extraSuperpower (version 1.5.0)

simulate_twoway_nrange: Simulated independent and repeated measures two-way experiments over a set of sample sizes

Description

Wrapper for both independent and repeated measures two-way simulations. A vector of defined sample sizes is simulated under the model provided.

Usage

simulate_twoway_nrange(
  matrices_obj,
  nset,
  balanced = TRUE,
  group_size = NULL,
  loss = NULL,
  repeated_measurements = FALSE,
  distribution = "normal",
  skewness = 1,
  shape = 0,
  inferior_limit = -Inf,
  superior_limit = Inf,
  nsims = 200
)

Value

List with of data.frames of simulated outcome values under different sample sizes. Each data.frame includes factor level labels, iteration number and sample size.

Arguments

matrices_obj

List - Output generated by calculate_mean_matrix that include cell mean and standard deviation matrices

nset

Vector - If default values are used for both balanced and group_size, sample sizes to be used in simulations. If balanced="FALSE" and a matrix is provided to group_size, number to add to all elements of group_size.

balanced

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

group_size

Matrix - Sample size for each condition (combination of factor levels). Only to be used when balanced=FALSE.

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 repeated_measurements=FALSE or balanced=TRUE. See 'details'.

repeated_measurements

Logical - Does the design have repeated measurements. Default is false.

distribution

Character - Type of distribution to simulate. Possible values are 'normal', 'truncated.normal' or 'skewed'.

skewness

Numeric - Momentum of distribution skewness, univariate distribution simulation.

shape

Numeric - 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. For multivariate distribution simulations.

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".

nsims

Integer - Number of iterations

Details

For unbalanced independent measures designs, this function generates a simulation with 'max(group_size)' for all combinations of factors and then eliminates observations at random in those factor combinations that have less participants or study subjects. This is also the behavior for unbalanced repeated measures designs when loss="random".

For unbalanced repeated measures designs in which 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. The elimination strategy 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

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
effects_treat_time$meansplot
n <- seq(from = 16, to = 24, by = 2)

## In this case, the default 'repeated_measurements', 'distribution' and options are used.
indep_simulation <- simulate_twoway_nrange(effects_treat_time, n)

## Simulate from a truncated distribution
indep_simulation_trunc <- simulate_twoway_nrange(matrices_obj = effects_treat_time, nset = n,
                          distribution="truncated.normal", inferior_limit= 0.8)

##randomly select iteration, select a condition
k <- sample(1:max(indep_simulation_trunc[[1]]$iteration), 1)
toviewdist <- indep_simulation_trunc[[1]]
toviewdist <- subset(toviewdist, iteration==k)
toviewdist <- subset(toviewdist, cond=="V6")
hist(toviewdist$y)