Fragmentation metrics from time series. — g.fragmentation • GGIR

The function is used by g.part5 to derive time series fragmentation metrics. The function assumes that NA values and nonwear time is accounted for before the data enters the function.

Usage

g.fragmentation(frag.metrics = c("mean", "TP", "Gini", "power",
        "CoV", "NFragPM", "all"), LEVELS = c(), Lnames=c(), xmin=1)

Arguments

frag.metrics: Character with fragmentation metric to exract. Can be "mean", "TP", "Gini", "power", or "CoV", "NFragPM", or all the above metrics with "all". See details.
LEVELS: Numeric vector of behavioural level classes derived with identify_levels
Lnames: Character vector with names of classes used in LEVELS, see details.
xmin: Numeric scalar to indicate the minimum recordable fragment length. In g.part5 this is derived from the epoch length.

Value

List with Character object showing how decimals are separated

TP_PA2IN: Transition probability physical activity to inactivity
TP_IN2PA: Transition probability physical inactivity to activity
Nfrag_IN2LIPA: Number of inacitivty fragments succeeded by LIPA (light physical activity)
TP_IN2LIPA: Transition probability physical inactivity to LIPA
Nfrag_IN2MVPA: Number of inacitivty fragments succeeded by MVPA (moderate or vigorous physical activity)
TP_IN2MVPA: Transition probability physical inactivity to MVPA
Nfrag_MVPA: Number of MVPA fragments
Nfrag_LIPA: Number of LIPA fragments
mean_dur_MVPA: mean MVPA fragment duration
mean_dur_LIPA: mean LIPA fragment duration
Nfrag_IN: Number of inactivity fragments
Nfrag_PA: Number of activity fragments
mean_dur_IN: mean duration inactivity fragments
mean_dur_PA: mean duration activity fragments
Gini_dur_IN: Gini index corresponding to inactivity fragment durations
Gini_dur_PA: Gini index corresponding to activity fragment durations
CoV_dur_IN: Coefficient of Variance corresponding to inactivity fragment durations
CoV_dur_PA: Coefficient of Variance corresponding to activity fragment durations
alpha_dur_IN: Alpha of the fitted power distribution through inactivity fragment durations
alpha_dur_PA: Alpha of the fitted power distribution through activity fragment durations
x0.5_dur_IN: x0.5 corresponding to alpha_dur_IN
x0.5_dur_PA: x0.5 corresponding to alpha_dur_PA
W0.5_dur_IN: W0.5 corresponding to alpha_dur_IN
W0.5_dur_PA: W0.5 corresponding to alpha_dur_PA
NFragPM_IN: Number of IN fragments per minutes in IN
NFragPM_PA: Number of PA fragments per minutes in PA
SD_dur_IN: Standard deviation in the duration of inactivity fragments
SD_dur_PA: Standard deviation in the duration of physical activity fragments

Details

See package vignette for description of fragmentation metrics. In short, abbreviation "TP" refers to transition probality metrics, abbreviation "CoV" refers to Coefficient of Variance, and metric "NFragPM" refers to the Number of fragments per minute.

Regarding the Lnames argument. The class names included in this are categorised as follows:

Inactive - if name includes the character strings "day_IN_unbt" or "day_IN_bts".
LIPA - If name includes the character strings "day_LIG_unbt" or "day_LIG_bts".
MVPA - If name includes the character strings "day_MOD_unbt", "day_VIG_unbt", or "day_MVPA_bts"

Author

Vincent T van Hees <v.vanhees@accelting.com>

Examples

if (FALSE) {
    x = c(6, 5, 6, 7, 6, 6, 7, 6, 6, 5, 6, 6, 6, 5, 7, 6, 6, 5, 5, 5, 6, 7, 6,
        6, 6, 6, 7, 6, 5, 5, 5, 5, 5, 6, 6, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6,
        7, 7, 6, 5, 6, 5, 6, 5, rep(12, 11), 5, 6, 6, 6, 5, 6, rep(9, 14), 6,
        5, 7, 7, 6, 7, 7, 7, 6, 6, 6, 5, 6, 5, 5, 5, 6, 5, 5, 5, 5, 5, 5, 5)
  Lnames = c("spt_sleep", "spt_wake_IN", "spt_wake_LIG", "spt_wake_MOD",
            "spt_wake_VIG", "day_IN_unbt", "day_LIG_unbt", "day_MOD_unbt",
            "day_VIG_unbt", "day_MVPA_bts_10", "day_IN_bts_30",
             "day_IN_bts_10_30", "day_LIG_bts_10")
  out = g.fragmentation(frag.metrics = "all",
                        LEVELS = x,
                        Lnames=Lnames)}