Fits a multi-component cosinor model with the fundamental (24h) and harmonics (12h, 8h, etc.) to capture patterns the single cosinor misses, such as bimodal rhythms.
Usage
cosinor.extended(
counts,
timestamps,
harmonics = c(24, 12),
wear_time = NULL,
min_valid_hours = 10
)Arguments
- counts
Numeric vector of activity counts
- timestamps
POSIXct vector of timestamps
- harmonics
Vector of periods to include (default: c(24, 12) for 24h + 12h)
- wear_time
Optional logical vector indicating valid wear time
- min_valid_hours
Numeric. Valid-day criterion (GGIR includedaycrit): minimum wear hours for a day to count; default 10, 0/NULL to disable.
Value
A list with class "actiRhythm_cosinor_extended" containing:
- mesor
Rhythm-adjusted mean
- components
Data frame with amplitude, acrophase, acrophase_time for each harmonic
- dominant_period
Period with largest amplitude
- dominant_acrophase
Acrophase of dominant component
- r_squared
Model R-squared (should be higher than single-component)
- r_squared_improvement
Improvement over single 24h cosinor
Details
The multi-component model is:
Y(t) = M + sum_k[A_k * cos(2*pi*t/T_k - phi_k)]
Where T_k are the periods (24h, 12h, 8h, etc.)
Use it for:
Bimodal patterns (morning + evening peaks), captured by the 12h harmonic
Complex daily routines with multiple activity bouts
Shift workers or irregular schedules
References
Cornelissen G (2014). “Cosinor-based rhythmometry.” Theoretical Biology and Medical Modelling, 11, 16. doi:10.1186/1742-4682-11-16 .
Refinetti R, Cornelissen G, Halberg F (2007). “Procedures for numerical analysis of circadian rhythms.” Biological Rhythm Research, 38(4), 275–325. doi:10.1080/09291010600903692 .
Examples
# \donttest{
counts <- agd.counts(read.agd(example_agd()))
#> AGD file tables: awakenings, capsense, crouterEpoch, crouterMinute, data, filterCategory, filters, logDiaryTimes, logEventHistory, logEventType, proximity, settings, sleep, sqlite_sequence, wtvBouts
#> Epochs loaded: 9919
#> Sleep periods found: 4
#> Awakenings found: 41
#> Wear time bouts: 5
#> Capsense samples: 9912
result <- cosinor.extended(counts$axis1, counts$timestamp, harmonics = c(24, 12, 8))
print(result)
#> Multi-Component Cosinor Analysis
#>
#> MESOR (mean): 330.12
#> Pattern type: Strong 24h
#> R-squared: 0.5938 (59.4% of variance explained)
#> Improvement: +15.7% over single 24h cosinor
#>
#> Harmonic Components:
#> 24h: Amplitude=300.2, Peak=16:58 (17.0h), Power=74.0% *DOMINANT*
#> 12h: Amplitude=171.2, Peak=07:24 (7.4h), Power=24.1%
#> 8h: Amplitude=48.6, Peak=04:31 (4.5h), Power=1.9%
#>
#> [!] Bimodal pattern: two activity peaks per day
#> Estimated peaks at: around 07:00 and around 19:00
#>
#> F-statistic: 4.14, p-value: 9.58e-03
# }