fragmentation_long2: Fragmentation Metrics for Whole Dataset
In postGGIR: Data Processing after Running 'GGIR' for Accelerometer Data

Description Usage Arguments Details Value

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

fragmentation_long2(
  count.data,
  weartime,
  thresh,
  bout.length = 1,
  metrics = c("mean_bout", "TP", "Gini", "power", "hazard", "all"),
  by = c("day", "subject")
)

`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.

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)

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