estimate_functions_quantile | R Documentation |
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(
perc_1RM,
reps,
eRIR = 0,
tau = 0.5,
reverse = FALSE,
control = quantreg::nlrq.control(maxiter = 10^4, InitialStepSize = 0),
...
)
estimate_k_generic_1RM_quantile(
weight,
reps,
eRIR = 0,
k = 0.0333,
tau = 0.5,
reverse = FALSE,
control = quantreg::nlrq.control(maxiter = 10^4, InitialStepSize = 0),
...
)
estimate_k_1RM_quantile(
weight,
reps,
eRIR = 0,
tau = 0.5,
reverse = FALSE,
control = quantreg::nlrq.control(maxiter = 10^4, InitialStepSize = 0),
...
)
estimate_kmod_quantile(
perc_1RM,
reps,
eRIR = 0,
tau = 0.5,
reverse = FALSE,
control = quantreg::nlrq.control(maxiter = 10^4, InitialStepSize = 0),
...
)
estimate_kmod_1RM_quantile(
weight,
reps,
eRIR = 0,
tau = 0.5,
reverse = FALSE,
control = quantreg::nlrq.control(maxiter = 10^4, InitialStepSize = 0),
...
)
estimate_klin_quantile(
perc_1RM,
reps,
eRIR = 0,
tau = 0.5,
reverse = FALSE,
control = quantreg::nlrq.control(maxiter = 10^4, InitialStepSize = 0),
...
)
estimate_klin_1RM_quantile(
weight,
reps,
eRIR = 0,
tau = 0.5,
reverse = FALSE,
control = quantreg::nlrq.control(maxiter = 10^4, InitialStepSize = 0),
...
)
perc_1RM |
%1RM |
reps |
Number of repetitions done |
eRIR |
Subjective estimation of reps-in-reserve (eRIR) |
tau |
Vector of quantiles to be estimated. Default is 0.5 |
reverse |
Logical, default is |
control |
Control object for the |
... |
Forwarded to |
weight |
Weight used |
k |
Value for the generic Epley's equation, which is by default equal to 0.0333 |
nlrq
object
estimate_k_quantile()
: Estimate the parameter k
in the Epley's equation
estimate_k_generic_1RM_quantile()
: Provides the model with generic k
parameter, as well as
estimated 1RM
. This is a novel estimation function that uses the absolute weights
estimate_k_1RM_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
estimate_kmod_1RM_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
estimate_klin_1RM_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
m1 <- estimate_k_quantile(
perc_1RM = c(0.7, 0.8, 0.9),
reps = c(10, 5, 3)
)
coef(m1)
# ---------------------------------------------------------
# Epley's model that also estimates 1RM
m1 <- estimate_k_generic_1RM_quantile(
weight = c(70, 110, 140),
reps = c(10, 5, 3)
)
coef(m1)
# ---------------------------------------------------------
# Epley's model that also estimates 1RM
m1 <- estimate_k_1RM_quantile(
weight = c(70, 110, 140),
reps = c(10, 5, 3)
)
coef(m1)
# ---------------------------------------------------------
# Modified Epley's model
m1 <- estimate_kmod_quantile(
perc_1RM = c(0.7, 0.8, 0.9),
reps = c(10, 5, 3)
)
coef(m1)
# ---------------------------------------------------------
# Modified Epley's model that also estimates 1RM
m1 <- estimate_kmod_1RM_quantile(
weight = c(70, 110, 140),
reps = c(10, 5, 3)
)
coef(m1)
# ---------------------------------------------------------
# Linear/Brzycki model
m1 <- estimate_klin_quantile(
perc_1RM = c(0.7, 0.8, 0.9),
reps = c(10, 5, 3)
)
coef(m1)
# ---------------------------------------------------------
# Linear/Brzycki model thal also estimates 1RM
m1 <- estimate_klin_1RM_quantile(
weight = c(70, 110, 140),
reps = c(10, 5, 3)
)
coef(m1)
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