g.part2: function to analyse and summarize pre-processed output from...

In PeteJWatson/ggircal: Raw Accelerometer Data Analysis

Description

Loads the output from g.part1 and then applies g.impute and g.analyse, after which the output is converted to .RData-format which will be used by g.shell.GGIR to generate reports. The variables in these reports are the same variables as described in g.analyse.

Usage

g.part2(datadir=c(),metadatadir=c(),f0=c(),f1=c(),strategy = 1, hrs.del.start = 0.5,
hrs.del.end = 0.5, maxdur = 7, includedaycrit = 16,
L5M5window = c(0,24), M5L5res = 10, winhr = 5, qwindow=c(0,24), qlevels = c(0.1),
ilevels = c(0,10), mvpathreshold = c(100),boutcriter = 0.8,ndayswindow=7,idloc=1,
do.imp=TRUE,storefolderstructure=FALSE,overwrite=FALSE,epochvalues2csv=FALSE,
mvpadur=c(1,5,10),selectdaysfile=c(),window.summary.size=10,dayborder=0,
bout.metric=2,closedbout=FALSE,desiredtz="Europe/London",
IVIS_windowsize_minutes = 60, IVIS_epochsize_seconds = 3600)

Arguments

datadir	Directory where the accelerometer files are stored or list, e.g. "C:/mydata" of accelerometer filenames and directories, e.g. c("C:/mydata/myfile1.bin","C:/mydata/myfile2.bin").
metadatadir	Directory where the output from g.part1 was stored
f0	File index to start with (default = 1). Index refers to the filenames sorted in increasing order
f1	File index to finish with (defaults to number of files available)
strategy	how to deal with knowledge about study protocol. value = 1 to select data based on hrs.del.start, hrs.del.end, and maxdur. Value = 2 to only use the data between the first midnight and the last midnight, value = 3 only selects the most active X days in the files. X is specified by argument ndayswindow See also g.impute
hrs.del.start	how many HOURS after start of experiment did wearing of monitor start?, see g.impute
hrs.del.end	how many HOURS before the end of the experiment did wearing of monitor definitely end?, see g.impute
maxdur	how many DAYS after start of experiment did experiment definitely stop? (set to zero if unknown = default), see g.impute
includedaycrit	minimum required number of valid hours in day specific analysis (NOTE: there is no minimum required number of hours per day in the summary of an entire measurement, every available hour is used to make the best possible inference on average metric value per week)
L5M5window	start and end time, in 24 hour clock hours, over which L5M5 needs to be calculated. The calculation is done based on the average day
M5L5res	resoltion of L5 and M5 analysis in minutes (default: 10 minutes)
winhr	window size in hours of L5 and M5 analysis (dedault = 5 hours)
qwindow	start and end time, in 24 hour clock hours, over which distribution in metric values need to be extracted. Value = c(0,24) will consider all 24 hours.
qlevels	array of percentiles for which value needs to be extracted. These need to be expressed as a fraction of 1, e.g. c(0.1, 0.5, 0.75). There is no limit to the number of percentiles. If left empty then percentiles will not be extracted. Distribution will be derived from short epoch metric data, see g.getmeta.
ilevels	Levels for acceleration value frequency distribution in mg, e.g. c(0,100,200) There is no constriction to the number of levels.
mvpathreshold	Threshold for MVPA estimation. Threshold needs to be based on metric ENMO. This can be a single number or an array of numbers, e.g. c(100,120). In the later case the code will estimate MVPA seperately for each threshold. If this variable is left blank c() then MVPA is not estimated
boutcriter	The variable boutcriter is a number between 0 and 1 and defines what fraction of a bout needs to be above the mvpathreshold
ndayswindow	If strategy is set to 3 then this is the size of the window as a number of days
idloc	If value = 1 (default) the code assumes that ID number is stored in the obvious header field. If value = 2 the code uses the character string preceding the character '_' in the filename as the ID number
do.imp	Whether to impute missing values (e.g. suspected of monitor non-wear) or not by g.impute. Default and recommended setting is TRUE
storefolderstructure	Store folder structure of the accelerometer data
overwrite	Overwrite previously generated milestone data by this function for this particular dataset. If FALSE then it will skip the previously processed files (default = FALSE).
epochvalues2csv	If TRUE then epoch values are exported to a CSV spreadsheet. Here, non-wear time is imputed where possible (default = FALSE).
mvpadur	default = c(1,5,10). Three bout duration for which MVPA will be calculated
selectdaysfile	Functionality designed for the London Centre of Longidutinal studies. Csv file holding the relation between device serial numbers and measurement days of interest.
dayborder	Hour at which days start and end (default = 0), value = 4 would mean 4am
window.summary.size	Functionality designed for the London Centre of Longidutinal studies. Size in minutes of the summary window
bout.metric	This argument used to be called mvpa.2014 and had TRUE or FALSE as its value. However, it has now become clear that this aspect of the analyses is still very much open for debate. Therefore, I have changed it into an argument where you can specify a metric for bout detection based on a number. A description of these bout metrics can be found in the new function g.getbout
closedbout	See g.getbout
desiredtz	see g.getmeta
IVIS_windowsize_minutes	Window size of the Intradaily Variability (IV) and Interdaily Stability (IS) metrics in minutes
IVIS_epochsize_seconds	Epoch size of the Intradaily Variability (IV) and Interdaily Stability (IS) metrics in seconds

Value

The function provides no values, it only ensures that other functions are called and that their output is stored in the folder structure as created with g.part1.

Author(s)

Vincent T van Hees <[e-mail]>

References

• van Hees VT, Gorzelniak L, Dean Leon EC, Eder M, Pias M, et al. (2013) Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity. PLoS ONE 8(4): e61691. doi:10.1371/journal.pone.0061691

• van Hees VT, Fang Z, Langford J, Assah F, Mohammad A, da Silva IC, Trenell MI, White T, Wareham NJ, Brage S. Auto-calibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. J Appl Physiol (1985). 2014 Aug 7

Examples

## Not run: 
metadatadir = "C:/myresults/output_mystudy"
g.part2(metadatadir)

## End(Not run)

PeteJWatson/ggircal documentation built on Nov. 24, 2021, 11:14 a.m.