tpc: Perform causal discovery using the temporal PC algorithm...

In causalDisco: Tools for Causal Discovery on Observational Data

Perform causal discovery using the temporal PC algorithm (TPC)

Description

Perform causal discovery using the temporal PC algorithm (TPC)

Usage

tpc(
  data,
  order,
  sparsity = 10^(-1),
  test = regTest,
  suffStat = NULL,
  output = "tpdag",
  ...
)

Arguments

data	A data.frame with data. All variables should be assigned to exactly one period by prefixing them with the period name (see example below).
order	A character vector with period-prefixes in their temporal order (see example below).
sparsity	The sparsity level to be used for independence testing (i.e. significance level threshold to use for each test).
test	A procedure for testing conditional independence. The default, regTest uses a regression-based information loss test. Another available option is corTest which tests for vanishing partial correlations. User supplied functions may also be used, see details below about the required syntax.
suffStat	Sufficient statistic. If this argument is supplied, the sufficient statistic is not computed from the inputted data. The format and contents of the sufficient statistic depends on which test is being used.
output	One of "tpdag" or "tskeleton". If "skeleton", a temporal skeleton is constructed and outputted, but the edges are not directed. If "tpdag" (the default), a the edges are directed, resulting in a temporal partially directed acyclic graph.
...	Further optional arguments which are currently not in use.

Details

Note that all independence test procedures implemented in the pcalg package may be used, see pc.

Value

A tpdag or tskeleton object. Both return types are S3 objects, i.e., lists with entries: $amat (the estimated adjacency matrix), $order (character vector with the order, as inputted to this function), $psi (the significance level used for testing), and $ntests (the number of tests conducted).

Examples

#TPC on included example data, use sparsity psi = 0.01, default test (regression-based
#information loss):
data(tpcExample)
tpc(tpcExample, order = c("child", "youth", "oldage"), sparsity = 0.01)


#TPC on included example data, use sparsity psi = 0.01, use test for vanishing partial
# correlations:
data(tpcExample)
tpc(tpcExample, order = c("child", "youth", "oldage"), sparsity = 0.01,
test = corTest)


#TPC on another simulated data set

#Simulate data
set.seed(123)
n <- 500
child_x <- rnorm(n)^2
child_y <- 0.5*child_x + rnorm(n)
child_z <- sample(c(0,1), n, replace = TRUE,
                  prob = c(0.3, 0.7))
         
adult_x <- child_x + rnorm(n)
adult_z <- as.numeric(child_z + rnorm(n) > 0)
adult_w <- 2*adult_z + rnorm(n)
adult_y <- 2*sqrt(child_x) + adult_w^2 + rnorm(n)

simdata <- data.frame(child_x, child_y, child_z,
                      adult_x, adult_z, adult_w,
                      adult_y)

#Define order
simorder <- c("child", "adult")

#Perform TPC with sparsity psi = 0.001
results <- tpc(simdata, order = simorder, sparsity = 10^(-3))

causalDisco documentation built on May 12, 2022, 9:05 a.m.