distribution_profile {trackeR}

Generate training distribution profiles.

Description

Generate training distribution profiles.

Usage

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
)

Arguments

object

An object of class trackeRdata.

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 object (what = NULL).

grid

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.

parallel

Logical. Should computation be carried out in parallel? Default is FALSE.

unit_reference_sport

The sport to inherit units from (default is taken to be the most frequent sport in object).

Value

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 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 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.

References

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

Examples

data('run', package = 'trackeR')
dProfile <- distribution_profile(run, what = c("speed", "cadence_running"))
## Not run: 
plot(dProfile, smooth = FALSE)

## End(Not run)