This function models the sprint instantaneous velocity using mono-exponential equation and non-linear
mixed model using nlme
to estimate fixed and random maximum sprinting speed (MSS
)
and relative acceleration (TAU
) parameters. In mixed model, fixed and random effects are estimated for
MSS
and TAU
parameters using athlete
as levels. velocity
is used as target or outcome
variable, and time
as predictor.
mixed_model_using_radar( data, time, velocity, athlete, time_correction = 0, random = MSS + TAU ~ 1, LOOCV = FALSE, na.rm = FALSE, ... )
data | Data frame |
---|---|
time | Character string. Name of the column in |
velocity | Character string. Name of the column in |
athlete | Character string. Name of the column in |
time_correction | Numeric vector. Used to filter out noisy data from the radar gun.
This correction is done by adding |
random | Formula forwarded to |
LOOCV | Should Leave-one-out cross-validation be used to estimate model fit? Default is |
na.rm | Logical. Default is FALSE |
... | Forwarded to |
List object with the following elements:
List with two data frames: fixed
and random
containing the following
estimated parameters: MSS
, TAU
, MAC
, and PMAX
List with the following components:
RSE
, R_squared
, minErr
, maxErr
, and RMSE
Model returned by the nlme
function
Data frame used to estimate the sprint parameters, consisting of athlete
, time
,
velocity
, and pred_velocity
columns
Samozino P. 2018. A Simple Method for Measuring Force, Velocity and Power Capabilities and Mechanical Effectiveness During Sprint Running. In: Morin J-B, Samozino P eds. Biomechanics of Training and Testing. Cham: Springer International Publishing, 237–267. DOI: 10.1007/978-3-319-05633-3_11.
data("radar_gun_data") mixed_model <- mixed_model_using_radar(radar_gun_data, "time", "velocity", "athlete") # mixed_model$parameters coef(mixed_model)#> $fixed #> MSS TAU MAC PMAX #> 8.301178 1.007782 8.237080 17.094367 #> time_correction distance_correction #> 0.000000 0.000000 #> #> $random #> athlete MSS TAU MAC PMAX time_correction #> 1 James 9.998556 1.1108457 9.000851 22.49888 0 #> 2 Jim 7.997945 0.8886712 8.999892 17.99516 0 #> 3 John 8.000051 1.0690357 7.483427 14.96695 0 #> 4 Kimberley 9.005500 1.2855706 7.005061 15.77102 0 #> 5 Samantha 6.503839 0.6847851 9.497635 15.44277 0 #> distance_correction #> 1 0 #> 2 0 #> 3 0 #> 4 0 #> 5 0 #>