R/estimate-quantile-functions.R
estimate_functions_quantile.Rd
These functions provide estimate 1RM and parameter values using the quantile regression. By default,
target variable is the reps performed, while the predictors is the perc_1RM
or
weight
. To reverse this, use the reverse = TRUE
argument
estimate_k_quantile(
weight,
reps,
eRIR = 0,
tau = 0.5,
reverse = FALSE,
control = quantreg::nlrq.control(maxiter = 10^4, InitialStepSize = 0),
...
)
estimate_kmod_quantile(
weight,
reps,
eRIR = 0,
tau = 0.5,
reverse = FALSE,
control = quantreg::nlrq.control(maxiter = 10^4, InitialStepSize = 0),
...
)
estimate_klin_quantile(
weight,
reps,
eRIR = 0,
tau = 0.5,
reverse = FALSE,
control = quantreg::nlrq.control(maxiter = 10^4, InitialStepSize = 0),
...
)
Weight used
Number of repetitions done
Subjective estimation of reps-in-reserve (eRIR)
Vector of quantiles to be estimated. Default is 0.5
Logical, default is FALSE
. Should reps be used as predictor instead as a target?
Control object for the nlrq
function. Default is:
quantreg::nlrq.control(maxiter = 10^4, InitialStepSize = 0)
Forwarded to nlrq
function
nlrq
object
estimate_k_quantile
: Estimate the parameter k
in the Epley's equation, as well as
1RM
. This is a novel estimation function that uses the absolute weights
estimate_kmod_quantile
: Estimate the parameter kmod
in the modified Epley's equation, as well as
1RM
. This is a novel estimation function that uses the absolute weights
estimate_klin_quantile
: Estimate the parameter klin
in the Linear/Brzycki equation, as well as
1RM
. This is a novel estimation function that uses the absolute weights
# ---------------------------------------------------------
# Epley's model that also estimates 1RM
m1 <- estimate_k_quantile(
weight = c(70, 110, 140),
reps = c(10, 5, 3)
)
coef(m1)
#> k 0RM
#> 0.2499988 245.0003205
# ---------------------------------------------------------
# Modified Epley's model that also estimates 1RM
m1 <- estimate_kmod_quantile(
weight = c(70, 110, 140),
reps = c(10, 5, 3)
)
coef(m1)
#> kmod 1RM
#> 0.1999983 196.0002286
# ---------------------------------------------------------
# Linear/Brzycki model thal also estimates 1RM
m1 <- estimate_klin_quantile(
weight = c(70, 110, 140),
reps = c(10, 5, 3)
)
coef(m1)
#> klin 1RM
#> 16 160