g.analyse: Function to analsyse meta-data generated by g.getmeta and...

In GGIR: Raw Accelerometer Data Analysis

Function to analsyse meta-data generated by g.getmeta and g.impute

Description

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.

Usage

  g.analyse(I, C, M, IMP, params_247 = c(), params_phyact = c(),
                      params_general = c(), params_cleaning = c(),
                      quantiletype = 7, myfun = c(), ID, ...)

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 GGIR
params_phyact	See GGIR
params_general	See GGIR
params_cleaning	See GGIR
quantiletype	type of quantile function to use (default recommended). For details, see quantile function in STATS package
myfun	External function object to be applied to raw data, see g.getmeta.
ID	ID extracted in g.part2.
...	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

summary	summary for the file that was analysed
daysummary	summary 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).

Author(s)

Vincent T van Hees <[e-mail]>

Examples

  data(data.getmeta)
  data(data.inspectfile)
  data(data.calibrate)
  ## Not run: 
    #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))
  
## End(Not run)
  #impute meta-data:
  IMP = g.impute(M = data.getmeta, I = data.inspectfile, ID = "01wk0")
  
  #analyse and produce summary:
  A = g.analyse(I = data.inspectfile, C = data.calibrate,
  M = data.getmeta, IMP, ID = "01wk0")

GGIR documentation built on Sept. 11, 2024, 8:59 p.m.