calculate_mean_matrix: Create input for simulation based two-way factorial...

In extraSuperpower: Power Calculation for Two-Way Factorial Designs

Create input for simulation based two-way factorial experiments

Description

This function will generate a matrix of expected mean values for ab factor level combinations of a two-way factorial design by assuming linear effects with possible departure from linearity by interaction. It will also provide a standard deviation matrix for these ab combinations of factor levels. If the design has repeated measures, it will additionally provide correlation and covariance matrices calculated depending on which factor has repeated measurements or is the 'within' factor.

Usage

calculate_mean_matrix(
  refmean,
  nlfA,
  nlfB,
  fAeffect,
  fBeffect,
  groupswinteraction = NULL,
  interact = 1,
  label_list = NULL,
  sdproportional = TRUE,
  sdratio = 0.2,
  endincrement = TRUE,
  rho = 0,
  withinf = NULL,
  plot = TRUE
)

Arguments

refmean	Numeric - expected mean for first level of both factors A and B
nlfA	Integer - number of levels of factor A
nlfB	integer - number of levels of factor B
fAeffect	Numeric - multiple that defines how cell means are modified by factor A. With the default endincrement (TRUE), determines the last level of factor A with respect to its first level. When endincrement=FALSE this multiple applies from one level to the next.
fBeffect	Numeric - multiple that defines how cell means are modified by factor B. With the default endincrement (TRUE), determines the last level of factor B with respect to its first level. When endincrement=FALSE this multiple applies from one level to the next.
groupswinteraction	vector length 2 or n*2 matrix - Combination of levels from factors A and B in which interaction is expected
interact	Numeric - value by which the mean from cell or cells indicated in groupswinteraction is multiplied after it has been calculated accordingly to fAeffect and fBeffect
label_list	List length 2 - vectors with the names of the factor levels. The objects in this list should be named as the factors. The use of this option is encouraged as these names are used for plotting and inherited to downstream functions.
sdproportional	Logical - whether the standard deviation for each combination of factor levels is a proportion of the respective factor level combination mean, defaults to TRUE
sdratio	Numeric - value by which the expected mean value of a factor level combination is multiplied to obtain the respective standard deviation, defaults to 0.2.
endincrement	Logical - determines if the multiples provided in fAeffect and fBeffect refer to change between first and last levels (default) or level to level changes.
rho	Vector length 1 or 2, or 2 by 2 matrix - Controls how the correlation and hence de covariance matrix is built. See 'details' and ?gencorrelationmat examples.
withinf	Character - Names the factor with repeated measures. Possible values are NULL, "fA", "fB" or "both"
plot	Logical - Should a line plot with the modeled mean and standard deviations be part of the output. Default is TRUE

Details

The user must provide a reference mean (usually mean in control or untreated group), the expected change for each factor from first to last level (or from one level to the next) and the number of levels in each factor.

The user can also specify factor level combinations in which interaction is assumed and its magnitude with respect to the reference mean. The cell mean matrix will be modified accordingly and this can also have an effect of the standard deviation matrix.

We were motivated by sample size calculation for two-way factorial designs with 1,2,...,a levels of factor A and 1,2,...,b levels of factor B in which the mean outcome value for replicates of cell A=1, B=1 are known. Furthermore, there is an expected change in level mean for each of the factors. Finally, interaction can be explicitly introduced to level combinations in which it is expected to occur.

If a repeated measures experiment is intended withinf must be set to "fA", "fB" or "both", depending on which is the 'within' factor. If rho is a vector length 1, the within subject correlation will be constant for the factor defined in withinf. If rho is a vector length 2 and withinf is either "fA" or "fB" a correlation gradient will be created from the first to second value of rho. If rho is a vector length 2 and withinf="both", the first element of rho will be the correlation within factor A, while the second element will be the correlation within factor B. If rho is a 2*2 matrix, only possible if withinf="both", a correlation gradient will be created across rows of rho for each of the factors.

Value

If rho and withinf are left at their default values of 0 and NULL, respectively, a cell mean matrix, a cell standard deviation matrix and optionally a graph that represents both.

If rho is between -1 and 1 but different to 0 and withinf is either "fA", "fB" or "both", correlation and covariance matrices are generated along with the aforementioned output.

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 <- 20
independent_experiment <- twoway_simulation_independent(group_size = n,
                                      matrices_obj = effects_treat_time)

head(independent_experiment, 10)

## Repeated measures design, suppose subjects from 4 independent treatment groups measured
## at 5 different timepoints.
## We use the same parameters as the independent design example, except we add within factor level
## correlation and we specify that factor B is the within factor.


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)

## Plot should look the same, structure within data can be checked once simulated
effects_treat_time$meansplot

n <- 20
repeatedmeasures_experiment <- twoway_simulation_correlated(group_size = n,
                                          matrices_obj = effects_treat_time)
head(repeatedmeasures_experiment, 10)

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