R: Data Processing after Running 'GGIR' for Accelerometer Data

# Data Processing after Running 'GGIR' for Accelerometer Data

## Documentation for package ‘postGGIR’ version 2.4.0.2

- 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
afterggir | Main Call for Data Processing after Runing GGIR for Accelerometer Data
bin_data2 | Bin data into longer windows
create.postGGIR | Create a template shell script of postGGIR
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
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)
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