<p>'pa3' is in tbl_df format. It has 13 observations for 3 individuals, and the variables are "ID", "ID_Nday" (the ith day observation for an individual), and activity. The activity variable is an embedded list with each element consisting of a vector of one-day observation.</p>

datasets

Penalized Multi-Band Learning algorithm can be effectively implemented for circadian rhythm analysis and daily activity pattern characterization using actigraphy (continuously measured objective physical activity data). Functions for interactive visualization of actigraph data are also included. Method reference: Li, X., Kane, M., Zhang, Y., Sun, W., Song, Y., Dong, S., Lin, Q., Zhu, Q., Jiang, F., Zhao, H. (2019) A Novel Penalized Multi-band Learning Approach Characterizes the Consolidation of Sleep-Wake Circadian Rhythms During Early Childhood Development.

Xinyue Li

Penalized Multi-Band Learning for Circadian Rhythm Analysis
Using Actigraphy

pa3: An example of reformated individual activity data

Description

Usage

Arguments

Format