Representative hypnogram from Group 2, Day 4
Subsetting vigilance data
remove recordings that failed visual quality inspection (Exclude == F)
remove recordings from Day 1 (habituation: Day > 1)
cut time down to 240 min - the quality of recordings drops after 4h due to battery life (eTime <= 240)
exclude recording 7 (in-between dark/light phase recording)
Compute pairwise correlations
sleep states are treated as awake or asleep (REM + NREM)
computes all pairwise correlations (Pearson) between and within groups
Unique pairs:
## [1] 120
Normality is not violated in any group
QQ-plots
Df | F value | Pr(>F) | |
---|---|---|---|
group | 7 | 7.393341 | 0 |
364 | NA | NA |
Groupwise variances are heteroscedastic
## [1] "Settings: unique SS "
stratum | term | df | R.Sum.Sq | R.Mean.Sq | Iter | Pr.Prob. |
---|---|---|---|---|---|---|
as.factor(pair) | GroupType | 1 | 0.7234812 | 0.7234812 | 5000 | 0.0000000 |
as.factor(pair) | Stage | 1 | 0.0046275 | 0.0046275 | 51 | 0.9411765 |
as.factor(pair) | GroupType:Stage | 1 | 0.0238593 | 0.0238593 | 882 | 0.1020408 |
as.factor(pair) | Residuals | 116 | 1.7747016 | 0.0152992 | NA | NA |
Within | GroupType | 1 | 0.1109127 | 0.1109127 | 5000 | 0.0010000 |
Within | Light1 | 1 | 0.1523508 | 0.1523508 | 51 | 1.0000000 |
Within | GroupType:Light1 | 1 | 0.0254551 | 0.0254551 | 51 | 0.8039216 |
Within | Stage | 1 | 0.0002219 | 0.0002219 | 51 | 1.0000000 |
Within | GroupType:Stage | 1 | 0.0008826 | 0.0008826 | 91 | 0.5274725 |
Within | Light1:Stage | 1 | 0.1517234 | 0.1517234 | 5000 | 0.0018000 |
Within | GroupType:Light1:Stage | 1 | 0.0046400 | 0.0046400 | 51 | 0.8823529 |
Within | Residuals | 245 | 2.5119000 | 0.0102527 | NA | NA |
Summary
##
## Error: as.factor(pair)
## Component 1 :
## Df R Sum Sq R Mean Sq Iter Pr(Prob)
## GroupType 1 0.72348 0.72348 5000 <0.0000000000000002 ***
## Stage 1 0.00463 0.00463 51 0.9412
## GroupType:Stage 1 0.02386 0.02386 882 0.1020
## Residuals 116 1.77470 0.01530
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Error: Within
## Component 1 :
## Df R Sum Sq R Mean Sq Iter Pr(Prob)
## GroupType 1 0.11091 0.110913 5000 0.0010 ***
## Light1 1 0.15235 0.152351 51 1.0000
## GroupType:Light1 1 0.02546 0.025455 51 0.8039
## Stage 1 0.00022 0.000222 51 1.0000
## GroupType:Stage 1 0.00088 0.000883 91 0.5275
## Light1:Stage 1 0.15172 0.151723 5000 0.0018 **
## GroupType:Light1:Stage 1 0.00464 0.004640 51 0.8824
## Residuals 245 2.51190 0.010253
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
“PseudoCausality” over time
Subset data
Run pseudo-causality function
Summarize
## [1] 16
Normality is violated in nMice == 1
Df | F value | Pr(>F) | |
---|---|---|---|
group | 29 | 1.695526 | 0.0155931 |
350 | NA | NA |
Groupwise variances are heteroscedastic
Mann-Whitney-Wilcoxon Test for time 1
Awake mice: 1
statistic | p.value | method | alternative |
---|---|---|---|
73 | 0.0388457 | Wilcoxon rank sum test with continuity correction | two.sided |
Awake mice: 3
statistic | p.value | method | alternative |
---|---|---|---|
45 | 0.1879591 | Wilcoxon rank sum test with continuity correction | two.sided |
lmPerm
Repeated-measures:
## [1] "Settings: unique SS : numeric variables centered"
stratum | term | df | R.Sum.Sq | R.Mean.Sq | Iter | Pr.Prob. |
---|---|---|---|---|---|---|
as.factor(MouseNumber) | nMice | 1 | 0.2569083 | 0.2569083 | 5000 | 0.0014 |
as.factor(MouseNumber) | Residuals | 14 | 0.2337892 | 0.0166992 | NA | NA |
Within | GroupType1 | 1 | 0.0015733 | 0.0015733 | 51 | 1.0000 |
Within | nMice | 1 | 1.5497744 | 1.5497744 | 5000 | 0.0000 |
Within | GroupType1:nMice | 1 | 0.3331050 | 0.3331050 | 5000 | 0.0000 |
Within | as.factor(timePeriod) | 4 | 2.5890506 | 0.6472627 | 5000 | 0.0000 |
Within | Residuals | 357 | 2.2431089 | 0.0062832 | NA | NA |
##
## Error: as.factor(MouseNumber)
## Component 1 :
## Df R Sum Sq R Mean Sq Iter Pr(Prob)
## nMice 1 0.25691 0.256908 5000 0.0014 **
## Residuals 14 0.23379 0.016699
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Error: Within
## Component 1 :
## Df R Sum Sq R Mean Sq Iter Pr(Prob)
## GroupType1 1 0.00157 0.00157 51 1
## nMice 1 1.54977 1.54977 5000 <0.0000000000000002 ***
## GroupType1:nMice 1 0.33310 0.33310 5000 <0.0000000000000002 ***
## as.factor(timePeriod) 4 2.58905 0.64726 5000 <0.0000000000000002 ***
## Residuals 357 2.24311 0.00628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Awake mice: 1
## [1] "Settings: unique SS "
stratum | term | df | R.Sum.Sq | R.Mean.Sq | Iter | Pr.Prob. |
---|---|---|---|---|---|---|
as.factor(MouseNumber) | Residuals | 15 | 0.1060661 | 0.0070711 | NA | NA |
Within | GroupType1 | 1 | 0.0755079 | 0.0755079 | 5000 | 0 |
Within | as.factor(timePeriod) | 4 | 0.3809504 | 0.0952376 | 5000 | 0 |
Within | Residuals | 139 | 0.4687782 | 0.0033725 | NA | NA |
Summary
##
## Error: as.factor(MouseNumber)
## Component 1 :
## Df R Sum Sq R Mean Sq
## Residuals 15 0.10607 0.0070711
##
##
## Error: Within
## Component 1 :
## Df R Sum Sq R Mean Sq Iter Pr(Prob)
## GroupType1 1 0.07551 0.075508 5000 < 0.00000000000000022
## as.factor(timePeriod) 4 0.38095 0.095238 5000 < 0.00000000000000022
## Residuals 139 0.46878 0.003373
##
## GroupType1 ***
## as.factor(timePeriod) ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Awake mice: 2
## [1] "Settings: unique SS "
stratum | term | df | R.Sum.Sq | R.Mean.Sq | Iter | Pr.Prob. |
---|---|---|---|---|---|---|
as.factor(MouseNumber) | Residuals | 13 | 0.2629157 | 0.0202243 | NA | NA |
Within | GroupType1 | 1 | 0.0038501 | 0.0038501 | 146 | 0.4109589 |
Within | as.factor(timePeriod) | 4 | 1.3369610 | 0.3342402 | 5000 | 0.0000000 |
Within | Residuals | 121 | 0.6011011 | 0.0049678 | NA | NA |
Summary
##
## Error: as.factor(MouseNumber)
## Component 1 :
## Df R Sum Sq R Mean Sq
## Residuals 13 0.26292 0.020224
##
##
## Error: Within
## Component 1 :
## Df R Sum Sq R Mean Sq Iter Pr(Prob)
## GroupType1 1 0.00385 0.00385 146 0.411
## as.factor(timePeriod) 4 1.33696 0.33424 5000 <0.0000000000000002 ***
## Residuals 121 0.60110 0.00497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Awake mice: 3
## [1] "Settings: unique SS "
stratum | term | df | R.Sum.Sq | R.Mean.Sq | Iter | Pr.Prob. |
---|---|---|---|---|---|---|
as.factor(MouseNumber) | Residuals | 7 | 0.1136644 | 0.0162378 | NA | NA |
Within | GroupType1 | 1 | 0.3104018 | 0.3104018 | 5000 | 0 |
Within | as.factor(timePeriod) | 4 | 1.2393094 | 0.3098273 | 5000 | 0 |
Within | Residuals | 67 | 0.3690574 | 0.0055083 | NA | NA |
Summary
##
## Error: as.factor(MouseNumber)
## Component 1 :
## Df R Sum Sq R Mean Sq
## Residuals 7 0.11366 0.016238
##
##
## Error: Within
## Component 1 :
## Df R Sum Sq R Mean Sq Iter Pr(Prob)
## GroupType1 1 0.31040 0.310402 5000 < 0.00000000000000022 ***
## as.factor(timePeriod) 4 1.23931 0.309827 5000 < 0.00000000000000022 ***
## Residuals 67 0.36906 0.005508
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Sleep Types [%] Stacked
Number of individuals:
## [1] 18
REM [%] x Rank - Baseline
Number of mice:
## [1] 18
Summary:
Normality is not violated in any group
Df | F value | Pr(>F) | |
---|---|---|---|
group | 3 | 2.294382 | 0.0995947 |
28 | NA | NA |
Groupwise variances are not heteroscedastic (although a trend exists)
SWA x Rank - Baseline
Load EEG FFT23:
## [1] "FFT files: 243"
## [1] 0.8943566
Number of mice:
## [1] 18
Summary:
Normality is not violated in any group
Df | F value | Pr(>F) | |
---|---|---|---|
group | 3 | 0.4157312 | 0.7429968 |
29 | NA | NA |
Groupwise variances are not heteroscedastic
Fragmentation x Rank - baseline
Number of mice:
## [1] 18
Summary:
Normality is not violated in any group
Df | F value | Pr(>F) | |
---|---|---|---|
group | 3 | 0.9178924 | 0.4449394 |
28 | NA | NA |
Groupwise variances are not heteroscedastic (although a trend exists)
Posthoc - Light:
Posthoc - Dark:
REM Duration x Rank x Stage
Boxplots
Number of mice:
## [1] 18
Summary:
Normality is not violated in any group
Df | F value | Pr(>F) | |
---|---|---|---|
group | 11 | 1.943249 | 0.0497985 |
64 | NA | NA |
Groupwise variances are not heteroscedastic (although a trend exists)