Generate training distribution profiles.
distribution_profile(object, session = NULL, what = NULL,
grid = NULL, parallel = FALSE, unit_reference_sport = NULL)distributionProfile(object, session = NULL, what = NULL, grid = NULL,
parallel = FALSE, unit_reference_sport = NULL)
An object of class distrProfile.
Object:
A named list with one or more components, corresponding to the
value of what. Each component is a matrix of dimension
g times n, where g is the length of the grids
set in grid (or 201 if grid = NULL) and n is
the number of sessions requested in the session argument.
Attributes:
Each distrProfile object has the following attributes:
sport: the sports corresponding to the columns of each
list component
session_times: the session start and end times
correspoding to the columns of each list component
unit_reference_sport: the sport where the units have
been inherited from
operations: a list with the operations that have been
applied to the object. See get_operations.distrProfile
limits: The variable limits that have been used for the
computation of the distribution profiles
units: an object listing the units used for the
calculation of distribution profiles. These is the output of
get_units on the corresponding
trackeRdata object, after inheriting units from
unit_reference_sport.
An object of class trackeRdata.
A numeric vector of the sessions to be used, defaults to all sessions.
The variables for which the distribution profiles
should be generated. Defaults to all variables in
object (what = NULL).
A named list containing the grid values for the
variables in what. If NULL (default) the grids
for the variables in what are inferred from
object.
Logical. Should computation be carried out in
parallel? Default is FALSE.
The sport to inherit units from
(default is taken to be the most frequent sport in
object).
Kosmidis, I., and Passfield, L. (2015). Linking the Performance of Endurance Runners to Training and Physiological Effects via Multi-Resolution Elastic Net. ArXiv e-print arXiv:1506.01388.
Frick, H., Kosmidis, I. (2017). trackeR: Infrastructure for Running and Cycling Data from GPS-Enabled Tracking Devices in R. Journal of Statistical Software, 82(7), 1--29. doi:10.18637/jss.v082.i07
data('run', package = 'trackeR')
dProfile <- distribution_profile(run, what = c("speed", "cadence_running"))
if (FALSE) {
plot(dProfile, smooth = FALSE)
}
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