g.impute | R Documentation |
Functions takes the output from g.getmeta and information about the study protocol to label impute invalid time segments in the data.
g.impute(M, I, params_cleaning = c(),
desiredtz="", dayborder= 0, TimeSegments2Zero =c(), acc.metric = "ENMO", ID, ...)
M |
output from g.getmeta |
I |
output from g.inspectfile |
params_cleaning |
See g.part1 |
desiredtz |
See g.part1 |
dayborder |
See g.part1 |
TimeSegments2Zero |
Optional data.frame to specify which time segments need to be ignored for the imputation, and acceleration metrics to be imputed by zeros. The data.frame is expected to contain two columns named windowstart and windowend, with the start- and end time of the time segment in POSIXlt class. |
acc.metric |
See GGIR |
ID |
ID extracted in g.part2. |
... |
Any argument used in the previous version of g.impute, which will now be used to overrule the arguments specified with the parameter objects. |
metashort |
imputed short epoch variables |
rout |
matrix to clarify when data was imputed for each long epoch time window and the reason for imputation. Value = 1 indicates imputation. Columns 1 = monitor non wear, column 2 = clipping, column 3 = additional nonwear, column 4 = protocol based exclusion and column5 = sum of column 1,2,3 and 4. |
averageday |
matrix with n columns for n metrics values and m rows for m short epoch time windows in an average 24 hours period |
Vincent T van Hees <v.vanhees@accelting.com>
## Not run:
#inspect file:
I = g.inspectfile(datafile)
#autocalibration:
C = g.calibrate(datafile)
#get meta-data:
M = g.getmeta(datafile)
## End(Not run)
data(data.getmeta)
data(data.inspectfile)
#impute meta-data:
IMP = g.impute(M=data.getmeta, I=data.inspectfile)
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