twoway_simulation_independent: Simulate independent measurements in a two-way factorial...

In extraSuperpower: Power Calculation for Two-Way Factorial Designs

Simulate independent measurements in a two-way factorial design

Description

Both regular and internal function. As regular function takes input generated by the calculate_mean_matrix function and iteratively simulates independent measures two-way factorial experiments. Outcome may be normally distributed, have a skewed normal distribution or a truncated normal distribution.

Usage

twoway_simulation_independent(
  group_size,
  matrices_obj,
  distribution = "normal",
  skewness = 1,
  inferior_limit = -Inf,
  superior_limit = Inf,
  balanced = TRUE,
  nsims = 200
)

Arguments

group_size	Integer or matrix - Sample size for each condition (combination of factor levels). If balanced=TRUE (default) group_size must be an integer. If balanced=FALSE group_size must be a matrix.
matrices_obj	List - Output generated by calculate_mean_matrix that include cell mean and standard deviation matrices.
distribution	Character - Type of distribution to simulate. Possible values are 'normal', 'skewed' or 'truncated.normal'.
skewness	Numeric - Momentum of distribution skewness
inferior_limit	Numeric - Value of the lower bound for the truncated distribution, defaults to '-Inf'. Ignored if distribution is either 'normal' or 'skewed'.
superior_limit	Numeric - Value of the upper bound for the truncated distribution, defaults to 'Inf'. Ignored if distribution is either 'normal' or 'skewed'.
balanced	Logical - Whether the study will be performed with the same number of subjects in all groups. Default is TRUE. See 'details'.
nsims	Integer - Number of iterations.

Details

As internal function runs with a single iteration inside graph_twoway_assumptions, which in itself is inside calculate_mean_matrix to generate data for the cell mean and standard deviation plot.

For unbalanced independent measures designs, this function generates a simulation with max(group_size) for all factors combinations and then eliminates observations at random in those factor combinations that have less participants or study subjects. This may not sound like the most efficient way to proceed, is quite fast anyhow. The 'n' column in the output will reflect how many observations each factor combination has. This should match the input matrix.

Value

data.frame with modeled outcome values, factor level labels, iteration number and sample size.

Examples

refmean <- 1
treatgroups <- 4
timepoints <- 5
treateff <- 1.5
timeeff <- 0.85
factors_levels_names <- list(treatment=letters[1:treatgroups], time=1:timepoints)

## Independent design
effects_treat_time <- calculate_mean_matrix(refmean = refmean,
                                            fAeffect = treateff, fBeffect = timeeff,
                                            nlfA = treatgroups, nlfB = timepoints,
                                            label_list = factors_levels_names)
## Inspect plot to check if matrices correspond to design
n <- 20
independent_experiment <- twoway_simulation_independent(group_size = n,
                                                        matrices_obj = effects_treat_time)
head(independent_experiment, 10)

extraSuperpower documentation built on Aug. 8, 2025, 6:44 p.m.