dsem (version 1.6.0)

test_dsep: Test d-separation

Description

Calculate the p-value for a test of d-separation (Experimental)

Usage

test_dsep(
  object,
  n_time = NULL,
  n_burnin = NULL,
  what = c("pvalue", "CIC", "all"),
  test = c("wald", "lr"),
  seed = 123456,
  order = NULL,
  impute_data = c("by_test", "single", "none")
)

Value

A p-value representing the weight of evidence that the data arises from the specified model, where a low p-value indicates significant evidence for rejecting this hypothesis.

Arguments

object

object from dsem

n_time

how many times to include when defining the set of conditional independence relationships. If missing, this value is taken from the maximum lag that's included in the model plus one.

n_burnin

how many times to include prior to seq_len(n_time) when identifying the conditioning set that must be included when defining conditional independence relationships.

what

whether to just get the p-value, an information criterion based on the conditional independence test, or a named list with these two and other intermediate calculations (used for diagnosing test behavior)

test

whether to test each conditional-independence relationship using a (univariate) wald test or a (multivariate) likelihood ratio test. The likelihood-ratio test might be more accurate given estimation covariance and also faster (does not require standard errors), but also is not used by phylopath and therefore less supported by previous d-dsep testing applications.

seed

random number seed used when simulating imputed data, so that results are reproducible.

order

an optional character vector providing the order for variables to be tested when defining the directed acyclic graph for use in d-sep testing

impute_data

whether to independently impute missing data for each conditional independence test, or to use imputed values from the original fit. The data are imputed separately for each conditional independence test, so that they are uncorrelated as expected when combining them using Fisher's method. Preliminary testing suggests that using imputed data improves test performance

Details

A user-specified SEM implies a set of conditional independence relationships among variables, which can be fitted individually, extracting the slope and associated p-value, and then combining these p-values to define a model-wide (omnibus) p-value for the hypothesis that a given data set arises from the specified model. This test is modified from package:phylopath. However it is unclear exactly how to define the set of conditional-independence assumptions in a model with temporal autocorrelation, and the test was not developed for uses when data are missing. At the time of writing, the function is hightly experimental.

Note that the method is not currently designed to deal with two-headed arrows among variables (i.e., exogenous covariance).

References

Shipley, B. (2000). A new inferential test for path models based on directed acyclic graphs. Structural Equation Modeling, 7(2), 206-218. tools:::Rd_expr_doi("10.1207/S15328007SEM0702_4")

Examples

# Simulate data set
set.seed(101)
a = rnorm( 100 )
b = 0.5*a + rnorm(100)
c = 1*a + rnorm(100)
d = 1*b - 0.5*c + rnorm(100)
tsdata = ts(data.frame(a=a, b=b, c=c, d=d))

# fit wrong model
wrong = dsem(
  tsdata = tsdata,
  sem = "
    a -> d, 0, a_to_d
    b -> d, 0, b_to_d
    c -> d, 0, c_to_d
  "
)
test_dsep( wrong )

# fit right model
right = dsem(
  tsdata = tsdata,
  sem = "
    a -> b, 0, a_to_b
    a -> c, 0, a_to_c
    b -> d, 0, b_to_d
    c -> d, 0, c_to_d
  "
)
test_dsep( right )