Cosinor Analysis for Circadian Rhythm — cosinor.analysis • actiRhythm

Fits a single-component cosinor model to activity data and returns the parametric circadian parameters.

Usage

cosinor.analysis(
  counts,
  timestamps,
  period = 24,
  wear_time = NULL,
  min_valid_hours = 10,
  transform = c("none", "log1p")
)

Arguments

counts: Numeric vector of activity counts
timestamps: POSIXct vector of timestamps
period: Period in hours (default: 24 for circadian rhythm)
wear_time: Optional logical vector indicating 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.
transform: Input transform: "none" (default) or "log1p", GGIR's log(ENMO_mg + 1) pre-transform for raw acceleration metrics (g-unit input is scaled to mg first).

Value

List with class 'actiRhythm_cosinor' containing:

mesor: Rhythm-adjusted mean (MESOR)
amplitude: Half the peak-to-trough difference
acrophase: Time of peak activity (hours, 0-24)
acrophase_time: Acrophase as HH:MM string
r_squared: Goodness of fit (coefficient of determination)
f_statistic: F-statistic for model significance
p_value: P-value for model significance

Details

The single-component cosinor model fits: $$Y(t) = M + A \cdot cos(2\pi t / T + \phi)$$

Where:

M = mesor (rhythm-adjusted mean)
A = amplitude (half peak-to-trough difference)
T = period (typically 24 hours)
$\phi$ = acrophase (phase angle, converted to time)

The model is fit using linear least squares on the linearized form: $$Y(t) = M + \beta_1 \cos(2\pi t / T) + \beta_2 \sin(2\pi t / T)$$

References

Nelson W, Tong YL, Lee JK, Halberg F (1979). “Methods for cosinor-rhythmometry.” Chronobiologia, 6(4), 305–323.

Cornelissen G (2014). “Cosinor-based rhythmometry.” Theoretical Biology and Medical Modelling, 11, 16. doi:10.1186/1742-4682-11-16 .

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.analysis(counts$axis1, counts$timestamp)
print(result)
#> Cosinor Analysis Results
#> 
#> Period:     24 hours
#> N obs:      9919 (8.0 days)
#> 
#> Parameters:
#>   MESOR:      327.25 (rhythm-adjusted mean)
#>   Amplitude:  295.63 (half peak-to-trough)
#>   Acrophase:  16:55 (16.92 h, time of peak)
#> 
#> Model Fit:
#>   R-squared:  0.4371
#>   F-statistic: 8.15
#>   P-value:    2.4e-03
# }