fragmentation_long: Fragmentation Metrics for Whole Dataset
In junruidi/actigraphy: Feature Extraction from Minute Level Actigraphy Data

Description Usage Arguments Details Value Examples

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

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fragmentation_long(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 minute activity data for all n subject days. The first two columns have to be ID and Day.
`weartime`	`data.frame` with dimension of `count.data`. The first two columns have to be ID and Day.
`thresh`	threshold to binarize the data.
`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`	indicator of which day of activity it is, can be a numeric vector of sequence 1,2,... or a string of date
`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

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