g.fragmentation: Fragmentation metrics from time series.

In GGIR: Raw Accelerometer Data Analysis

Fragmentation metrics from time series.

Description

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, mode = "day")

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.
mode	Character to indicate whether input data is daytime ("day") or sleep period time ("spt").

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"

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

Author(s)

Vincent T van Hees <[e-mail]>

Examples

## Not run: 
    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)
## End(Not run)

GGIR documentation built on Oct. 17, 2023, 1:12 a.m.