estimate_functions_mixed {STMr}

Estimate relationship between reps and weight using the non-linear mixed-effects regression

Description

These functions provide estimated 1RM and parameter values using the mixed-effect regression. By default, target variable is the reps performed, while the predictor is the perc_1RM or weight. To reverse this, use the reverse = TRUE argument

Usage

estimate_k_mixed(athlete, perc_1RM, reps, eRIR = 0, reverse = FALSE, ...)

estimate_k_generic_1RM_mixed(
  athlete,
  weight,
  reps,
  eRIR = 0,
  k = 0.0333,
  reverse = FALSE,
  random = zeroRM ~ 1,
  ...
)

estimate_k_1RM_mixed(
  athlete,
  weight,
  reps,
  eRIR = 0,
  reverse = FALSE,
  random = k + zeroRM ~ 1,
  ...
)

estimate_kmod_mixed(athlete, perc_1RM, reps, eRIR = 0, reverse = FALSE, ...)

estimate_kmod_1RM_mixed(
  athlete,
  weight,
  reps,
  eRIR = 0,
  reverse = FALSE,
  random = kmod + oneRM ~ 1,
  ...
)

estimate_klin_mixed(athlete, perc_1RM, reps, eRIR = 0, reverse = FALSE, ...)

estimate_klin_1RM_mixed(
  athlete,
  weight,
  reps,
  eRIR = 0,
  reverse = FALSE,
  random = klin + oneRM ~ 1,
  ...
)

Arguments

athlete

Athlete identifier

perc_1RM

%1RM

reps

Number of repetitions done

eRIR

Subjective estimation of reps-in-reserve (eRIR)

reverse

Logical, default is FALSE. Should reps be used as predictor instead as a target?

...

Forwarded to nlme function

weight

Weight used

k

Value for the generic Epley's equation, which is by default equal to 0.0333

random

Random parameter forwarded to nlme function. Default is k + zeroRM ~ 1 for, estimate_k_mixed function, or k + oneRM ~ 1 for estimate_kmod_mixed and estimate_klin_mixed functions

Value

nlme object

Functions

• estimate_k_mixed(): Estimate the parameter k in the Epley's equation

• estimate_k_generic_1RM_mixed(): 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_mixed(): 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_mixed(): Estimate the parameter kmod in the Modified Epley's equation

• estimate_kmod_1RM_mixed(): 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_mixed(): Estimate the parameter klin in the Linear/Brzycki's equation

• estimate_klin_1RM_mixed(): Estimate the parameter klin in the Linear/Brzycki equation, as well as 1RM. This is a novel estimation function that uses the absolute weights

Examples

# ---------------------------------------------------------
# Epley's model
m1 <- estimate_k_mixed(
  athlete = RTF_testing$Athlete,
  perc_1RM = RTF_testing$`Real %1RM`,
  reps = RTF_testing$nRM
)

coef(m1)
# ---------------------------------------------------------
# Generic Epley's model that also estimates 1RM
m1 <- estimate_k_generic_1RM_mixed(
  athlete = RTF_testing$Athlete,
  weight = RTF_testing$`Real Weight`,
  reps = RTF_testing$nRM
)

coef(m1)
# ---------------------------------------------------------
# Epley's model that also estimates 1RM
m1 <- estimate_k_1RM_mixed(
  athlete = RTF_testing$Athlete,
  weight = RTF_testing$`Real Weight`,
  reps = RTF_testing$nRM
)

coef(m1)
# ---------------------------------------------------------
# Modifed Epley's model
m1 <- estimate_kmod_mixed(
  athlete = RTF_testing$Athlete,
  perc_1RM = RTF_testing$`Real %1RM`,
  reps = RTF_testing$nRM
)

coef(m1)
# ---------------------------------------------------------
# Modified Epley's model that also estimates 1RM
m1 <- estimate_kmod_1RM_mixed(
  athlete = RTF_testing$Athlete,
  weight = RTF_testing$`Real Weight`,
  reps = RTF_testing$nRM
)

coef(m1)
# ---------------------------------------------------------
# Linear/Brzycki model
m1 <- estimate_klin_mixed(
  athlete = RTF_testing$Athlete,
  perc_1RM = RTF_testing$`Real %1RM`,
  reps = RTF_testing$nRM
)

coef(m1)
# ---------------------------------------------------------
# Linear/Brzycki model that also estimates 1RM
m1 <- estimate_klin_1RM_mixed(
  athlete = RTF_testing$Athlete,
  weight = RTF_testing$`Real Weight`,
  reps = RTF_testing$nRM
)

coef(m1)