This function models the sprint split times 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. time
is used as target or outcome
variable, and distance
as predictor.
mixed_model_using_split_times( data, distance, time, athlete, time_correction = 0, na.rm = FALSE, ... )
data | Data frame |
---|---|
distance | Character string. Name of the column in |
time | Character string. Name of the column in |
athlete | Character string. Name of the column in |
time_correction | Numeric vector. Used to correct for different starting techniques. This correction is
done by adding |
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
, distance
,
time
, and time_correction
, corrected_time
, and pred_time
columns
Haugen TA, Tønnessen E, Seiler SK. 2012. The Difference Is in the Start: Impact of Timing and Start Procedure on Sprint Running Performance: Journal of Strength and Conditioning Research 26:473–479. DOI: 10.1519/JSC.0b013e318226030b.
data("split_times") mixed_model <- mixed_model_using_split_times(split_times, "distance", "time", "athlete") mixed_model$parameters#> $fixed #> MSS TAU MAC PMAX #> 1 8.350701 0.5092097 16.39934 34.23649 #> #> $random #> athlete MSS TAU MAC PMAX #> 1 John 8.112372 0.6308761 12.858900 26.07905 #> 2 Kimberley 7.229267 0.2924022 24.723707 44.68357 #> 3 Jim 9.222750 1.1688497 7.890449 18.19291 #> 4 James 10.052620 0.2089077 48.119903 120.93278 #> 5 Samantha 7.136494 0.2450125 29.127060 51.96627 #>