PML-package: Penalized Multi-Band Learning for Circadian Rhythm Analysis...

In xinyue-L/PML: Penalized Multi-Band Learning for Circadian Rhythm Analysis Using Actigraphy

Description

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.

Details

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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.

Author(s)

Xinyue Li [aut, cre], Michael Kane [aut]

Maintainer: Xinyue Li <[e-mail]>

References

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.

Fisher, R. A. (1929). Tests of significance in harmonic analysis. Proceedings of the Royal Society of London. Series A, 125(796), 54-59.

Examples

library(PML)
##reformat data for further analysis
data(lis3)
pa3 <- form(lis3)

##apply Penalized Multi-band Learning
data(pa3)
re <- bandSelect(df=pa3,Nlength=1440*3,Nlambda=100,alpha=1,Ntop=3,cross=FALSE,Ncross=NULL,plot=TRUE)

##use trelliscope to visualize data:
##return a dataset with trelliscope panels for individual mean activity plots
data(var3)
tre.ind <- tre(lis3,varlis=var3)
tre.ind$activity_ind <- tre.ind$activity_all <- NULL

xinyue-L/PML documentation built on May 13, 2020, 8:36 a.m.