These functions model the sprint split times using mono-exponential equation, where time
is used as target or outcome variable, and distance
as predictor. Function
model_using_splits
provides the simplest model with estimated MSS
and TAU
parameters. Time correction using heuristic rule of thumbs (e.g., adding 0.3s to split times) can be
implemented using time_correction
function parameter. Function
model_using_splits_with_time_correction
, besides estimating MSS
and TAU
,
estimates additional parameter time_correction
. Function model_using_splits_with_corrections
,
besides estimating MSS
, TAU
and time_correction
, estimates additional parameter
distance_correction
. For more information about these function please refer to accompanying vignettes in
this package.
model_using_splits( distance, time, time_correction = 0, weights = 1, LOOCV = FALSE, na.rm = FALSE, ... ) model_using_splits_with_time_correction( distance, time, weights = 1, LOOCV = FALSE, na.rm = FALSE, ... ) model_using_splits_with_corrections( distance, time, weights = 1, LOOCV = FALSE, na.rm = FALSE, ... )
distance, time | Numeric vector. Indicates the position of the timing gates and time measured |
---|---|
time_correction | Numeric vector. Used to correct for different starting techniques. This correction is
done by adding |
weights | Numeric vector. Default is vector of 1.
This is used to give more weight to particular observations. For example, use |
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 the following estimated parameters:
MSS
, TAU
, MAC
, PMAX
, time_correction
, and
distance_correction
List with the following components:
RSE
, R_squared
, minErr
, maxErr
, and RMSE
Model returned by the nls
function
Data frame used to estimate the sprint parameters, consisting of distance
,
time
, weights
, 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.
split_times <- data.frame( distance = c(5, 10, 20, 30, 35), time = c(1.20, 1.96, 3.36, 4.71, 5.35) ) # Simple model simple_model <- with( split_times, model_using_splits(distance, time) ) print(simple_model)#> Estimated model parameters #> -------------------------- #> MSS TAU MAC PMAX #> 7.3937467 0.6377437 11.5936019 21.4300389 #> time_correction distance_correction #> 0.0000000 0.0000000 #> #> Model fit estimators #> -------------------- #> RSE R_squared minErr maxErr maxAbsErr RMSE #> 0.02240014 0.99988183 -0.02066091 0.02133341 0.02133341 0.01735107 #> MAE MAPE #> 0.01554950 0.60513742#> MSS TAU MAC PMAX #> 7.3937467 0.6377437 11.5936019 21.4300389 #> time_correction distance_correction #> 0.0000000 0.0000000# Model with correction of 0.3s model_with_correction <- with( split_times, model_using_splits(distance, time, time_correction = 0.3) ) print(model_with_correction)#> Estimated model parameters #> -------------------------- #> MSS TAU MAC PMAX #> 7.741672 1.137652 6.804956 13.170434 #> time_correction distance_correction #> 0.300000 0.000000 #> #> Model fit estimators #> -------------------- #> RSE R_squared minErr maxErr maxAbsErr RMSE #> 0.02193501 0.99988942 -0.02860385 0.01613049 0.02860385 0.01699078 #> MAE MAPE #> 0.01442655 0.78055040#> MSS TAU MAC PMAX #> 7.741672 1.137652 6.804956 13.170434 #> time_correction distance_correction #> 0.300000 0.000000# Model with time_correction estimation model_with_time_correction_estimation <- with( split_times, model_using_splits_with_time_correction(distance, time) ) print(model_with_time_correction_estimation)#> Estimated model parameters #> -------------------------- #> MSS TAU MAC PMAX #> 7.5452583 0.8851386 8.5243805 16.0796632 #> time_correction distance_correction #> 0.1612379 0.0000000 #> #> Model fit estimators #> -------------------- #> RSE R_squared minErr maxErr maxAbsErr RMSE #> 0.014535141 0.999965920 -0.013753975 0.010847344 0.013753975 0.009192830 #> MAE MAPE #> 0.008146573 0.284484426#> MSS TAU MAC PMAX #> 7.5452583 0.8851386 8.5243805 16.0796632 #> time_correction distance_correction #> 0.1612379 0.0000000# Model with time and distance correction estimation model_with_time_distance_correction_estimation <- with( split_times, model_using_splits_with_corrections(distance, time) ) print(model_with_time_distance_correction_estimation)#> Estimated model parameters #> -------------------------- #> MSS TAU MAC PMAX #> 8.070141 2.684240 3.006490 6.065701 #> time_correction distance_correction #> 3.038271 12.015492 #> #> Model fit estimators #> -------------------- #> RSE R_squared minErr maxErr maxAbsErr RMSE #> 0.011430609 0.999989462 -0.007871967 0.006166580 0.007871967 0.005111924 #> MAE MAPE #> 0.004572915 0.141619773#> MSS TAU MAC PMAX #> 8.070141 2.684240 3.006490 6.065701 #> time_correction distance_correction #> 3.038271 12.015492