This function models the sprint split times using mono-exponential equation that estimates maximum sprinting speed (MSS), relative acceleration (TAU) and time_correction. time is used as target or outcome variable, and distance as predictor.

model_using_split_times_with_time_correction(
  distance,
  time,
  weights = 1,
  na.rm = FALSE,
  ...
)

Arguments

distance

Numeric vector

time

Numeric vector

weights

Numeric vector. Default is vector of 1 This is used to give more weight to particular observations. For example, use 1\distance to give more weight to observations from shorter distances.

na.rm

Logical. Default is FALSE

...

Forwarded to nls function

Value

List object with the following elements:

parameters

List with the following estimated parameters: MSS, TAU, MAC, PMAX, and time_correction

model_fit

List with the following components: RSE, R_squared, minErr, maxErr, and RMSE

model

Model returned by the nls function

data

Data frame used to estimate the sprint parameters, consisting of distance, time, weights,and pred_time columns

Examples

split_times <- data.frame( distance = c(5, 10, 20, 30, 35), time = c(1.21, 1.99, 3.38, 4.71, 5.30) ) sprint_model <- with( split_times, model_using_split_times_with_time_correction(distance, time) ) sprint_model$parameters
#> $MSS #> [1] 7.927337 #> #> $TAU #> [1] 1.187946 #> #> $MAC #> [1] 6.673144 #> #> $PMAX #> [1] 13.22507 #> #> $time_correction #> [1] 0.2724719 #>