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Rest-Active Transition Probabilities

Source: R/circadian_transitions.R
transition.probability.Rd

Maximum-likelihood and Bayesian estimates of the rest-to-active and active-to-rest transition probabilities (Danilevicz et al. 2024): the transitions out of a state divided by its epochs at risk.

Usage

transition.probability(counts, threshold = 1, eps = 0.5)

Arguments

counts

Numeric activity vector; NA are dropped.

threshold

Counts at or above which an epoch is active (default 1).

eps

Bayesian Beta pseudo-count added to the transition count and the epochs at risk (default 0.5).

Value

A list with the maximum-likelihood and Bayesian tp_ra (rest to active) and tp_ar (active to rest), the active bout count, and the mean active bout length.

References

Danilevicz IM, van Hees VT, van der Heide F, Jacob L, Landre B, Benadjaoud MA, Sabia S (2024). “Measures of fragmentation of rest activity patterns: mathematical properties and interpretability based on accelerometer real life data.” BMC Medical Research Methodology, 24, 132. doi:10.1186/s12874-024-02255-w .

See also

state.transitions, activity.balance.index

Examples

counts <- c(rep(0, 50), rep(100, 20), rep(0, 40), rep(80, 30), rep(0, 60))
transition.probability(counts)
#> $tp_ar_mle
#> [1] 0.04
#> 
#> $tp_ra_mle
#> [1] 0.01342282
#> 
#> $tp_ar_bayes
#> [1] 0.04950495
#> 
#> $tp_ra_bayes
#> [1] 0.01672241
#> 
#> $n_active_bouts
#> [1] 2
#> 
#> $mean_active_bout
#> [1] 25
#>