set_and_reps_schemes: Set and Rep Schemes

In STMr: Strength Training Manual R-Language Functions

Set and Rep Schemes

Description

Set and Rep Schemes

Usage

scheme_generic(
  reps,
  adjustment,
  vertical_planning,
  vertical_planning_control = list(),
  progression_table,
  progression_table_control = list()
)

scheme_wave(
  reps = c(10, 8, 6),
  adjustment = -rev((seq_along(reps) - 1) * 5)/100,
  vertical_planning = vertical_constant,
  vertical_planning_control = list(),
  progression_table = progression_perc_drop,
  progression_table_control = list(volume = "normal")
)

scheme_plateau(
  reps = c(5, 5, 5),
  vertical_planning = vertical_constant,
  vertical_planning_control = list(),
  progression_table = progression_perc_drop,
  progression_table_control = list(volume = "normal")
)

scheme_step(
  reps = c(5, 5, 5),
  adjustment = -rev((seq_along(reps) - 1) * 10)/100,
  vertical_planning = vertical_constant,
  vertical_planning_control = list(),
  progression_table = progression_perc_drop,
  progression_table_control = list(volume = "intensive")
)

scheme_step_reverse(
  reps = c(5, 5, 5),
  adjustment = -((seq_along(reps) - 1) * 10)/100,
  vertical_planning = vertical_constant,
  vertical_planning_control = list(),
  progression_table = progression_perc_drop,
  progression_table_control = list(volume = "intensive")
)

scheme_wave_descending(
  reps = c(6, 8, 10),
  adjustment = -rev((seq_along(reps) - 1) * 5)/100,
  vertical_planning = vertical_constant,
  vertical_planning_control = list(),
  progression_table = progression_perc_drop,
  progression_table_control = list(volume = "normal")
)

scheme_light_heavy(
  reps = c(10, 5, 10, 5),
  adjustment = c(-0.1, 0)[(seq_along(reps)%%2) + 1],
  vertical_planning = vertical_constant,
  vertical_planning_control = list(),
  progression_table = progression_perc_drop,
  progression_table_control = list(volume = "normal")
)

scheme_pyramid(
  reps = c(12, 10, 8, 10, 12),
  adjustment = 0,
  vertical_planning = vertical_constant,
  vertical_planning_control = list(),
  progression_table = progression_perc_drop,
  progression_table_control = list(volume = "extensive")
)

scheme_pyramid_reverse(
  reps = c(8, 10, 12, 10, 8),
  adjustment = 0,
  vertical_planning = vertical_constant,
  vertical_planning_control = list(),
  progression_table = progression_perc_drop,
  progression_table_control = list(volume = "extensive")
)

scheme_rep_acc(
  reps = c(10, 10, 10),
  adjustment = 0,
  vertical_planning_control = list(step = rep(0, 4)),
  progression_table = progression_perc_drop,
  progression_table_control = list(volume = "normal")
)

scheme_ladder(
  reps = c(3, 5, 10),
  adjustment = 0,
  vertical_planning = vertical_constant,
  vertical_planning_control = list(),
  progression_table = progression_perc_drop,
  progression_table_control = list(volume = "normal")
)

scheme_manual(
  index = NULL,
  step,
  sets = 1,
  reps,
  adjustment = 0,
  perc_1RM = NULL,
  progression_table = progression_perc_drop,
  progression_table_control = list(volume = "normal")
)

scheme_perc_1RM(reps = c(5, 5, 5), perc_1RM = c(0.4, 0.5, 0.6), n_steps = 4)

Arguments

reps	Numeric vector indicating reps prescription
adjustment	Numeric vector indicating adjustments. Forwarded to progression_table.
vertical_planning	Vertical planning function. Default is vertical_constant
vertical_planning_control	Arguments forwarded to the vertical_planning function
progression_table	Progression table function. Default is progression_perc_drop
progression_table_control	Arguments forwarded to the progression_table function
index	Numeric vector. If not provided, index will be create using sequence of step
step	Numeric vector
sets	Numeric vector. Used to replicate reps and adjustments
perc_1RM	Numeric vector of user provided 1RM percentage
n_steps	How many progression steps to generate? Default is 4

Value

Data frame with the following columns: reps, index, step, adjustment, and perc_1RM.

Functions

• scheme_generic(): Generic set and rep scheme. scheme_generic is called in all other set and rep schemes - only the default parameters differ to make easier and quicker schemes writing and groupings

• scheme_wave(): Wave set and rep scheme

• scheme_plateau(): Plateau set and rep scheme

• scheme_step(): Step set and rep scheme

• scheme_step_reverse(): Reverse Step set and rep scheme

• scheme_wave_descending(): Descending Wave set and rep scheme

• scheme_light_heavy(): Light-Heavy set and rep scheme. Please note that the adjustment column in the output will be wrong, hence set to NA

• scheme_pyramid(): Pyramid set and rep scheme

• scheme_pyramid_reverse(): Reverse Pyramid set and rep scheme

• scheme_rep_acc(): Rep Accumulation set and rep scheme

• scheme_ladder(): Ladder set and rep scheme. Please note that the adjustment column in the output will be wrong, hence set to NA

• scheme_manual(): Manual set and rep scheme

• scheme_perc_1RM(): Manual %1RM set and rep scheme

Examples

scheme_generic(
  reps = c(8, 6, 4, 8, 6, 4),
  # Adjusting using lower %1RM (RIR Increment method used)
  adjustment = c(4, 2, 0, 6, 4, 2),
  vertical_planning = vertical_linear,
  vertical_planning_control = list(reps_change = c(0, -2, -4)),
  progression_table = progression_RIR_increment,
  progression_table_control = list(volume = "extensive")
)

# Wave set and rep schemes
# --------------------------
scheme_wave()

scheme_wave(
  reps = c(8, 6, 4, 8, 6, 4),
  # Second wave with higher intensity
  adjustment = c(-0.25, -0.15, 0.05, -0.2, -0.1, 0),
  vertical_planning = vertical_block,
  progression_table = progression_perc_drop,
  progression_table_control = list(type = "ballistic")
)

# Adjusted second wave
# and using 3 steps progression
scheme_wave(
  reps = c(8, 6, 4, 8, 6, 4),
  # Adjusting using lower %1RM (progression_perc_drop method used)
  adjustment = c(0, 0, 0, -0.1, -0.1, -0.1),
  vertical_planning = vertical_linear,
  vertical_planning_control = list(reps_change = c(0, -2, -4)),
  progression_table = progression_perc_drop,
  progression_table_control = list(volume = "extensive")
)

# Adjusted using RIR inc
# This time we adjust first wave as well, first two sets easier
scheme <- scheme_wave(
  reps = c(8, 6, 4, 8, 6, 4),
  # Adjusting using lower %1RM (RIR Increment method used)
  adjustment = c(4, 2, 0, 6, 4, 2),
  vertical_planning = vertical_linear,
  vertical_planning_control = list(reps_change = c(0, -2, -4)),
  progression_table = progression_RIR_increment,
  progression_table_control = list(volume = "extensive")
)
plot(scheme)

# Plateau set and rep schemes
# --------------------------
scheme_plateau()

scheme <- scheme_plateau(
  reps = c(3, 3, 3),
  progression_table_control = list(type = "ballistic")
)
plot(scheme)

# Step set and rep schemes
# --------------------------
scheme_step()

scheme <- scheme_step(
  reps = c(2, 2, 2),
  adjustment = c(-0.1, -0.05, 0),
  vertical_planning = vertical_linear_reverse,
  progression_table_control = list(type = "ballistic")
)
plot(scheme)

# Reverse Step set and rep schemes
#- -------------------------
scheme <- scheme_step_reverse()
plot(scheme)

# Descending Wave set and rep schemes
# --------------------------
scheme <- scheme_wave_descending()
plot(scheme)

# Light-Heavy set and rep schemes
# --------------------------
scheme <- scheme_light_heavy()
plot(scheme)

# Pyramid set and rep schemes
# --------------------------
scheme <- scheme_pyramid()
plot(scheme)

# Reverse Pyramid set and rep schemes
# --------------------------
scheme <- scheme_pyramid_reverse()
plot(scheme)

# Rep Accumulation set and rep schemes
# --------------------------
scheme_rep_acc()

# Generate Wave scheme with rep accumulation vertical progression
# This functions doesn't allow you to use different vertical planning
# options
scheme <- scheme_rep_acc(reps = c(10, 8, 6), adjustment = c(-0.1, -0.05, 0))
plot(scheme)

# Other options is to use `.vertical_rep_accumulation.post()` and
# apply it after
# The default vertical progression is `vertical_const()`
scheme <- scheme_wave(reps = c(10, 8, 6), adjustment = c(-0.1, -0.05, 0))

.vertical_rep_accumulation.post(scheme)

# We can also create "undulating" rep decrements
.vertical_rep_accumulation.post(
  scheme,
  rep_decrement = c(-3, -1, -2, 0)
)

# `scheme_rep_acc` will not allow you to generate `scheme_ladder()`
# and `scheme_scheme_light_heavy()`
# You must use `.vertical_rep_accumulation.post()` to do so
scheme <- scheme_ladder()
scheme <- .vertical_rep_accumulation.post(scheme)
plot(scheme)

# Please note that reps < 1 are removed. If you do not want this,
# use `remove_reps = FALSE` parameter
scheme <- scheme_ladder()
scheme <- .vertical_rep_accumulation.post(scheme, remove_reps = FALSE)
plot(scheme)

# Ladder set and rep schemes
# --------------------------
scheme <- scheme_ladder()
plot(scheme)

# Manual set and rep schemes
# --------------------------
scheme_df <- data.frame(
  index = 1, # Use this just as an example
  step = c(-3, -2, -1, 0),
  # Sets are just an easy way to repeat reps and adjustment
  sets = c(5, 4, 3, 2),
  reps = c(5, 4, 3, 2),
  adjustment = 0
)

# Step index is estimated to be sequences of steps
# If you want specific indexes, use it as an argument (see next example)
scheme <- scheme_manual(
  step = scheme_df$step,
  sets = scheme_df$sets,
  reps = scheme_df$reps,
  adjustment = scheme_df$adjustment
)

plot(scheme)

# Here we are going to provide our own index
scheme <- scheme_manual(
  index = scheme_df$index,
  step = scheme_df$step,
  sets = scheme_df$sets,
  reps = scheme_df$reps,
  adjustment = scheme_df$adjustment
)

plot(scheme)

# More complicated example
scheme_df <- data.frame(
  step = c(-3, -3, -3, -3, -2, -2, -2, -1, -1, 0),
  sets = 1,
  reps = c(5, 5, 5, 5, 3, 2, 1, 2, 1, 1),
  adjustment = c(0, -0.05, -0.1, -0.15, -0.1, -0.05, 0, -0.1, 0, 0)
)

scheme_df

scheme <- scheme_manual(
  step = scheme_df$step,
  sets = scheme_df$sets,
  reps = scheme_df$reps,
  adjustment = scheme_df$adjustment,

  # Select another progression table
  progression_table = progression_DI,
  # Extra parameters for the progression table
  progression_table_control = list(
    volume = "extensive",
    type = "ballistic",
    max_perc_1RM_func = max_perc_1RM_linear,
    klin = 36
  )
)

plot(scheme)

# Provide %1RM manually

scheme_df <- data.frame(
  index = rep(c(1, 2, 3, 4), each = 3),
  reps = rep(c(5, 5, 5), 4),
  perc_1RM = rep(c(0.4, 0.5, 0.6), 4)
)

warmup_scheme <- scheme_manual(
  index = scheme_df$index,
  reps = scheme_df$reps,
  perc_1RM = scheme_df$perc_1RM
)

plot(warmup_scheme)
# Manual %1RM set and rep schemes
# --------------------------
warmup_scheme <- scheme_perc_1RM(
  reps = c(10, 8, 6),
  perc_1RM = c(0.4, 0.5, 0.6),
  n_steps = 3
)

plot(warmup_scheme)

STMr documentation built on Nov. 2, 2023, 5:20 p.m.