Nothing
library(backShift)
context("All supported arguments")
# Example --------
seed <- 1
set.seed(seed)
## Parameters for simulation ------
# number of observations
n <- 100
# number of variables
p <- 10
# number of environments
G <- 5
# if the location of the interventions is known, on how many vars. should
# be intervention in each environment (as a fraction of p)
fracVarInt <- 0.5
# multiplier for interventions (m_I in manuscript)
intMult <- 1.5
# multiplier for interventions (m_e in manuscript)
noiseMult <- 1
data("exampleAdjacencyMatrix")
A <- exampleAdjacencyMatrix
p <- 10
## Options for method -------
# if stability selection should not be used, set EV = 0
# number of false selections for stability selection
EV <- 2
# selection threshold for stability selection
thres <- 0.75
boolean <- c(TRUE, FALSE)
# simulate non-Gaussian noise?
for(nonGauss in boolean){
# also simulate observational data?
for(simulateObs in boolean){
# should hidden vars be included?
for(hidden in boolean){
# should the location of the interventions be known?
for(knownInterventions in boolean){
## Simulate data -------
simulation.res <- simulateInterventions(n, p, A, G, intMult, noiseMult,
nonGauss = nonGauss,
hiddenVars = hidden,
knownInterventions = knownInterventions,
fracVarInt,
simulateObs = simulateObs,
seed)
# extract X, environment vector and index of observational data
X <- simulation.res$X
env <- simulation.res$environment
baseInd <- simulation.res$configs$indexObservationalData
# use covariance matrix instead of Gram matrix
for(useCov in boolean){
## Run backShift -------
test_that(paste("Checks backShift for.."), {
expect_is(
backshift.res <- backShift(X, env, covariance=useCov, nsim = 5,
ev=EV, threshold=thres,
baseSettingEnv = baseInd, tolerance = 1e-3,
verbose = FALSE)$Ahat
, "matrix")
})
}
}
}
}
}
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