Version: 2.4.0.2
Date: 2022-01-06
Title: Data Processing after Running 'GGIR' for Accelerometer Data
Maintainer: Wei Guo <wei.guo3@nih.gov>
Description: Generate all necessary R/Rmd/shell files for data processing after running 'GGIR' (v2.4.0) for accelerometer data. In part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data.
URL: https://github.com/dora201888/postGGIR
BugReports: https://github.com/dora201888/postGGIR/issues
License: GPL-3
Imports: refund, denseFLMM, dplyr, xlsx, survival, stats, tidyr, zoo, ineq, cosinor, cosinor2, abind, accelerometry, ActCR, ActFrag, minpack.lm, kableExtra, GGIR
Depends: R (≥ 3.6.0)
Suggests: knitr, rmarkdown
Encoding: UTF-8
ByteCompile: true
Type: Package
VignetteBuilder: knitr
RoxygenNote: 7.1.2
NeedsCompilation: no
Packaged: 2022-01-06 13:33:20 UTC; guow4
Author: Wei Guo [aut, cre], Andrew Leroux [aut], Vadim Zipunnikov [aut], Kathleen Merikangas [aut]
Repository: CRAN
Date/Publication: 2022-01-06 14:30:02 UTC

Cosinor Model for Circadian Rhythmicity

Description

A parametric approach to study circadian rhythmicity assuming cosinor shape.

Usage

ActCosinor2(x, window = 1, n1440 = 1440)

Arguments

x

vector vector of dimension n*1440 which reprsents n days of 1440 minute activity data

window

The calculation needs the window size of the data. E.g window = 1 means each epoch is in one-minute window.

n1440

the number of points of a day. Default is 1440 for the minute-level data.

Value

A list with elements

mes

MESOR which is short for midline statistics of rhythm, which is a rhythm adjusted mean. This represents mean activity level.

amp

amplitude, a measure of half the extend of predictable variation within a cycle. This represents the highest activity one can achieve.

acro

acrophase, a meaure of the time of the overall high values recurring in each cycle. Here it has a unit of radian. This represents time to reach the peak.

acrotime

acrophase in the unit of the time (hours)

ndays

Number of days modeled

References

Cornelissen, G. Cosinor-based rhythmometry. Theor Biol Med Model 11, 16 (2014). https://doi.org/10.1186/1742-4682-11-16

Cosinor Model for Circadian Rhythmicity for the Whole Dataset

Description

A parametric approach to study circadian rhythmicity assuming cosinor shape.This function is a whole dataset wrapper for ActCosinor.

Usage

ActCosinor_long2(count.data, window = 1)

Arguments

count.data

data.frame of dimension n * (p+2) containing the p dimensional activity data for all n subject days. The first two columns have to be ID and Day. ID can be either character or numeric. Day has to be numeric indicating the sequence of days within each subject.

window

The calculation needs the window size of the data. E.g window = 1 means each epoch is in one-minute window.

Value

A data.frame with the following 5 columns

ID

ID

ndays

number of days

mes

MESRO, which is short for midline statistics of rhythm, which is a rhythm adjusted mean. This represents mean activity level.

amp

amplitude, a measure of half the extend of predictable variation within a cycle. This represents the highest activity one can achieve.

acro

acrophase, a meaure of the time of the overall high values recurring in each cycle. Here it has a unit of radian. This represents time to reach the peak.

acrotime

acrophase in the unit of the time (hours)

ndays

Number of days modeled

Extended Cosinor Model for Circadian Rhythmicity

Description

Extended cosinor model based on sigmoidally transformed cosine curve using anti-logistic transformation

Usage

ActExtendCosinor2(
  x,
  window = 1,
  lower = c(0, 0, -1, 0, -3),
  upper = c(Inf, Inf, 1, Inf, 27),
  n1440 = 1440
)

Arguments

x

vector vector of dimension n*1440 which represents n days of 1440 minute activity data

window

The calculation needs the window size of the data. E.g window = 1 means each epoch is in one-minute window.

lower

A numeric vector of lower bounds on each of the five parameters (in the order of minimum, amplitude, alpha, beta, acrophase) for the NLS. If not given, the default lower bound for each parameter is set to -Inf.

upper

A numeric vector of upper bounds on each of the five parameters (in the order of minimum, amplitude, alpha, beta, acrophase) for the NLS. If not given, the default lower bound for each parameter is set to Inf

n1440

the number of points of a day. Default is 1440 for the minute-level data.

Value

A list with elements

minimum

Minimum value of the of the function.

amp

amplitude, a measure of half the extend of predictable variation within a cycle. This represents the highest activity one can achieve.

alpha

It determines whether the peaks of the curve are wider than the troughs: when alpha is small, the troughs are narrow and the peaks are wide; when alpha is large, the troughs are wide and the peaks are narrow.

beta

It dertermines whether the transformed function rises and falls more steeply than the cosine curve: large values of beta produce curves that are nearly square waves.

acrotime

acrophase is the time of day of the peak in the unit of the time (hours)

F_pseudo

Measure the improvement of the fit obtained by the non-linear estimation of the transformed cosine model

UpMesor

Time of day of switch from low to high activity. Represents the timing of the rest- activity rhythm. Lower (earlier) values indicate increase in activity earlier in the day and suggest a more advanced circadian phase.

DownMesor

Time of day of switch from high to low activity. Represents the timing of the rest-activity rhythm. Lower (earlier) values indicate decline in activity earlier in the day, suggesting a more advanced circadian phase.

MESOR

A measure analogous to the MESOR of the cosine model (or half the deflection of the curve) can be obtained from mes=min+amp/2. However, it goes through the middle of the peak, and is therefore not equal to the MESOR of the cosine model, which is the mean of the data.

ndays

Number of days modeled.

References

Marler MR, Gehrman P, Martin JL, Ancoli-Israel S. The sigmoidally transformed cosine curve: a mathematical model for circadian rhythms with symmetric non-sinusoidal shapes. Stat Med.

Cosinor Model for Circadian Rhythmicity for the Whole Dataset

Description

Extended cosinor model based on sigmoidally transformed cosine curve using anti-logistic transformation.This function is a whole dataset wrapper for ActExtendCosinor.

Usage

ActExtendCosinor_long2(
  count.data,
  window = 1,
  lower = c(0, 0, -1, 0, -3),
  upper = c(Inf, Inf, 1, Inf, 27)
)

Arguments

count.data

data.frame of dimension n * (p+2) containing the p dimensional activity data for all n subject days. The first two columns have to be ID and Day. ID can be either character or numeric. Day has to be numeric indicating the sequence of days within each subject.

window

The calculation needs the window size of the data. E.g window = 1 means each epoch is in one-minute window. window size as an argument.

lower

A numeric vector of lower bounds on each of the five parameters (in the order of minimum, amplitude, alpha, beta, acrophase) for the NLS. If not given, the default lower bound for each parameter is set to -Inf.

upper

A numeric vector of upper bounds on each of the five parameters (in the order of minimum, amplitude, alpha, beta, acrophase) for the NLS. If not given, the default lower bound for each parameter is set to Inf

Value

A data.frame with the following 5 columns

ID

ID

ndays

number of days

minimum

Minimum value of the of the function.

amp

amplitude, a measure of half the extend of predictable variation within a cycle. This represents the highest activity one can achieve.

alpha

It determines whether the peaks of the curve are wider than the troughs: when alpha is small, the troughs are narrow and the peaks are wide; when alpha is large, the troughs are wide and the peaks are narrow.

beta

It dertermines whether the transformed function rises and falls more steeply than the cosine curve: large values of beta produce curves that are nearly square waves.

acrotime

acrophase is the time of day of the peak in the unit of the time (hours)

F_pseudo

Measure the improvement of the fit obtained by the non-linear estimation of the transformed cosine model

UpMesor

Time of day of switch from low to high activity. Represents the timing of the rest- activity rhythm. Lower (earlier) values indicate increase in activity earlier in the day and suggest a more advanced circadian phase.

DownMesor

Time of day of switch from high to low activity. Represents the timing of the rest-activity rhythm. Lower (earlier) values indicate decline in activity earlier in the day, suggesting a more advanced circadian phase.

MESOR

A measure analogous to the MESOR of the cosine model (or half the deflection of the curve) can be obtained from mes=min+amp/2. However, it goes through the middle of the peak, and is therefore not equal to the MESOR of the cosine model, which is the mean of the data.

Annotating the merged data for all accelerometer files in the GGIR output

Description

Annotating the merged ENMO/ANGLEZ data by adding some descriptive variables such as number of valid days and missing pattern.

Usage

DataShrink(
  studyname,
  outputdir,
  workdir,
  QCdays.alpha = 7,
  QChours.alpha = 16,
  summaryFN = "../summary/part24daysummary.info.csv",
  epochIn = 5,
  epochOut = 60,
  useIDs.FN = NULL,
  RemoveDaySleeper = FALSE,
  trace = FALSE,
  Step = 1
)

Arguments

studyname

character Specify the study name that used in the output file names

outputdir

character Directory where the GGIR output was stored.

workdir

character Directory where the output needs to be stored. Note that this directory must exist.

QCdays.alpha

number Minimum required number of valid days in subject specific analysis as a quality control step in part2. Default is 7 days.

QChours.alpha

number Minimum required number of valid hours in day specific analysis as a quality control step in part2. Default is 16 hours.

summaryFN

character Filename with or without directory for sample information in CSV format, which includes summary description of each accelerometer file. Some description will be extracted and merged into the ENMO/ANGLEZ data.

epochIn

number Epoch size to which acceleration was averaged (seconds) in GGIR output. Defaut is 5 seconds.

epochOut

number Epoch size to which acceleration was averaged (seconds) in part1. Defaut is 60 seconds.

useIDs.FN

character Filename with or without directory for sample information in CSV format, which inclues "filename" and "duplicate" in the headlines at least. If duplicate="remove", the accelerometer files will not be used in the data analysis of part 5-7. Defaut is NULL, which makes all accelerometer files will be used in part 5-7.

RemoveDaySleeper

logical Specify if the daysleeper nights are removed from the calculation of number of valid days for each subject. Default is FALSE.

trace

logical Specify if the intermediate results is printed when the function was executed. Default is FALSE.

Step

number Specify which of the varaible need to be cleaned. For example, Step = 1 for the "anglez" variable, and Step = 2 for the "enmo" variable.

Value

Files were written to the specified sub-directory, named as flag_ALL_studyname_ENMO.data.Xs.csv and flag_ALL_studyname_ANGLEZ.data.Xs.csv, which Xs is the epoch size to which acceleration was averaged (seconds) in GGIR output. This excel file includs the following columns,

filename

accelerometer file name

Date

date recored from the GGIR part2.summary file

id

IDs recored from the GGIR part2.summary file

calender_date

date in the format of yyyy-mm-dd

N.valid.hours

number of hours with valid data recored from the part2_daysummary.csv file in the GGIR output

N.hours

number of hours of measurement recored from the part2_daysummary.csv file in the GGIR output

weekday

day of the week-Day of the week

measurementday

day of measurement-Day number relative to start of the measurement

newID

new IDs defined as the user-defined function of filename2id(), e.g. substrings of the filename

Nmiss_c9_c31

number of NAs from the 9th to 31th column in the part2_daysummary.csv file in the GGIR output

missing

"M" indicates missing for an invalid day, and "C" indicates completeness for a valid day

Ndays

number of days of measurement

ith_day

rank of the measurementday, for example, the value is 1,2,3,4,-3,-2,-1 for measurementday = 1,...,7

Nmiss

number of missing (invalid) days

Nnonmiss

number of non-missing (valid) days

misspattern

indicators of missing/nonmissing for all measurement days at the subject level

RowNonWear

number of columnns in the non-wearing matrix

NonWearMin

number of minutes of non-wearing

Nvalid.day

number of valid days with/without removing daysleeper nights; It is equal to Nnonmiss when RemoveDaySleeper=FALSE.

daysleeper

If 0 then the person is a nightsleeper (sleep period did not overlap with noon) if value=1 then the person is a daysleeper (sleep period did overlap with noon).

remove16h7day

indicator of a key qulity control output. If remove16h7day=1, the day need to be removed. If remove16h7day=0, the day need to be kept.

duplicate

If duplicate="remove", the accelerometer files will not be used in the data analysis of part5-7.

Interdaily Statbility

Description

This function calcualte interdaily stability, a nonparametric metric of circadian rhtymicity

Usage

IS2(x)

Arguments

x

data.frame of dimension ndays by p, where p is the dimension of the data.

References

Junrui Di et al. Joint and individual representation of domains of physical activity, sleep, and circadian rhythmicity. Statistics in Biosciences.

Interdaily Statbility for the Whole Dataset

Description

This function calcualte interdaily stability, a nonparametric metric of circadian rhtymicity. This function is a whole dataset wrapper for IS

Usage

IS_long2(count.data, window = 1, method = c("average", "sum"))

Arguments

count.data

data.frame of dimension n * (1440+2) containing the 1440 dimensional activity data for all n subject days. The first two columns have to be ID and Day. ID can be either character or numeric. Day has to be numeric indicating the sequency of days within each subject.

window

an integer indicating what is the window to bin the data before the function can be apply to the dataset. For details, see bin_data.

method

character of "sum" or "average", function used to bin the data

Value

A data.frame with the following 2 columns

ID

ID

IS

IS

References

Junrui Di et al. Joint and individual representation of domains of physical activity, sleep, and circadian rhythmicity. Statistics in Biosciences.

Intradaily Variability

Description

This function calcualte intradaily variability, a nonparametric metric reprsenting fragmentation of circadian rhtymicity

Usage

IV2(x)

Arguments

x

vector of activity data

Value

IV

References

Junrui Di et al. Joint and individual representation of domains of physical activity, sleep, and circadian rhythmicity. Statistics in Biosciences.

Intradaily Variability for the Whole Dataset

Description

This function calcualte intradaily variability, a nonparametric metric reprsenting fragmentation of circadian rhtymicity. This function is a whole dataset wrapper for IV.

Usage

IV_long2(count.data, window = 1, method = c("average", "sum"))

Arguments

count.data

data.frame of dimension n * (1440+2) containing the 1440 dimensional activity data for all n subject days. The first two columns have to be ID and Day. ID can be either character or numeric. Day has to be numeric indicating the sequency of days within each subject.

window

an integer indicating what is the window to bin the data before the function can be apply to the dataset. For details, see bin_data.

method

character of "sum" or "average", function used to bin the data

Value

A data.frame with the following 5 columns

ID

ID

Day

Day

IV

IV

References

Junrui Di et al. Joint and individual representation of domains of physical activity, sleep, and circadian rhythmicity. Statistics in Biosciences.

Timne Metrics for Whole Dataset

Description

This function is a whole dataset wrapper for Time

Usage

PAfun(count.data, weartime, PA.threshold = c(50, 100, 400))

Arguments

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.

PA.threshold

threshold to calculate the time in minutes of sedentary, light, moderate and vigorous activity the data.

Value

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

time

time of certain state

Relative Amplitude

Description

This function calcualte relative amplitude, a nonparametric metric reprsenting fragmentation of circadian rhtymicity

Usage

RA2(x, window = 1, method = c("average", "sum"))

Arguments

x

vector vector of activity data

window

since the caculation of M10 and L5 depends on the dimension of data, we need to include window size as an argument.

method

character of "sum" or "average", function used to bin the data

Value

RA

References

Junrui Di et al. Joint and individual representation of domains of physical activity, sleep, and circadian rhythmicity. Statistics in Biosciences.

Relative Amplitude for the Whole Datset

Description

This function calcualte relative amplitude, a nonparametric metric of circadian rhtymicity. This function is a whole dataset wrapper for RA.

Usage

RA_long2(count.data, window = 1, method = c("average", "sum"))

Arguments

count.data

data.frame of dimension n * (p+2) containing the p dimensional activity data for all n subject days. The first two columns have to be ID and Day. ID can be either character or numeric. Day has to be numeric indicating the sequency of days within each subject.

window

since the caculation of M10 and L5 depends on the dimension of data, we need to include window size as an argument. This function is a whole dataset wrapper for RA.

method

character of "sum" or "average", function used to bin the data

Value

A data.frame with the following 3 columns

ID

ID

Day

Day

RA

RA

Modified SVDmiss function (package SpatioTemporal)

Description

Modify ncomp = min(ncol(X),nrow(X),ncomp) for the matrix with nrow(X)<ncol(X)

Usage

SVDmiss2(X, niter = 200, ncomp = dim(X)[2], conv.reldiff = 0.001)

Arguments

X

X Data matrix, with missing values marked by 'NA'.

niter

niter Maximum number of iterations to run before exiting, 'Inf' will run until the 'conv.reldiff' criteria is met.

ncomp

ncomp Number of SVD components to use in the reconstruction (>0).

conv.reldiff

conv.reldiff Assume the iterative procedure has converged when the relative difference between two consecutive iterations is less than 'conv.reldiff'.

Details

See SVDmiss(package:SpatioTemporal) for details.

Value

See SpatioTemporal:: SVDmiss for details

Time of A Certain activity State

Description

Calculate the total time of being in certain state, e.g. sedentary, active, MVPA, etc.

Usage

Time2(x, w, thresh, smallerthan = TRUE, bout.length = 1)

Arguments

x

vector of activity data.

w

vector of wear flag data with same dimension as x.

thresh

threshold to binarize the data.

smallerthan

Find a state that is smaller than a threshold, or greater than or equal to.

bout.length

minimum duration of defining an active bout; defaults to 1.

Value

Time

Timne Metrics for Whole Dataset

Description

This function is a whole dataset wrapper for Time

Usage

Time_long2(count.data, weartime, thresh, smallerthan = TRUE, bout.length = 1)

Arguments

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.

smallerthan

Find a state that is smaller than a threshold, or greater than or equal to.

bout.length

minimum duration of defining an active bout; defaults to 1.

Value

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

time

time of certain state

Total Volumen of Activity for Whole Dataset

Description

Calculate total volume of activity level, which includes TLAC (total log transfored activity counts), TAC (total activity counts).

Usage

Tvol2(count.data, weartime, logtransform = FALSE, log.multiplier = 9250)

Arguments

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.

logtransform

if TRUE, then calcualte TLAC. Or calculate TAC.

log.multiplier

number The coefficient used in the log transformation of the ENMO data, i.e. log( log.multiplier * ENMO + 1). Defaut is 9250.

Details

log transormation is defined as log(x+1).

Value

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

TAC

total activity count

TLAC

total log activity count

Main Call for Data Processing after Runing GGIR for Accelerometer Data

Description

This R script will generate all necessary R/Rmd/shell files for data processing after running GGIR for accelerometer data.

Usage

afterggir(
  mode,
  useIDs.FN = NULL,
  currentdir,
  studyname,
  bindir = NULL,
  outputdir,
  epochIn = 5,
  epochOut = 5,
  flag.epochOut = 60,
  log.multiplier = 9250,
  use.cluster = TRUE,
  QCdays.alpha = 7,
  QChours.alpha = 16,
  QCnights.feature.alpha = c(0, 0),
  Rversion = "R",
  filename2id = NULL,
  PA.threshold = c(50, 100, 400),
  desiredtz = "US/Eastern",
  RemoveDaySleeper = FALSE,
  part5FN = "WW_L50M100V400_T5A5",
  NfileEachBundle = 20,
  trace = FALSE
)

Arguments

mode

number Specify which of the five parts need to be run, e.g. mode = 0 makes that all R/Rmd/sh files are generated for other parts. When mode = 1, all csv files in the GGIR output directory were read, transformed and then merged. When mode = 2, the GGIR output files were checked and summarized in one excel sheet. When mode = 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. When mode = 4, the cleaned data was imputed.

useIDs.FN

character Filename with or without directory for sample information in CSV format, which including "filename" and "duplicate" in the headlines at least. If duplicate="remove", the accelerometer files will not be used in the data analysis of part 5-7. Defaut is NULL, which makes all accelerometer files will be used in part 5-7.

currentdir

character Directory where the output needs to be stored. Note that this directory must exist.

studyname

character Specify the study name that used in the output file names

bindir

character Directory where the accelerometer files are stored or list

outputdir

character Directory where the GGIR output was stored.

epochIn

number Epoch size to which acceleration was averaged (seconds) in GGIR output. Defaut is 5 seconds.

epochOut

number Epoch size to which acceleration was averaged (seconds) in part1. Defaut is 5 seconds.

flag.epochOut

number Epoch size to which acceleration was averaged (seconds) in part 3. Defaut is 60 seconds.

log.multiplier

number The coefficient used in the log transformation of the ENMO data, i.e. log( log.multiplier * ENMO + 1), which have been used in part 5-7. Defaut is 9250.

use.cluster

logical Specify if part1 will be done by parallel computing. Default is TRUE, and the CSV file in GGIR output will be merged for every 20 files first, and then combined for all.

QCdays.alpha

number Minimum required number of valid days in subject specific analysis as a quality control step in part2. Default is 7 days.

QChours.alpha

number Minimum required number of valid hours in day specific analysis as a quality control step in part2. Default is 16 hours.

QCnights.feature.alpha

number Minimum required number of valid nights in day specific mean and SD analysis as a quality control step in the JIVE analysis. Default is c(0,0), i.e. no additional data cleaning in this step.

Rversion

character R version, eg. "R/3.6.3". Default is "R".

filename2id

R function User defined function for converting filename to sample IDs. Default is NULL.

PA.threshold

number Threshold for light, moderate and vigorous physical activity. Default is c(50,100,400).

desiredtz

charcter desired timezone: see also http://en.wikipedia.org/wiki/Zone.tab. Used in g.inspectfile(). Default is "US/Eastern". Used in g.inspectfile() function to inspect acceleromether file for brand, sample frequency in part 2.

RemoveDaySleeper

logical Specify if the daysleeper nights are removed from the calculation of number of valid days for each subject. Default is FALSE.

part5FN

character Specify which output is used in the GGIR part5 results. Defaut is "WW_L50M100V400_T5A5", which means that part5_daysummary_WW_L50M100V400_T5A5.csv and part5_personsummary_WW_L50M100V400_T5A5.csv are used in the analysis.

NfileEachBundle

number Number of files in each bundle when the csv data were read and processed in a cluster. Default is 20.

trace

logical Specify if the intermediate results is printed when the function was executed. Default is FALSE.

Value

See postGGIR manual for details.

Bin data into longer windows

Description

Bin minute level data into different time resolutions

Usage

bin_data2(x = x, window = 1, method = c("average", "sum"))

Arguments

x

vector of activity data.

window

window size used to bin the original 1440 dimensional data into. Window size should be an integer factor of 1440

method

character of "sum" or "average", function used to bin the data

Value

a vector of binned data

Create a template shell script of postGGIR

Description

Create a template shell script of postGGIR, named as STUDYNAME_part0.maincall.R.

Usage

create.postGGIR()

Value

The function will create a template shell script of postGGIR in the current directory, names as STUDYNAME_part0.maincall.R

Data imputation for the cleaned data with annotation

Description

Data imputation for the merged ENMO data with annotation. The missing values were imputated by the average ENMO over all the valid days for each subject.

Usage

data.imputation(workdir, csvInput)

Arguments

workdir

character Directory where the output needs to be stored. Note that this directory must exist.

csvInput

character File name with or without directory for sample information in CSV format. The ENMO data will be read through read.csv(csvInput,header=1) command, and the missing values were imputated by the average ENMO over all the valid days for each subject at each time point. In this package, csvInput = flag_All_studyname_ENMO.data.Xs.csv.

Value

Files were written to the specified sub-directory, named as impu.flag_All_studyname_ENMO.data.Xs.csv, which Xs is the epoch size to which acceleration was averaged (seconds) in GGIR output. This excel file includs the following columns,

filename

accelerometer file name

Date

date recored from the GGIR part2.summary file

id

IDs recored from the GGIR part2.summary file

calender_date

date in the format of yyyy-mm-dd

N.valid.hours

number of hours with valid data recored from the part2_daysummary.csv file in the GGIR output

N.hours

number of hours of measurement recored from the part2_daysummary.csv file in the GGIR output

weekday

day of the week-Day of the week

measurementday

day of measurement-Day number relative to start of the measurement

newID

new IDs defined as the user-defined function of filename2id(), e.g. substrings of the filename

Nmiss_c9_c31

number of NAs from the 9th to 31th column in the part2_daysummary.csv file in the GGIR output

missing

"M" indicates missing for an invalid day, and "C" indicates completeness for a valid day

Ndays

number of days of measurement

ith_day

rank of the measurementday, for example, the value is 1,2,3,4,-3,-2,-1 for measurementday = 1,...,7

Nmiss

number of missing (invalid) days

Nnonmiss

number of non-missing (valid) days

misspattern

indicators of missing/nonmissing for all measurement days at the subject level

RowNonWear

number of columnns in the non-wearing matrix

NonWearMin

number of minutes of non-wearing

daysleeper

If 0 then the person is a nightsleeper (sleep period did not overlap with noon) if value=1 then the person is a daysleeper (sleep period did overlap with noon).

remove16h7day

indicator of a key qulity control output. If remove16h7day=1, the day need to be removed. If remove16h7day=0, the day need to be kept.

duplicate

If duplicate="remove", the accelerometer files will not be used in the data analysis of part5.

ImpuMiss.b

number of missing values on the ENMO data before imputation

ImpuMiss.a

number of missing values on the ENMO data after imputation

KEEP

The value is "keep"/"remove", e.g. KEEP="remove" if remove16h7day=1 or duplicate="remove" or ImpuMiss.a>0

Fragmentation Metrics

Description

Fragmentation methods to study the transition between two states, e.g. sedentary v.s. active.

Usage

fragmentation2(
  x,
  w,
  thresh,
  bout.length = 1,
  metrics = c("mean_bout", "TP", "Gini", "power", "hazard", "all")
)

Arguments

x

integer vector of activity data.

w

vector of wear flag data with same dimension as x.

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".

Details

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)

Value

A list with elements

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

References

Junrui Di, Andrew Leroux, Jacek Urbanek, Ravi Varadhan, Adam P. Spira, Jennifer Schrack, Vadim Zipunnikov. Patterns of sedentary and active time accumulation are associated with mortality in US adults: The NHANES study. bioRxiv 182337; doi: https://doi.org/10.1101/182337

Fragmentation Metrics for Whole Dataset

Description

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

Usage

fragmentation_long2(
  count.data,
  weartime,
  thresh,
  bout.length = 1,
  metrics = c("mean_bout", "TP", "Gini", "power", "hazard", "all"),
  by = c("day", "subject")
)

Arguments

count.data

data.frame of dimension n*1442 containing the 1440 minutes of activity data for all n subject days. The first two columns have to be ID and Day. ID can be either character or numeric. Day has to be numeric indicating the sequency of days within each subject.

weartime

data.frame with dimension of count.data. The first two columns have to be ID and Day.ID can be either character or numeric. Day has to be numeric indicating the sequencey of days within each subject.

thresh

threshold to define the two states.

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.

Details

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)

Value

A dataframe with some of the following columns

ID

identifier of the person

Day

numeric vector indicating the sequencey of days within each subject.

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

Transform the data and merge all accelerometer files in the GGIR output

Description

An accelerometer file was transformed into wide data matrix, in which the rows represent available days and the columns including all timestamps for 24 hours. Further, the wide data was merged together.

Usage

ggir.datatransform(
  outputdir,
  subdir,
  studyname,
  numericID = FALSE,
  sortByid = "newID",
  f0 = 1,
  f1 = 1e+06,
  epochIn = 5,
  epochOut = 600,
  mergeVar = 1
)

Arguments

outputdir

character Directory where the GGIR output was stored.

subdir

character Sub-directory where the summary output was stored under the current directory. Defaut is "data".

studyname

character Specify the study name that used in the output file names

numericID

logical Specify if the ID is numeric when checking ID errors in part2. Default is FALSE.

sortByid

character Specify the name of "ID" for each accelerometer file in the report of part5. The value could be "newID","id" and "filename". Defaut is "filename".

f0

number File index to start with (default = 1). Index refers to the filenames sorted in increasing order.

f1

number File index to finish with. Note that file ends with the minimum of f1 and the number of files available. Default = 1000000.

epochIn

number Epoch size to which acceleration was averaged (seconds) in GGIR output. Defaut is 5 seconds.

epochOut

number Epoch size to which acceleration was averaged (seconds) in part1. Defaut is 600 seconds.

mergeVar

number Specify which of the varaible need to be processed and merged. For example, mergeVar = 1 makes that the M$metalong varialbes were read from R data on the directory of /meta/basic under GGIR ourput directory, which includes "nonwearscore","clippingscore","lightmean","lightpeak","temperaturemean" and "EN". When mergeVar = 2, makes that the "enmo" and "anglez" varialbes were read from csv data on the directory of /meta/csv under GGIR ourput directory.

Value

mergeVar = 1

Six files were written to the specified sub-directory as follows,

nonwearscore_studyname_f0_f1_Xs.xlsx

Data matrix of nonwearscore, where f0 and f1 are the file index to start and finish with and Xs is the epoch size to which acceleration was averaged (seconds) in GGIR output.

clippingscore_studyname_f0_f1_Xs.xlsx

Data matrix of clippingscore

lightmean_studyname_f0_f1_Xs.xlsx

Data matrix of lightmean

lightpeak_studyname_f0_f1_Xs.xlsx

Data matrix of lightpeak

temperaturemean_studyname_f0_f1_Xs.xlsx

Data matrix of temperaturemean

EN_studyname_f0_f1_Xs.xlsx

Data matrix of EN

mergeVar = 2

Two files were written to the specified sub-directory as follows,

studyname_ENMO.dataf0_f1_Xs.xlsx

Data matrix of ENMO, where f0 and f1 are the file index to start and finish with and Xs is the epoch size to which acceleration was averaged (seconds) in GGIR output.

studyname_ANGLEZ.dataf0_f1_Xs.xlsx

Data matrix of ANGLEZ

Description of all accelerometer files in the GGIR output

Description

Description of all accelerometer files in the GGIR output and this script was executed when mode=2 in the main call.

Usage

ggir.summary(
  bindir = NULL,
  outputdir,
  studyname,
  numericID = FALSE,
  sortByid = "filename",
  subdir = "summary",
  part5FN = "WW_L50M125V500_T5A5",
  QChours.alpha = 16,
  filename2id = NULL,
  desiredtz = "US/Eastern",
  trace = FALSE
)

Arguments

bindir

character Directory where the accelerometer files are stored or list for the purpose of extracting the bin file list. Default=NULL when it is not available and therefore the bin file list is extracted from the /meta/basic folder of the GGIR output.

outputdir

character Directory where the GGIR output was stored.

studyname

character Specify the study name that used in the output file names

numericID

logical Specify if the ID is numeric when checking ID errors in part2. Default is FALSE.

sortByid

character Specify the name of "ID" for each accelerometer file in the report of part2. The value could be "newID","id" and "filename". Defaut is "filename".

subdir

character Sub-directory where the summary output was stored under the current directory. Defaut is "summary".

part5FN

character Specify which output is used in the GGIR part5 results. Defaut is "WW_L50M125V500_T5A5", which means that part5_daysummary_WW_L50M125V500_T5A5.csv and part5_personsummary_WW_L50M125V500_T5A5.csv are used in the analysis.

QChours.alpha

number Minimum required number of valid hours in day specific analysis as a quality control step in part2. Default is 16 hours.

filename2id

R function User defined function for converting filename to sample IDs. Default is NULL.

desiredtz

charcter desired timezone: see also http://en.wikipedia.org/wiki/Zone.tab. Used in g.inspectfile(). Default is "US/Eastern".

trace

logical Specify if the intermediate results is printed when the function was executed. Default is FALSE.

Value

Four files were written to the specified sub-directory

studyname_ggir_output_summary.xlsx

This excel file includs 9 pages as follows,

page 1

List of files in the GGIR output

page 2

Summary of files

page 3

List of duplicate IDs

page 4

ID errors

page 5

Number of valid days

page 6

Table of number of valid/missing days

page 7

Missing patten

page 8

Frequency of the missing pattern

page 9

Description of all accelerometer files

page 10

Inspects accelerometer file for key information, including: monitor brand, sample frequency and file header

studyname_ggir_output_summary_plot.pdf

Some plots such as the number of valid days, which were included in the part2a_studyname_postGGIR.report.html file as well.

part24daysummary.info.csv

Intermediate results for description of each accelerometer file.

studyname_samples_remove_temp.csv

Create studyname_samples_remove.csv file by filling "remove" in the "duplicate" column in this template. If duplicate="remove", the accelerometer files will not be used in the data analysis of part 5-7.

Modified jive.predict function (package: r.jive)

Description

Replace SVDmiss by SVDmiss2 in the function

Usage

jive.predict2(data.new, jive.output)

Arguments

data.new

data.new A list of two or more linked data matrices on which to estimate JIVE scores. These matrices must have the same column dimension N, which is assumed to be common.

jive.output

jive.output An object of class "jive", with row dimensions matching those for data.new.

Details

See jive.predict(package:r.jive) for details.

Value

See r.jive:: jive.predict for details

View phenotype variables

Description

This R script will generate plot for each variable and write description to a log file.

Usage

pheno.plot(
  inputFN,
  outFN = paste("plot_", inputFN, ".pdf", sep = ""),
  csv = TRUE,
  sep = " ",
  start = 3,
  read = TRUE,
  logFN = NULL,
  track = TRUE
)

Arguments

inputFN

character Input file name or input data

outFN

character Output pdf file name for the plots

csv

logical Specify if input file is a CSV file. Default is TRUE.

sep

character Separator between columns. Default is space. If csv=TRUE, this will not be used.

start

number The location of the first phenotype variable starts in the input file.

read

logical Specify if inputFN is a file name or a data. Default is TRUE when inputFN is a file name.

logFN

character File name of the log file. Default is NULL, while logFN=paste(inputFN,".log",sep="") in the function.

track

logical Specify if the intermediate results is printed when the function was executed. Default is TRUE.

Value

Files were written to the current directory. One is .pdf file for plots and the other is .log file for variable description.

Create Wear/Nonwear Flags

Description

Determine during which time period, subject should wear the device. It is preferable that user provide their own wear/non wear flag which should has the same dimension as the activity data. This function provide wear/non wear flag based on time of day.

Usage

wear_flag(count.data, start = "05:00", end = "23:00")

Arguments

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.

start

start time, a string in the format of 24hr, e.g. "05:00"; defaults to "05:00".

end

end time, a string in the format of 24hr, e.g. "23:00"; defaults to "23:00"

Details

Fragmentation metrics are usually defined when subject is awake. The weartime provide time periods on which those features should be extracted. This can be also used as indication of wake/sleep.

Value

A data.frame with same dimension and column name as the count.data, with 0/1 as the elments reprensting wear, nonwear respectively.