Description Usage Arguments Details Examples
Derives testable implications from the given graphical model and tests them against the given dataset.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  localTests(
x = NULL,
data = NULL,
type = c("cis", "cis.loess", "cis.chisq", "tetrads", "tetrads.within",
"tetrads.between", "tetrads.epistemic"),
tests = NULL,
sample.cov = NULL,
sample.nobs = NULL,
conf.level = 0.95,
R = NULL,
max.conditioning.variables = NULL,
tol = NULL,
loess.pars = NULL
)
ciTest(X, Y, Z = NULL, data, ...)

x 
the input graph, a DAG, MAG, or PDAG. Either an input graph or an explicit list of tests needs to be specified. 
data 
matrix or data frame containing the data. 
type 
character indicating which kind of local
test to perform. Supported values are 
tests 
list of the precise tests to perform. If not given, the list of tests is automatically derived from the input graph. Can be used to restrict testing to only a certain subset of tests (for instance, to test only those conditional independencies for which the conditioning set is of a reasonably low dimension, such as shown in the example). 
sample.cov 
the sample covariance matrix; ignored if 
sample.nobs 
number of observations; ignored if 
conf.level 
determines the size of confidence intervals for test statistics. 
R 
how many bootstrap replicates for estimating confidence
intervals. If 
max.conditioning.variables 
for conditional independence testing, this parameter can be used to perform only those tests where the number of conditioning variables does not exceed the given value. Highdimensional conditional independence tests can be very unreliable. 
tol 
bound value for tolerated deviation from local test value. By default, we perform a twosided test of the hypothesis theta=0. If this parameter is given, the test changes to abs(theta)=tol versus abs(theta)>tol. 
loess.pars 
list of parameter to be passed on to

X 
vector of variable names. 
Y 
vector of variable names. 
Z 
vector of variable names. 
... 
parameters passed on from 
Tetrad implications can only be derived if a Gaussian model (i.e., a linear structural equation model) is postulated. Conditional independence implications (CI) do not require this assumption. However, both Tetrad and CI implications are tested parametrically: for Tetrads, Wishart's confidence interval formula is used, whereas for CIs, a Z test of zero conditional covariance (if the covariance matrix is given) or a test of residual independence after linear regression (it the raw data is given) is performed. Both tetrad and CI tests also support bootstrapping instead of estimating parametric confidence intervals.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  # Simulate full mediation model with measurement error of M1
set.seed(123)
d < simulateSEM("dag{X>{U1 M2}>Y U1>M1}",.6,.6)
# Postulate and test full mediation model without measurement error
r < localTests( "dag{ X > {M1 M2} > Y }", d, "cis" )
plotLocalTestResults( r )
# Simulate data from example SEM
g < getExample("Polzer")
d < simulateSEM(g,.1,.1)
# Compute independencies with at most 3 conditioning variables
r < localTests( g, d, "cis.loess", R=100, loess.pars=list(span=0.6),
max.conditioning.variables=3 )
plotLocalTestResults( r )
# Test independencies for categorical data using chisquare test
d < simulateLogistic("dag{X>{U1 M2}>Y U1>M1}",2)
localTests( "dag{X>{M1 M2}>Y}", d, type="cis.chisq" )

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