Analyses the output from other functions within the packages to generate a basic descriptive summary for each accelerometer data file. Analyses include: Average acceleration per day, per measurement, L5M5 analyses (assessment of the five hours with lowest acceleration and with highest acceleration). Further, the traditionally popular variable MVPA is automatically extracted in six variants: without bout criteria in combination with epoch = epoch length as defined in g.getmeta (first value of the input argument windowsizes), 1 minute, and 5 minutes, and for bout durations 1 minute, 5 minutes or 10 minutes in combination with the epoch length as defined in g.getmeta.
Arguments
- I
the output from function g.inspectfile
- C
the output from function g.calibrate
- M
the output from function g.getmeta
- IMP
the output from function g.impute
- params_247
See g.part2
- params_phyact
See g.part2
- quantiletype
type of quantile function to use (default recommended). For details, see quantile function in STATS package
- includedaycrit
See g.part1
- idloc
See g.part1
- snloc
If value = 1 (default) the code assumes that device serial number is stored in the obvious header field. If value = 2 the code uses the character string between the first and second character '_' in the filename as the serial number
- dayborder
See g.part1
- desiredtz
See g.part1
- myfun
External function object to be applied to raw data, see g.getmeta.
- acc.metric
Character, see g.part1.
- ...
Any argument used in the previous version of g.analyse, which will now be used to overrule the arguments specified with the parameter objects.
Value
g.analyse generated two data,franeL
summarysummary for the file that was analysed
daysummarysummary per day for the file that was analysed
These data.frames are used by function g.report.part2 to generate csv reports. An exaplantion of all the columns in the data.frame and subsequent csv reports can be found in the package vignette (Output part 2).
Examples
data(data.getmeta)
data(data.inspectfile)
data(data.calibrate)
if (FALSE) {
#inspect file:
I = g.inspectfile(datafile)
#autocalibration:
C = g.calibrate(datafile)
#get meta-data:
M = g.getmeta(datafile, desiredtz = "Europe/London",
windowsizes = c(5, 900, 3600),
daylimit = FALSE, offset = c(0, 0, 0),
scale = c(1, 1, 1), tempoffset = c(0, 0, 0))
}
#impute meta-data:
IMP = g.impute(M = data.getmeta, I = data.inspectfile)
#analyse and produce summary:
A = g.analyse(I = data.inspectfile, C = data.calibrate,
M = data.getmeta, IMP)