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

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)
#>                    k
#> Athlete A 0.01937865
#> Athlete B 0.03403605
#> Athlete C 0.06747237
#> Athlete D 0.02754989
#> Athlete E 0.04001677
#> Athlete F 0.02442086
#> Athlete G 0.03584091
#> Athlete H 0.02952992
#> Athlete I 0.02172886
#> Athlete J 0.04741195
#> Athlete K 0.05844494
#> Athlete L 0.03987504
# ---------------------------------------------------------
# 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)
#>              zeroRM
#> Athlete A 115.60573
#> Athlete B  94.61033
#> Athlete C 106.24996
#> Athlete D 110.00999
#> Athlete E 106.53626
#> Athlete F  97.34956
#> Athlete G 100.87702
#> Athlete H 133.10815
#> Athlete I 119.93968
#> Athlete J  86.68878
#> Athlete K  93.39298
#> Athlete L 133.96571
# ---------------------------------------------------------
# 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
)
#> Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)

coef(m1)
#>                    k    zeroRM
#> Athlete A 0.02009568 100.90930
#> Athlete B 0.03190396  93.57043
#> Athlete C 0.05145831 112.78563
#> Athlete D 0.02931850 106.70531
#> Athlete E 0.04598452 114.11875
#> Athlete F 0.02445762  90.03293
#> Athlete G 0.03320945 100.69127
#> Athlete H 0.03081954 131.42121
#> Athlete I 0.02224391 108.13537
#> Athlete J 0.04190882  89.90772
#> Athlete K 0.05472608 101.01473
#> Athlete L 0.03821828 138.52861
# ---------------------------------------------------------
# Modifed Epley's model
m1 <- estimate_kmod_mixed(
  athlete = RTF_testing$Athlete,
  perc_1RM = RTF_testing$`Real %1RM`,
  reps = RTF_testing$nRM
)

coef(m1)
#>                 kmod
#> Athlete A 0.02061064
#> Athlete B 0.03816219
#> Athlete C 0.07955734
#> Athlete D 0.03001988
#> Athlete E 0.04559265
#> Athlete F 0.02638650
#> Athlete G 0.04044088
#> Athlete H 0.03240645
#> Athlete I 0.02325264
#> Athlete J 0.05521785
#> Athlete K 0.06915300
#> Athlete L 0.04531026
# ---------------------------------------------------------
# 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
)
#> Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)

coef(m1)
#>                 kmod     oneRM
#> Athlete A 0.03015293 109.90580
#> Athlete B 0.03302727  91.99509
#> Athlete C 0.03135532 102.41449
#> Athlete D 0.03086472 105.47098
#> Athlete E 0.03133872 102.51785
#> Athlete F 0.03261368  94.57204
#> Athlete G 0.03213297  97.56802
#> Athlete H 0.02771251 125.11427
#> Athlete I 0.02956090 113.59524
#> Athlete J 0.03421882  84.57016
#> Athlete K 0.03325593  90.57077
#> Athlete L 0.02747214 126.61255
# ---------------------------------------------------------
# Linear/Brzycki model
m1 <- estimate_klin_mixed(
  athlete = RTF_testing$Athlete,
  perc_1RM = RTF_testing$`Real %1RM`,
  reps = RTF_testing$nRM
)

coef(m1)
#>               klin
#> Athlete A 64.11011
#> Athlete B 35.11002
#> Athlete C 16.22552
#> Athlete D 45.24156
#> Athlete E 30.05447
#> Athlete F 50.70299
#> Athlete G 33.20719
#> Athlete H 41.90200
#> Athlete I 57.35190
#> Athlete J 24.65495
#> Athlete K 19.43184
#> Athlete L 30.18074
# ---------------------------------------------------------
# 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
)
#> Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)

coef(m1)
#>               klin     oneRM
#> Athlete A 75.15769  96.19463
#> Athlete B 45.44975  88.91455
#> Athlete C 25.33309 107.11406
#> Athlete D 53.32681 100.49170
#> Athlete E 33.89090 106.06520
#> Athlete F 62.45929  85.33938
#> Athlete G 43.19576  95.83903
#> Athlete H 50.16078 123.79762
#> Athlete I 67.24280 103.20196
#> Athlete J 34.37574  84.98052
#> Athlete K 24.84090  95.45042
#> Athlete L 39.44231 130.14431