Performs critical power estimations based on the methods chosen.
critical_power( .data, power_output_column = "PO", time_to_exhaustion_column = "TTE", method = c("3-hyp", "2-hyp", "linear", "1/time"), plot = TRUE, reverse_y_axis = FALSE, all_combinations = FALSE )
A data frame containing a power output column and their respective time-to-exhaustion.
The name of the power output column. This value has to be in watts.
The name of the time-to-exhaustion column. This value has to be in seconds. Default to
The method for estimating critical power. It can be one or multiple methods. Default to
A boolean indicating whether to produce a plot from the critical power estimation. Default to
A boolean to indicate whether to plot the Power Output (W) in the y-axis. It is ignored for the linear methods. Default to
A boolean indicating whether to perform the critical power estimation from all the possible combinations of time-to-exhaustion trials provided. Please, see 'Details' for more information. Default to
a tibble containing the following columns:
all_combinations = TRUE this column indicates the row number of the trials chosen for the critical power estimation. They correspond to the row numbers in your data.
The critical power method for that estimation.
The data you provided.
lm object. The model used in the critical power fitting.
Critical power, in watts.
Standard error of the estimation of critical power, in watts.
Anaerobic work capacity, in joules.
Standard error of the estimation of anaerobic work capacity, in joules.
Maximal instantaneous power, in watts (only available in CP 3-hyp).
Standard error of the estimation of maximal instantaneous power, in watts (only available in CP 3-hyp).
Root mean square error, the units vary depending on the method, as they represent the units of the response (i.e., y-axis): CP3-hyp in seconds, CP2-hyp in seconds, CPlinear in joules, and CP1/time in watts.
The critical power plot.
Please, note that estimations of critical power are highly influenced by the range of time-to-exhaustion trials performed, and the chosen critical power model. Please, see Mattioni Maturana et al. (2018).
A note on the R2 value: Please, note that for the hyperbolic fits (CP 3-hyp and CP 2-hyp) this value is actually a pseudo r-squared, as they are not linear models.
The 3-parameter hyperbolic model is calculated as the following (Morton, 1996):
The 2-parameter hyperbolic model is calculated as the following (Hill, 1993):
The linear model is calculated as the following (Moritani et al. 1981):
The 1/time linear model is calculated as the following (Whipp et al. 1982):
When the argument
all_combinations = TRUE, the function takes the given data and finds all the
possible combinations of the trials given. For example, if 5 trials are provided (i.e., your data contains 5 rows),
critical power will be fitted with all the 5 trials, then all the possible combinations using only 4 trials, and so on.
This feature was inspired on our paper Mattioni Maturana et al. (2018).
When all the combinations are fitted, an extra column called "index" appears in the final results.
This is the index of the trial number used to fit the model.
This corresponds to the row number of your data (e.g., in
[1,3,5] trials #1, #3, and #5 are used).
Mattioni Maturana, F., Fontana, F. Y., Pogliaghi, S., Passfield, L., & Murias, J. M. (2018). Critical power: How different protocols and models affect its determination. Journal of Science and Medicine in Sport, 21(7), 742-747.
Hugh Morton, R. (1996). A 3-parameter critical power model. Ergonomics, 39(4), 611-619.
Hill, D. W. (1993). The critical power concept. Sports medicine, 16(4), 237-254.
Moritani, T., NAGATA, A., DEVRIES, H. A., & MURO, M. (1981). Critical power as a measure of physical work capacity and anaerobic threshold. Ergonomics, 24(5), 339-350.
Whipp, B. J., Huntsman, D. J., Storer, T. W., Lamarra, N., & Wasserman, K. (1982, January). A constant which determines the duration of tolerance to high-intensity work. In Federation proceedings (Vol. 41, No. 5, pp. 1591-1591). 9650 ROCKVILLE PIKE, BETHESDA, MD 20814-3998: FEDERATION AMER SOC EXP BIOL.
results <- critical_power( .data = demo_critical_power, power_output_column = "PO", time_to_exhaustion_column = "TTE", method = c("3-hyp", "2-hyp", "linear", "1/time"), plot = TRUE, all_combinations = FALSE ) results#> # A tibble: 4 x 12 #> method data model CP `CP SEE` `W'` `W' SEE` Pmax `Pmax SEE` R2 #> <chr> <lis> <lis> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 3-hyp <tib… <nls> 260. 3.1 27410. 4794 1004. 835. 0.998 #> 2 2-hyp <tib… <nls> 262. 1.6 24174. 1889. NA NA 0.997 #> 3 linear <tib… <lm> 266. 3 20961. 2248. NA NA 1.00 #> 4 1/time <tib… <lm> 274. 6.2 17784. 1160 NA NA 0.987 #> # … with 2 more variables: RMSE <dbl>, plot <list>