These functions are reverse version of the adj_perc_1RM
family of functions. Use these when you want to estimate number of
repetitions to be used when using the known %1RM and level of
adjustment
adj_reps_RIR(
perc_1RM,
adjustment = 0,
mfactor = 1,
max_reps_func = max_reps_epley,
...
)
adj_reps_DI(
perc_1RM,
adjustment = 1,
mfactor = 1,
max_reps_func = max_reps_epley,
...
)
adj_reps_rel_int(
perc_1RM,
adjustment = 1,
mfactor = 1,
max_reps_func = max_reps_epley,
...
)
adj_reps_perc_MR(
perc_1RM,
adjustment = 1,
mfactor = 1,
max_reps_func = max_reps_epley,
...
)
Numeric vector. %1RM used (use 0.5 for 50%, 0.9 for 90%)
Numeric vector. Adjustment to be implemented
Numeric vector. Default is 1 (i.e., no adjustment).
Use mfactor = 2
to generate ballistic adjustment and tables
Max reps function to be used. Default is max_reps_epley
Forwarded to max_reps_func
. Usually the parameter value.
For example klin = 36
when using max_reps_linear
as
max_reps_func
function
Numeric vector. Predicted number of repetitions to be performed
adj_reps_RIR
: Adjust number of repetitions using the Reps In Reserve (RIR) approach
adj_reps_DI
: Adjust number of repetitions using the Deducted Intensity (DI) approach
adj_reps_rel_int
: Adjust number of repetitions using the Relative Intensity (RelInt) approach
adj_reps_perc_MR
: Adjust number of repetitions using the % max reps (%MR) approach
# ------------------------------------------
# Adjustment using Reps In Reserve (RIR)
adj_reps_RIR(0.75)
#> [1] 10.01001
# Use ballistic adjustment (this implies doing half the reps)
adj_reps_RIR(0.75, mfactor = 2)
#> [1] 5.005005
# Use 2 reps in reserve
adj_reps_RIR(0.75, adjustment = 2)
#> [1] 8.01001
# Use Linear model
adj_reps_RIR(0.75, max_reps_func = max_reps_linear, adjustment = 2)
#> [1] 7.25
# Use Modifed Epley's equation with a custom parameter values
adj_reps_RIR(
0.75,
max_reps_func = max_reps_modified_epley,
adjustment = 2,
kmod = 0.06
)
#> [1] 4.555556
# ------------------------------------------
# Adjustment using Deducted Intensity (DI)
adj_reps_DI(0.75)
#> [1] -150.1502
# Use ballistic adjustment (this implies doing half the reps)
adj_reps_DI(0.75, mfactor = 2)
#> [1] -75.07508
# Use 10% deducted intensity
adj_reps_DI(0.75, adjustment = -0.1)
#> [1] 5.299417
# Use Linear model
adj_reps_DI(0.75, max_reps_func = max_reps_linear, adjustment = -0.1)
#> [1] 5.95
# Use Modifed Epley's equation with a custom parameter values
adj_reps_DI(
0.75,
max_reps_func = max_reps_modified_epley,
adjustment = -0.1,
kmod = 0.06
)
#> [1] 3.941176
# ------------------------------------------
# Adjustment using Relative Intensity (RelInt)
adj_reps_rel_int(0.75)
#> [1] 10.01001
# Use ballistic adjustment (this implies doing half the reps)
adj_reps_rel_int(0.75, mfactor = 2)
#> [1] 5.005005
# Use 85% relative intensity
adj_reps_rel_int(0.75, adjustment = 0.85)
#> [1] 4.004004
# Use Linear model
adj_reps_rel_int(0.75, max_reps_func = max_reps_linear, adjustment = 0.85)
#> [1] 4.882353
# Use Modifed Epley's equation with a custom parameter values
adj_reps_rel_int(
0.75,
max_reps_func = max_reps_modified_epley,
adjustment = 0.85,
kmod = 0.06
)
#> [1] 3.222222
# ------------------------------------------
# Adjustment using % max reps (%MR)
adj_reps_perc_MR(0.75)
#> [1] 10.01001
# Use ballistic adjustment (this implies doing half the reps)
adj_reps_perc_MR(0.75, mfactor = 2)
#> [1] 5.005005
# Use 85% of max reps
adj_reps_perc_MR(0.75, adjustment = 0.85)
#> [1] 8.508509
# Use Linear model
adj_reps_perc_MR(0.75, max_reps_func = max_reps_linear, adjustment = 0.85)
#> [1] 7.8625
# Use Modifed Epley's equation with a custom parameter values
adj_reps_perc_MR(
0.75,
max_reps_func = max_reps_modified_epley,
adjustment = 0.85,
kmod = 0.06
)
#> [1] 5.572222