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_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

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_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$nRM ) coef(m1) #> k #> Athlete A 0.01937865 #> Athlete B 0.03403605 #> Athlete C 0.06747237 #> Athlete D 0.02754989 #> Athlete E 0.04001677 #> Athlete F 0.02442086 #> Athlete G 0.03584091 #> Athlete H 0.02952992 #> Athlete I 0.02172886 #> Athlete J 0.04741195 #> Athlete K 0.05844494 #> Athlete L 0.03987504 # --------------------------------------------------------- # Epley's model that also estimates 1RM m1 <- estimate_k_1RM_mixed( athlete = RTF_testing$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 0.05145821 112.78560
#> Athlete D 0.02931850 106.70531
#> Athlete E 0.04598449 114.11873
#> 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 0.04190880  89.90771
#> Athlete K 0.05472578 101.01462
#> Athlete L 0.03821827 138.52860
# ---------------------------------------------------------
# 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 0.03015280 109.90566
#> Athlete B 0.03302730  91.99510
#> Athlete C 0.03135538 102.41450
#> Athlete D 0.03086468 105.47095
#> Athlete E 0.03133877 102.51788
#> Athlete F 0.03261365  94.57202
#> Athlete G 0.03213298  97.56803
#> Athlete H 0.02771242 125.11420
#> Athlete I 0.02956076 113.59511
#> Athlete J 0.03421891  84.57019
#> Athlete K 0.03325603  90.57081
#> Athlete L 0.02747210 126.61253
# ---------------------------------------------------------
# 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 34.37575  84.98052
#> Athlete K 24.84090  95.45042
#> Athlete L 39.44232 130.14431