FV_slope_perc to profile_imb
in the output of the optimal_ functionsfind_optimal_FV() and
find_optimal_FV_peak() are now wrapped inside the
optimal_FV, but the method is selected using
method parameter (“max” for find_optimal_FV()
and “peak” for find_optimal_FV_peak()). Please refer to
examplesoptimal_ functions
and moved to probe_FV() and probe_MSS_MAC()
functions. Please refer to examplesfind_optimal_distance() function that can take
other optimal and probe functions as parameter. Please refer to
examplesfind_optimal_FV_peak() (now
implemented in find_optimal_FV() function using
method="peak" parameter) where information needed to
calculate air resistance was not forwardedfind_optimal_MSS_MAC() and find_optimal_FV()
functions. Some of the columns are renamed to better clarityconvert_FV() to convert from FV
profile to sprint profile. This is used to calculate Peak Power
(Ppeak) metric and its locationmin_func parameter to
find_optimal_MSS_MAC_distance() and
find_optimal_FV_distance(). This allows to optimize by
using any other metricfind_optimal_FV_peak() function, which optimize
the profile while keeping the Peak Power the same. Accompanying
find_optimal_FV_peak_distance() is also addedformat_splits()model_tether() function for modeling data output
from tether devices, which involve velocity at distancePmax_relative to Pmax_rel in
the output of the make_FV_profile() functionF0_poly,
F0_poly_rel, V0_poly, Pmax_poly,
Pmax_poly_rel, and FV_slope_poly to the output
of the make_FV_profile() function using the method outlined
in Pierre Samozino and Nicolas Peyror, et al (2021) <doi:
10.1111/sms.14097>predict_time_at_distance_FV() which uses FV
profile’s F0 and V0 to predict time at
distance. For more info see Pierre Samozino and Nicolas Peyror, et al
(2021) <doi: 10.1111/sms.14097>find_optimal_MSS_MAC(),
find_optimal_MSS_MAC_distance(),
find_optimal_FV(), find_optimal_FV_distance().
For more info on the FV optimization see Pierre Samozino and Nicolas
Peyror, et al (2021) <doi: 10.1111/sms.14097>This is NEW version of the {shorts} package INCOMPATIBLE with the previous due to drastic changes in functions. Here are the changes utilized:
predict_ functions, time_correction and
distance_correction are no longer used, since due to novel
models of estimation, it is hard to neatly implement them into
functions. Now the predict_ functions predict on a scale
where sprint starts at t=0 and d=0, rather
than on the original (data) scale. This will also remove the confusion
for the user.predict_ functions, the user now uses
MSS and MAC parametersstats::nls() to minpack.lm::nlsLM() in
model_ functions. This is done to avoid “singular gradient”
error and inability of the stats::nls() to estimate when
there are zero residuals. Please make note that now when you use
... in model_ function, it will be forwarded
to minpack.lm::nlsLM(). If you have been using
control = stats::nls.control(warnOnly = TRUE) to avoid
stats::nls() to throw error when fitting when there are
zero residuals, now you can remove it. If needed use
control = minpack.lm::nls.lm.control() instead.create_timing_gates_splits() function to generate
timing gates splitsmodel_timing_gates(),
model_timing_gates_TC(),
model_timing_gates_FD(), and
model_timing_gates_FD_TC(). All other functions have been
removedmodel_radar_gun() which also estimates time correction
(TC) parameter.model_radar_gun() feature n-folds
cross-validation, as opposed to
model_timing_gates() family of functions, which features
leave-one-out cross-validation (LOOCV) due to small number of
observations. Using the CV parameter, set n-fold
cross-validations for the model_radar_gun() function.LOOCV in the
shorts_model object to CV to reflect above
changes in model_radar_gun() functionmodel_using_splits_with_distance_correction() function the
predict_XXX_at_distance() family of functions doesn’t work
correctly if distance_correction is used as parameter
(i.e., different than zero). This is because the model definition is
completely different, and predicting on the same distance scale is not
possible. Please refer to Jovanović, M., Vescovi, J.D. (2020) for more
information.plot.shorts_fv_profile() functionresiduals() S3 method, as well as with internal
function shorts_model_fit() that provides model fit
estimates (i.e. RMSE, MAE, MAPE)model_using_splits_with_distance_correction()
function that implements novel model definition to estimate flying start
distancemixed_model_using_splits_with_distance_correction()
function that implements novel model definition to estimate flying start
distanceggplot2 and tidyr package dependency
and implemented it in S3 plotting functionsget_FV_profile now return a object class
shorts_fv_profileshorts_fv_profile
objectforce and relative_force to
horizontal_force and relative_horizontal_force
in the shorts_fv_profile object and data frame returned by
the predict_kinematicsacceleration, bodymass,
net_horizontal_force, air_resistance, and
vertical_force, resultant_force_relative,
power, and force_angle in the
shorts_fv_profile object and data frame returned by the
predict_kinematicsshorts_model and
shorts_mixed_model objectsAdded sample radar gun data from Jean-Benoît Morin Microsoft
Excel spreadsheet, freely available at his website
(accessed October 27, 2020)). For more information
?jb_morin
Added get_FV_profile for generating Force-Velocity
profile summary using Pierre Samozino and Jean-Benoît Morin method (for
more DOI:10.1111/sms.12490)
get_air_resistance function to estimate air
resistance in newtonspredict_air_resistance_at_time and
predict_air_resistance_at_time functionspredict_force_at_time and
predict_force_at_distance functionspredict_power_at_time and
predict_power_at_distance functionspredict_relative_power_at_distance and
predict_relative_power_at_time use air resistance to do the
calculations. The default bodymass is 75kg. To replicate earlier
function behavior, use predict_velocity_at_ multiplied by
predict_acceleration_at_ to get relative power without air
resistancepredict_kinematics now uses aforementioned
changes in predict_relative_power_ functions, and added
force and air resistance in the outputfind_max_power_distance and
find_power_critical_distance now uses aforementioned
changes and returns absolute power, rather than relative, and air
resistance is used in the power calculationsfind_max_power_time,
find_velocity_critical_time,
find_acceleration_critical_time, and
find_power_critical_time functionsmodel_using_radar_with_time_correction where
time_correction parameter is estimatedmixed_model_using_radar_with_time_correction
where time_correction parameter is estimateddata("vescovi")). For more info see
?vescovitime_correction and
distance_correction to all model parameters output to align
thempredict_kinematics function that predicts 0-6s
distance (100Hz), velocity, acceleration, and relative powerprint, coef,
summary, predict, and residuals
methodsrandom parameter to mixed_ family of
functions to allow higher flexibility in model definitions, but removed
corrections_as_random_effects parameter. The default
behavious is to use all parameters as random effects.... to model_using_radar and
mixed_model_using_radar to be forwarded to
nlme::nlmesprint-corrections vignette by adding a
short simulation studytime_delay to time_correction in
shorts::model_using_instant_velocity and
shorts::mixed_model_using_instant_velocity functions to be
more consistent across functions. Also, this correction is
added to time, so use negative numbers insteadtime_correction in
shorts::mixed_model_using_instant_velocity and
shorts::mixed_model_using_split_times is now numeric
vector, not column nametime_correction in
shorts::model_using_split_times and
shorts::mixed_model_using_split_timestime_correction in
shorts:predict_ family of functionsdistance_correction in
shorts:predict_ family of functionstime_correction in
shorts::model_using_split_times_with_time_correction and
shorts::mixed_model_using_split_times_with_time_correctiondistance_correction in
shorts::model_using_split_times_with_corrections and
shorts::mixed_model_using_split_times_with_correctionsshorts::mixed_model_
functionsmaxAbsErr from model_fit elementfind_ family of functions for finding max
power and critical distance when velocity or acceleration reaches
certain threshold... to all modeling functions so that extra
parameters can be forwarded to the optimization enginesplit_times recreated using distance shift behind
the first timing gate. Needed to demo mixed models with corrections_model_using_instant_velocity to
_model_using_radar_model_using_split_times_ to
_model_using_splits_