fragmentation_long {ActFrag}

Fragmentation Metrics for Whole Dataset

Description

Fragmentation methods to study the transition between two states, e.g. sedentary v.s. active.This function is a whole dataset wrapper for fragmentation

Usage

fragmentation_long(
  count.data,
  weartime,
  thresh,
  bout.length = 1,
  metrics = c("mean_bout", "TP", "Gini", "power", "hazard", "all"),
  by = c("day", "subject")
)

Arguments

count.data

data.frame of dimension n*1442 containing the 1440 minutes of activity data for all n subject days. The first two columns have to be ID and Day. ID can be either character or numeric. Day has to be numeric indicating the sequency of days within each subject.

weartime

data.frame with dimension of count.data. The first two columns have to be ID and Day.ID can be either character or numeric. Day has to be numeric indicating the sequencey of days within each subject.

thresh

threshold to define the two states.

bout.length

minimum duration of defining an active bout; defaults to 1.

metrics

What is the fragmentation metrics to exract. Can be "mean_bout","TP","Gini","power","hazard",or all the above metrics "all".

by

Determine whether fragmentation is calcualted by day or by subjects (i.e. aggregate bouts across days). by-subject is recommended to gain more power.

Details

Metrics include mean_bout (mean bout duration), TP (between states transition probability), Gini (gini index), power (alapha parameter for power law distribution) hazard (average hazard function)

Value

A dataframe with some of the following columns

ID

identifier of the person

Day

numeric vector indicating the sequencey of days within each subject.

mean_r

mean sedentary bout duration

mean_a

mean active bout duration

SATP

sedentary to active transition probability

ASTP

bactive to sedentary transition probability

Gini_r

Gini index for active bout

Gini_a

Gini index for sedentary bout

h_r

hazard function for sedentary bout

h_a

hazard function for active bout

alpha_r

power law parameter for sedentary bout

alpha_a

power law parameter for active bout

Examples

data(example_activity_data)
count = example_activity_data$count
wear = example_activity_data$wear
frag_by_day = fragmentation_long(count.data = count,
weartime = wear,thresh = 100,bout.length = 1,
metrics = "all",by = "day")
tp_by_subject = fragmentation_long(count.data = count,
weartime = wear,thresh = 100,bout.length = 1,
metrics = "TP",by = "subject")