Rest-Activity State Transition Rates (kRA, kAR)

Computes the rest-to-activity and activity-to-rest transition rates from a binarized activity series, following the survival-curve method used by pyActigraphy (Lim et al. 2011). Thresholds the series into rest/active epochs, builds the per-lag transition probability (hazard) of each bout type from the bout-length survival curve, and takes a single rate over the LOWESS "sustained" plateau of that curve.

Usage

state.transitions(counts, threshold = 1, frac = 0.3, iter = 0)

Arguments

counts

Numeric activity vector; NA are dropped.

threshold

Activity level at or above which an epoch is "active" (default 1, i.e. any non-zero count).

frac

LOWESS smoother span for the sustained-region search (default 0.3).

iter

LOWESS robustifying iterations (default 0).

Value

An object of class actiRhythm_transitions: a list with kRA/kAR (sustained rest-to-active / active-to-rest rates over the LOWESS plateau), pRA/pAR (overall rest-to-active / active-to-rest rate, the reciprocal mean bout length), bout counts, and the two transition curves. The plateau search follows the pyActigraphy implementation of Lim et al. (2011).

References

Lim ASP, Yu L, Costa MD, Buchman AS, Bennett DA, Leurgans SE, Saper CB (2011). “Quantification of the fragmentation of rest-activity patterns in elderly individuals using a state transition analysis.” Sleep, 34(11), 1569–1581. doi:10.5665/sleep.1400 .

Hammad G, Reyt M, Beliy N, Baillet M, Deantoni M, Lesoinne A, Muto V, Schmidt C (2021). “pyActigraphy: open-source python package for actigraphy data visualization and analysis.” PLOS Computational Biology, 17(10), e1009514. doi:10.1371/journal.pcbi.1009514 .

Examples

set.seed(1)
counts <- as.integer(stats::runif(5000) < 0.1) * 100
state.transitions(counts)
#> Rest-Activity State Transitions
#> 
#>   Threshold:  >= 1 counts = active
#>   kRA (rest->active): 0.1051   (471 rest bouts)
#>   kAR (active->rest): 0.8747   (470 active bouts)
#>   pRA / pAR:          0.1055 / 0.8752