Processing of Accelerometry Data with 'GGIR' in mMARCH

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Documentation for package ‘mMARCH.AC’ version 2.9.2.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

ActCosinor2	Cosinor Model for Circadian Rhythmicity
ActCosinor_long2	Cosinor Model for Circadian Rhythmicity for the Whole Dataset
ActExtendCosinor2	Extended Cosinor Model for Circadian Rhythmicity
ActExtendCosinor_long2	Cosinor Model for Circadian Rhythmicity for the Whole Dataset
bin_data2	Bin data into longer windows
create.shell	Create a template shell script of mMARCH.AC
data.imputation	Data imputation for the cleaned data with annotation
DataShrink	Annotating the merged data for all accelerometer files in the GGIR output
fragmentation2	Fragmentation Metrics
fragmentation_long2	Fragmentation Metrics for Whole Dataset
get_mean_sd_hour	get subject average of time variables
ggir.datatransform	Transform the data and merge all accelerometer files in the GGIR output
ggir.summary	Description of all accelerometer files in the GGIR output
IS2	Interdaily Statbility
IS_long2	Interdaily Statbility for the Whole Dataset
IV2	Intradaily Variability
IV_long2	Intradaily Variability for the Whole Dataset
jive.predict2	Modified jive.predict function (package: r.jive)
makeSleepDataMatrix	Make a sleep matrix based on the sleep onset and wake up time
mMARCH.AC.maincall	Main Call for Data Processing after Runing GGIR for Accelerometer Data
PAfun	Timne Metrics for Whole Dataset
pheno.plot	View phenotype variables
RA2	Relative Amplitude
RA_long2	Relative Amplitude for the Whole Datset
SVDmiss2	Modified SVDmiss function (package SpatioTemporal)
Time2	Time of A Certain activity State
Time_long2	Timne Metrics for Whole Dataset
Tvol2	Total Volumen of Activity for Whole Dataset
wear_flag	Create Wear/Nonwear Flags