extraSuperpower (version 1.5.0)

twoway_simulation_correlated: Simulate measurements repeated over either or both factors of a two-way design

Description

Both regular and internal function. As regular function takes input generated by the calculate_mean_matrix function and iteratively simulates repeated measures two-way factorial experiments. Data are sampled from a normal, skewed normal or truncated normal distribution.

Usage

twoway_simulation_correlated(
  group_size,
  matrices_obj,
  distribution = "normal",
  shape = 0,
  inferior_limit = -Inf,
  superior_limit = Inf,
  balanced = TRUE,
  loss = NULL,
  nsims = 200
)

Value

Dataframe with simulated outcome values, factor level labels and iteration number.

Arguments

group_size

Integer or matrix - Sample size for each group (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 covariance matrices

distribution

Character - Type of distribution from which to sample, possible values are "normal", "skewed" and "truncated"

shape

Vector - Degree of skewness in the distribution. May be a single value, have a length equal to the number of levels of any one of the factors or a length equal to the product of the length of each factor.

inferior_limit

Numeric - Value for which the distribution is truncated on the left. Only valid if distribution="truncated.normal"

superior_limit

Numeric - Value for which the distribution is truncated on the right. Only valid if distribution="truncated.normal"

balanced

Logical - Whether the study will be performed with the same number of subjects in all groups. Default is TRUE. See 'details'.

loss

Character - Type of selection of subjects in groups that have less observations than max(group_size). Possible values are 'random' and 'sequential'. Ignored if balanced=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 repeated measures designs, this function generates a simulation with max(group_size) for all combinations of factors and then eliminates observations. If loss="random" elimination of in those factor combinations that have less participants or study subjects will occur at random. If loss="sequential" the participants or subjects from the groups with less observations will be a subset of participants or subjects of groups with more observations. 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.

Examples

## Repeated measures design, suppose subjects from 4 independent treatment groups
## measured at 5 different timepoints.

refmean <- 1
treatgroups <- 4
timepoints <- 5
treateff <- 1.5
timeeff <- 0.85
rho <- 0.8
withinf <- "fB"
factors_levels_names <- list(treatment=letters[1:treatgroups], time=1:timepoints)

effects_treat_time <- calculate_mean_matrix(refmean = refmean,
                                            fAeffect = treateff, fBeffect = timeeff,
                                            nlfA = treatgroups,  nlfB = timepoints,
                                            rho = rho, withinf = withinf,
                                            label_list = factors_levels_names)

## Inspect plot to check if matrices correspond to design
effects_treat_time$meansplot

n <- 20
repeatedmeasures_experiment <- twoway_simulation_correlated(group_size = n,
                                matrices_obj = effects_treat_time)

head(repeatedmeasures_experiment, 10)