.702  0.9999
 cycle1 N2 - cycle4 N2  0.38080 0.361 2332   1.055  0.9963
 cycle1 N2 - cycle1 N3  1.22028 0.359 2278   3.399  0.0334
 cycle1 N2 - cycle2 N3  0.59319 0.363 2413   1.635  0.8962
 cycle1 N2 - cycle3 N3  0.53668 0.367 2414   1.462  0.9506
 cycle1 N2 - cycle4 N3  0.84135 0.404 2422   2.085  0.6339
 cycle2 N2 - cycle3 N2 -0.01616 0.359 2330  -0.045  1.0000
 cycle2 N2 - cycle4 N2  0.11264 0.361 2332   0.312  1.0000
 cycle2 N2 - cycle1 N3  0.95212 0.359 2411   2.652  0.2513
 cycle2 N2 - cycle2 N3  0.32504 0.363 2284   0.896  0.9992
 cycle2 N2 - cycle3 N3  0.26853 0.367 2414   0.731  0.9999
 cycle2 N2 - cycle4 N3  0.57320 0.404 2422   1.420  0.9597
 cycle3 N2 - cycle4 N2  0.12881 0.362 2330   0.355  1.0000
 cycle3 N2 - cycle1 N3  0.96829 0.361 2412   2.683  0.2355
 cycle3 N2 - cycle2 N3  0.34120 0.365 2413   0.935  0.9988
 cycle3 N2 - cycle3 N3  0.28469 0.369 2289   0.772  0.9998
 cycle3 N2 - cycle4 N3  0.58936 0.405 2422   1.456  0.9520
 cycle4 N2 - cycle1 N3  0.83948 0.363 2412   2.313  0.4673
 cycle4 N2 - cycle2 N3  0.21239 0.367 2414   0.579  1.0000
 cycle4 N2 - cycle3 N3  0.15588 0.371 2414   0.421  1.0000
 cycle4 N2 - cycle4 N3  0.46055 0.406 2335   1.134  0.9932
 cycle1 N3 - cycle2 N3 -0.62709 0.365 2337  -1.720  0.8594
 cycle1 N3 - cycle3 N3 -0.68360 0.369 2340  -1.852  0.7884
 cycle1 N3 - cycle4 N3 -0.37893 0.405 2370  -0.935  0.9988
 cycle2 N3 - cycle3 N3 -0.05651 0.373 2345  -0.152  1.0000
 cycle2 N3 - cycle4 N3  0.24816 0.409 2375   0.607  1.0000
 cycle3 N3 - cycle4 N3  0.30467 0.413 2376   0.738  0.9999

class = C:
 contrast              estimate    SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1 -4.43930 0.410 2372 -10.833 <0.0001
 cycle1 N1 - cycle3 N1 -2.41100 0.422 2391  -5.712 <0.0001
 cycle1 N1 - cycle4 N1 -2.94237 0.435 2397  -6.767 <0.0001
 cycle1 N1 - cycle1 N2 -1.63742 0.369 2292  -4.433  0.0006
 cycle1 N1 - cycle2 N2 -2.09128 0.369 2413  -5.662 <0.0001
 cycle1 N1 - cycle3 N2 -1.88362 0.371 2414  -5.073 <0.0001
 cycle1 N1 - cycle4 N2 -1.69924 0.373 2415  -4.553  0.0003
 cycle1 N1 - cycle1 N3 -5.03125 0.371 2288 -13.568 <0.0001
 cycle1 N1 - cycle2 N3 -4.16228 0.375 2415 -11.100 <0.0001
 cycle1 N1 - cycle3 N3 -4.14993 0.379 2417 -10.940 <0.0001
 cycle1 N1 - cycle4 N3 -2.71626 0.414 2423  -6.554 <0.0001
 cycle2 N1 - cycle3 N1  2.02831 0.449 2400   4.522  0.0004
 cycle2 N1 - cycle4 N1  1.49693 0.460 2402   3.252  0.0531
 cycle2 N1 - cycle1 N2  2.80189 0.400 2420   7.005 <0.0001
 cycle2 N1 - cycle2 N2  2.34802 0.400 2327   5.870 <0.0001
 cycle2 N1 - cycle3 N2  2.55568 0.402 2420   6.358 <0.0001
 cycle2 N1 - cycle4 N2  2.74007 0.404 2421   6.784 <0.0001
 cycle2 N1 - cycle1 N3 -0.59195 0.402 2420  -1.473  0.9478
 cycle2 N1 - cycle2 N3  0.27702 0.406 2339   0.683  0.9999
 cycle2 N1 - cycle3 N3  0.28937 0.408 2420   0.709  0.9999
 cycle2 N1 - cycle4 N3  1.72304 0.444 2432   3.878  0.0061
 cycle3 N1 - cycle4 N1 -0.53138 0.468 2390  -1.136  0.9931
 cycle3 N1 - cycle1 N2  0.77358 0.411 2421   1.881  0.7712
 cycle3 N1 - cycle2 N2  0.31971 0.411 2421   0.777  0.9998
 cycle3 N1 - cycle3 N2  0.52737 0.412 2337   1.279  0.9818
 cycle3 N1 - cycle4 N2  0.71176 0.414 2420   1.720  0.8592
 cycle3 N1 - cycle1 N3 -2.62026 0.413 2422  -6.344 <0.0001
 cycle3 N1 - cycle2 N3 -1.75129 0.417 2424  -4.202  0.0016
 cycle3 N1 - cycle3 N3 -1.73893 0.419 2332  -4.153  0.0020
 cycle3 N1 - cycle4 N3 -0.30526 0.452 2427  -0.676  0.9999
 cycle4 N1 - cycle1 N2  1.30496 0.424 2422   3.076  0.0885
 cycle4 N1 - cycle2 N2  0.85109 0.424 2422   2.006  0.6892
 cycle4 N1 - cycle3 N2  1.05875 0.425 2422   2.489  0.3472
 cycle4 N1 - cycle4 N2  1.24314 0.427 2349   2.914  0.1366
 cycle4 N1 - cycle1 N3 -2.08888 0.426 2423  -4.904 <0.0001
 cycle4 N1 - cycle2 N3 -1.21991 0.430 2425  -2.839  0.1643
 cycle4 N1 - cycle3 N3 -1.20756 0.433 2425  -2.787  0.1859
 cycle4 N1 - cycle4 N3  0.22611 0.461 2356   0.490  1.0000
 cycle1 N2 - cycle2 N2 -0.45387 0.357 2328  -1.271  0.9826
 cycle1 N2 - cycle3 N2 -0.24621 0.359 2330  -0.686  0.9999
 cycle1 N2 - cycle4 N2 -0.06182 0.361 2332  -0.171  1.0000
 cycle1 N2 - cycle1 N3 -3.39384 0.359 2278  -9.454 <0.0001
 cycle1 N2 - cycle2 N3 -2.52487 0.363 2413  -6.958 <0.0001
 cycle1 N2 - cycle3 N3 -2.51251 0.367 2414  -6.843 <0.0001
 cycle1 N2 - cycle4 N3 -1.07884 0.404 2422  -2.673  0.2403
 cycle2 N2 - cycle3 N2  0.20766 0.359 2330   0.578  1.0000
 cycle2 N2 - cycle4 N2  0.39205 0.361 2332   1.086  0.9953
 cycle2 N2 - cycle1 N3 -2.93997 0.359 2411  -8.189 <0.0001
 cycle2 N2 - cycle2 N3 -2.07100 0.363 2284  -5.708 <0.0001
 cycle2 N2 - cycle3 N3 -2.05865 0.367 2414  -5.607 <0.0001
 cycle2 N2 - cycle4 N3 -0.62498 0.404 2422  -1.549  0.9267
 cycle3 N2 - cycle4 N2  0.18438 0.362 2330   0.509  1.0000
 cycle3 N2 - cycle1 N3 -3.14763 0.361 2412  -8.720 <0.0001
 cycle3 N2 - cycle2 N3 -2.27866 0.365 2413  -6.246 <0.0001
 cycle3 N2 - cycle3 N3 -2.26631 0.369 2289  -6.149 <0.0001
 cycle3 N2 - cycle4 N3 -0.83264 0.405 2422  -2.057  0.6539
 cycle4 N2 - cycle1 N3 -3.33202 0.363 2412  -9.182 <0.0001
 cycle4 N2 - cycle2 N3 -2.46305 0.367 2414  -6.716 <0.0001
 cycle4 N2 - cycle3 N3 -2.45069 0.371 2414  -6.615 <0.0001
 cycle4 N2 - cycle4 N3 -1.01702 0.406 2335  -2.504  0.3378
 cycle1 N3 - cycle2 N3  0.86897 0.365 2337   2.383  0.4182
 cycle1 N3 - cycle3 N3  0.88132 0.369 2340   2.388  0.4144
 cycle1 N3 - cycle4 N3  2.31499 0.405 2370   5.711 <0.0001
 cycle2 N3 - cycle3 N3  0.01235 0.373 2345   0.033  1.0000
 cycle2 N3 - cycle4 N3  1.44602 0.409 2375   3.535  0.0212
 cycle3 N3 - cycle4 N3  1.43367 0.413 2376   3.474  0.0261

class = D:
 contrast              estimate    SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1 -2.98149 0.410 2372  -7.276 <0.0001
 cycle1 N1 - cycle3 N1 -4.27872 0.422 2391 -10.138 <0.0001
 cycle1 N1 - cycle4 N1 -3.69004 0.435 2397  -8.487 <0.0001
 cycle1 N1 - cycle1 N2 -4.52722 0.369 2292 -12.258 <0.0001
 cycle1 N1 - cycle2 N2 -4.21303 0.369 2413 -11.407 <0.0001
 cycle1 N1 - cycle3 N2 -4.05048 0.371 2414 -10.909 <0.0001
 cycle1 N1 - cycle4 N2 -4.72679 0.373 2415 -12.664 <0.0001
 cycle1 N1 - cycle1 N3 -4.37588 0.371 2288 -11.801 <0.0001
 cycle1 N1 - cycle2 N3 -3.84078 0.375 2415 -10.243 <0.0001
 cycle1 N1 - cycle3 N3 -3.18543 0.379 2417  -8.398 <0.0001
 cycle1 N1 - cycle4 N3 -4.49782 0.414 2423 -10.853 <0.0001
 cycle2 N1 - cycle3 N1 -1.29722 0.449 2400  -2.892  0.1441
 cycle2 N1 - cycle4 N1 -0.70855 0.460 2402  -1.539  0.9296
 cycle2 N1 - cycle1 N2 -1.54573 0.400 2420  -3.864  0.0064
 cycle2 N1 - cycle2 N2 -1.23154 0.400 2327  -3.079  0.0879
 cycle2 N1 - cycle3 N2 -1.06899 0.402 2420  -2.659  0.2475
 cycle2 N1 - cycle4 N2 -1.74530 0.404 2421  -4.321  0.0010
 cycle2 N1 - cycle1 N3 -1.39439 0.402 2420  -3.470  0.0264
 cycle2 N1 - cycle2 N3 -0.85929 0.406 2339  -2.119  0.6091
 cycle2 N1 - cycle3 N3 -0.20394 0.408 2420  -0.499  1.0000
 cycle2 N1 - cycle4 N3 -1.51633 0.444 2432  -3.413  0.0320
 cycle3 N1 - cycle4 N1  0.58868 0.468 2390   1.258  0.9839
 cycle3 N1 - cycle1 N2 -0.24850 0.411 2421  -0.604  1.0000
 cycle3 N1 - cycle2 N2  0.06569 0.411 2421   0.160  1.0000
 cycle3 N1 - cycle3 N2  0.22824 0.412 2337   0.553  1.0000
 cycle3 N1 - cycle4 N2 -0.44807 0.414 2420  -1.083  0.9954
 cycle3 N1 - cycle1 N3 -0.09717 0.413 2422  -0.235  1.0000
 cycle3 N1 - cycle2 N3  0.43794 0.417 2424   1.051  0.9965
 cycle3 N1 - cycle3 N3  1.09329 0.419 2332   2.611  0.2740
 cycle3 N1 - cycle4 N3 -0.21911 0.452 2427  -0.485  1.0000
 cycle4 N1 - cycle1 N2 -0.83718 0.424 2422  -1.974  0.7115
 cycle4 N1 - cycle2 N2 -0.52299 0.424 2422  -1.233  0.9863
 cycle4 N1 - cycle3 N2 -0.36044 0.425 2422  -0.847  0.9995
 cycle4 N1 - cycle4 N2 -1.03675 0.427 2349  -2.430  0.3860
 cycle4 N1 - cycle1 N3 -0.68585 0.426 2423  -1.610  0.9057
 cycle4 N1 - cycle2 N3 -0.15074 0.430 2425  -0.351  1.0000
 cycle4 N1 - cycle3 N3  0.50461 0.433 2425   1.165  0.9914
 cycle4 N1 - cycle4 N3 -0.80778 0.461 2356  -1.751  0.8441
 cycle1 N2 - cycle2 N2  0.31419 0.357 2328   0.880  0.9993
 cycle1 N2 - cycle3 N2  0.47674 0.359 2330   1.328  0.9756
 cycle1 N2 - cycle4 N2 -0.19957 0.361 2332  -0.553  1.0000
 cycle1 N2 - cycle1 N3  0.15133 0.359 2278   0.422  1.0000
 cycle1 N2 - cycle2 N3  0.68644 0.363 2413   1.892  0.7646
 cycle1 N2 - cycle3 N3  1.34179 0.367 2414   3.654  0.0140
 cycle1 N2 - cycle4 N3  0.02940 0.404 2422   0.073  1.0000
 cycle2 N2 - cycle3 N2  0.16255 0.359 2330   0.453  1.0000
 cycle2 N2 - cycle4 N2 -0.51376 0.361 2332  -1.423  0.9592
 cycle2 N2 - cycle1 N3 -0.16285 0.359 2411  -0.454  1.0000
 cycle2 N2 - cycle2 N3  0.37225 0.363 2284   1.026  0.9971
 cycle2 N2 - cycle3 N3  1.02760 0.367 2414   2.799  0.1811
 cycle2 N2 - cycle4 N3 -0.28479 0.404 2422  -0.706  0.9999
 cycle3 N2 - cycle4 N2 -0.67631 0.362 2330  -1.866  0.7804
 cycle3 N2 - cycle1 N3 -0.32540 0.361 2412  -0.902  0.9991
 cycle3 N2 - cycle2 N3  0.20970 0.365 2413   0.575  1.0000
 cycle3 N2 - cycle3 N3  0.86505 0.369 2289   2.347  0.4432
 cycle3 N2 - cycle4 N3 -0.44734 0.405 2422  -1.105  0.9945
 cycle4 N2 - cycle1 N3  0.35090 0.363 2412   0.967  0.9983
 cycle4 N2 - cycle2 N3  0.88601 0.367 2414   2.416  0.3955
 cycle4 N2 - cycle3 N3  1.54136 0.371 2414   4.160  0.0020
 cycle4 N2 - cycle4 N3  0.22897 0.406 2335   0.564  1.0000
 cycle1 N3 - cycle2 N3  0.53511 0.365 2337   1.467  0.9492
 cycle1 N3 - cycle3 N3  1.19045 0.369 2340   3.226  0.0574
 cycle1 N3 - cycle4 N3 -0.12194 0.405 2370  -0.301  1.0000
 cycle2 N3 - cycle3 N3  0.65535 0.373 2345   1.758  0.8406
 cycle2 N3 - cycle4 N3 -0.65704 0.409 2375  -1.606  0.9072
 cycle3 N3 - cycle4 N3 -1.31239 0.413 2376  -3.180  0.0658

class = E:
 contrast              estimate    SE   df t.ratio p.value
 cycle1 N1 - cycle2 N1  0.86237 0.410 2372   2.104  0.6197
 cycle1 N1 - cycle3 N1  0.33987 0.422 2391   0.805  0.9997
 cycle1 N1 - cycle4 N1  0.56230 0.435 2397   1.293  0.9801
 cycle1 N1 - cycle1 N2 -2.37940 0.369 2292  -6.442 <0.0001
 cycle1 N1 - cycle2 N2 -2.59144 0.369 2413  -7.016 <0.0001
 cycle1 N1 - cycle3 N2 -3.05244 0.371 2414  -8.221 <0.0001
 cycle1 N1 - cycle4 N2 -3.19698 0.373 2415  -8.565 <0.0001
 cycle1 N1 - cycle1 N3 -0.85020 0.371 2288  -2.293  0.4820
 cycle1 N1 - cycle2 N3 -1.32184 0.375 2415  -3.525  0.0219
 cycle1 N1 - cycle3 N3 -2.09818 0.379 2417  -5.531 <0.0001
 cycle1 N1 - cycle4 N3 -2.38585 0.414 2423  -5.757 <0.0001
 cycle2 N1 - cycle3 N1 -0.52250 0.449 2400  -1.165  0.9914
 cycle2 N1 - cycle4 N1 -0.30007 0.460 2402  -0.652  1.0000
 cycle2 N1 - cycle1 N2 -3.24177 0.400 2420  -8.105 <0.0001
 cycle2 N1 - cycle2 N2 -3.45381 0.400 2327  -8.635 <0.0001
 cycle2 N1 - cycle3 N2 -3.91481 0.402 2420  -9.739 <0.0001
 cycle2 N1 - cycle4 N2 -4.05934 0.404 2421 -10.050 <0.0001
 cycle2 N1 - cycle1 N3 -1.71257 0.402 2420  -4.262  0.0013
 cycle2 N1 - cycle2 N3 -2.18421 0.406 2339  -5.386 <0.0001
 cycle2 N1 - cycle3 N3 -2.96054 0.408 2420  -7.249 <0.0001
 cycle2 N1 - cycle4 N3 -3.24822 0.444 2432  -7.310 <0.0001
 cycle3 N1 - cycle4 N1  0.22243 0.468 2390   0.475  1.0000
 cycle3 N1 - cycle1 N2 -2.71927 0.411 2421  -6.612 <0.0001
 cycle3 N1 - cycle2 N2 -2.93131 0.411 2421  -7.128 <0.0001
 cycle3 N1 - cycle3 N2 -3.39231 0.412 2337  -8.224 <0.0001
 cycle3 N1 - cycle4 N2 -3.53684 0.414 2420  -8.547 <0.0001
 cycle3 N1 - cycle1 N3 -1.19007 0.413 2422  -2.881  0.1482
 cycle3 N1 - cycle2 N3 -1.66171 0.417 2424  -3.987  0.0040
 cycle3 N1 - cycle3 N3 -2.43804 0.419 2332  -5.822 <0.0001
 cycle3 N1 - cycle4 N3 -2.72572 0.452 2427  -6.034 <0.0001
 cycle4 N1 - cycle1 N2 -2.94170 0.424 2422  -6.935 <0.0001
 cycle4 N1 - cycle2 N2 -3.15374 0.424 2422  -7.435 <0.0001
 cycle4 N1 - cycle3 N2 -3.61474 0.425 2422  -8.498 <0.0001
 cycle4 N1 - cycle4 N2 -3.75928 0.427 2349  -8.811 <0.0001
 cycle4 N1 - cycle1 N3 -1.41250 0.426 2423  -3.316  0.0435
 cycle4 N1 - cycle2 N3 -1.88414 0.430 2425  -4.385  0.0007
 cycle4 N1 - cycle3 N3 -2.66048 0.433 2425  -6.141 <0.0001
 cycle4 N1 - cycle4 N3 -2.94815 0.461 2356  -6.390 <0.0001
 cycle1 N2 - cycle2 N2 -0.21204 0.357 2328  -0.594  1.0000
 cycle1 N2 - cycle3 N2 -0.67305 0.359 2330  -1.874  0.7753
 cycle1 N2 - cycle4 N2 -0.81758 0.361 2332  -2.264  0.5026
 cycle1 N2 - cycle1 N3  1.52919 0.359 2278   4.260  0.0013
 cycle1 N2 - cycle2 N3  1.05756 0.363 2413   2.915  0.1363
 cycle1 N2 - cycle3 N3  0.28122 0.367 2414   0.766  0.9998
 cycle1 N2 - cycle4 N3 -0.00645 0.404 2422  -0.016  1.0000
 cycle2 N2 - cycle3 N2 -0.46100 0.359 2330  -1.284  0.9812
 cycle2 N2 - cycle4 N2 -0.60553 0.361 2332  -1.677  0.8787
 cycle2 N2 - cycle1 N3  1.74124 0.359 2411   4.850 <0.0001
 cycle2 N2 - cycle2 N3  1.26960 0.363 2284   3.499  0.0240
 cycle2 N2 - cycle3 N3  0.49327 0.367 2414   1.343  0.9733
 cycle2 N2 - cycle4 N3  0.20559 0.404 2422   0.509  1.0000
 cycle3 N2 - cycle4 N2 -0.14453 0.362 2330  -0.399  1.0000
 cycle3 N2 - cycle1 N3  2.20224 0.361 2412   6.101 <0.0001
 cycle3 N2 - cycle2 N3  1.73060 0.365 2413   4.744  0.0001
 cycle3 N2 - cycle3 N3  0.95427 0.369 2289   2.589  0.2863
 cycle3 N2 - cycle4 N3  0.66659 0.405 2422   1.647  0.8915
 cycle4 N2 - cycle1 N3  2.34677 0.363 2412   6.467 <0.0001
 cycle4 N2 - cycle2 N3  1.87513 0.367 2414   5.113 <0.0001
 cycle4 N2 - cycle3 N3  1.09880 0.371 2414   2.966  0.1194
 cycle4 N2 - cycle4 N3  0.81112 0.406 2335   1.997  0.6957
 cycle1 N3 - cycle2 N3 -0.47164 0.365 2337  -1.293  0.9801
 cycle1 N3 - cycle3 N3 -1.24797 0.369 2340  -3.382  0.0353
 cycle1 N3 - cycle4 N3 -1.53565 0.405 2370  -3.788  0.0086
 cycle2 N3 - cycle3 N3 -0.77633 0.373 2345  -2.082  0.6357
 cycle2 N3 - cycle4 N3 -1.06401 0.409 2375  -2.601  0.2796
 cycle3 N3 - cycle4 N3 -0.28768 0.413 2376  -0.697  0.9999

Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 12 estimates 

est_means_coverage <- as.data.frame(emm_coverage$emmeans)

Plot

est_means_coverage_plot <- est_means_coverage |> 
  select(class, cycle, stage, emmean, lower.CL, upper.CL) |> 
  slice(1:60) |> 
  ggplot(aes(group = stage, color = stage, y = emmean, x = cycle)) +
  geom_line(fill = cycle, alpha = 1) +
  geom_point(stat="identity", size = 3, alpha = 1) +
  geom_errorbar(aes(ymin=lower.CL, ymax=upper.CL), width=.2, alpha = 1) +
  ggtitle("Microstate Classes") +
  facet_wrap(~ class, ncol = 5) +
  theme_bw() +
   theme(
    plot.title = element_text(size = size_big, hjust= 0.5),
    axis.text = element_text(size = size_small, family = "sans"),
    axis.title = element_text(size = size_big, family = "sans"),
    strip.text.x = element_text(size = size_big, family = "sans"),
    strip.background = element_blank(),
    legend.title = element_blank(),
    legend.text = element_text(size = size_small),
    legend.position="right",
  ) +
  labs(color = "Sleep Stages") +
  scale_y_continuous(name = "Coverage (%)", limits = c(0,35), expand = c(0,0)) +
  scale_x_discrete(name= "Sleep Cycles") +
  scale_color_manual(values = c("#1b9e77","#d95f02","#7570b3")) #color-blind friendly colors

est_means_coverage_plot

ggsave("coverage_plot.png",
        plot = est_means_coverage_plot,
        width = 9,
        height = 7,
        dpi = 1200,
        bg = "transparent",
        path = "R:/MicrostateAnalysis/sleepData/MS_SleepMarkers/scripts/graphs/sleep/cycles")