View source: R/concentrationProfile.R
concentration_profile.distrProfile | R Documentation |
Generate training concentration profiles.
## S3 method for class 'distrProfile'
concentration_profile(object, session = NULL, what = NULL, ...)
## S3 method for class 'trackeRdata'
concentration_profile(
object,
session = NULL,
what = NULL,
limits = NULL,
parallel = FALSE,
unit_reference_sport = NULL,
scale = FALSE,
...
)
object |
An object of class |
session |
A numeric vector of the sessions to be used, defaults to all sessions. |
what |
The variables for which the distribution profiles
should be generated. Defaults to all variables in |
... |
Currently not used. |
limits |
A named list of vectors of two numbers to specify the
lower and upper limits for the variables in |
parallel |
Logical. Should computation be carried out in
parallel? Default is |
unit_reference_sport |
The sport to inherit units from
(default is taken to be the most frequent sport in
|
scale |
Logical. If |
An object of class conProfile
.
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 200 if grid = NULL
) and n
is
the number of sessions requested in the session
argument.
Attributes:
Each conProfile
object has the following attributes:
sport
: the sports corresponding to the columns of each
list component
session_times
: the session start and end times
corresponding 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 concentration 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
.
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
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