perform_exp: Perform an experimental design on theoretical 'scanList'

In R-KenK/SimuNet: Network Simulation Framework, with a Zest of Empiricism

Description

Perform an experimental design on theoretical scanList

Usage

perform_exp(scan.list, exp.design = NULL, ...)

Arguments

scan.list	a scanList object. See objects returned by simunet()
exp.design	an expDesign object. See objects returned by design_exp(). If NULL, the inputted scan.list is returned as is.
...	additional expDesign object. If not NULL, the different expDesign will be applied to scan.list in "parallel": the returned value will be a list of empirical scanList, i.e. a sLlist object

Value

an empirical scanList object representing the simulated theoretical scan on which the experimental manipulations have been applied. Such objects contain:

• the 3 dimensional array representing adjacency matrices (first 2 dimensions) throughout the different scans (3rd dimension)

• the same attrs attribute than the inputted scan.list (a list of attributes), in which scanList.type = "empirical"

• a class empirical, which inherits from scanList

• and other attributes might have been added to attrs or modifications depending on exp.design (e.g. sum_scans() returns an object of class sum, that inherits from classes empirical or theoretical, and further from scanList)

If more than one expDesign has been inputted via ..., returns a list of empirical scanList, i.e. a sLlist object

See Also

design_exp(), simunet(), design_sampling().

Examples

set.seed(42)
n <- 5L
samp.effort <- 100L

# Adjacency matrix import
## random directed adjacency matrix
Adj <- sample(1:samp.effort,n * n) |>
  matrix(nrow = 5,dimnames = list(letters[1:n],letters[1:n]))
Adj[lower.tri(Adj,diag = TRUE)] <- 0L
Adj

# Designing the experiments:
## setting a constant probability of not observing edges
group.scan <- design_sampling(method = "group",sampling = 0.8)

## setting a biased focal sampling favoring central individual (node strength)
focal.scan <- design_exp(
  design_sampling(
    method = "focal",
    sampling = function(Adj) Adj |>
      igraph::graph.adjacency("upper",weighted = TRUE) |>
      igraph::strength()
  )
)

## Adding more scans, removing the most peripheral individual, before performing an even focal
## sampling
focal.periRemoved <- design_exp(
  function(Adj) add_scans(Adj,42),     # users can use anonymous function to specify arguments
  remove_mostPeripheral,               # ... or pass functions as arguments directly
  design_sampling(method = "focal",sampling = "even")    # design_sampling: special case
                                                            # that returns sampling functions
)

# Apply the experimental design
## on previously obtained theoretical scans
sL <- simunet(Adj = Adj,samp.effort = samp.effort,mode = "upper",n.scans = 120L)

perform_exp(sL,group.scan)
perform_exp(sL,focal.periRemoved) |> sum_scans()

## performing a list of experiments
perform_exp(sL,group.scan,focal.scan)

R-KenK/SimuNet documentation built on Oct. 22, 2021, 1:27 a.m.