Test Graph against Data
Derives testable implications from the given graphical model and tests them against the given dataset.
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. High-dimensional conditional independence tests can be very unreliable. |
tol |
bound value for tolerated deviation from local test value. By default, we perform a two-sided 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.
# 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 chi-square test
d <- simulateLogistic("dag{X->{U1 M2}->Y U1->M1}",2)
localTests( "dag{X->{M1 M2}->Y}", d, type="cis.chisq" )Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.