Returns information necessary to interpret piecewise structural equation models, including tests of directed separation, path coefficients, information criterion values, and R-squared values of individual models.
# S3 method for psem summary(object, ..., basis.set = NULL, direction = NULL, conserve = FALSE, conditioning = FALSE, add.claims = NULL, standardize = "scale", standardize.type = "latent.linear", test.type = "II", intercepts = FALSE, .progressBar = TRUE)
a list of structural equations
additional arguments to summary
a vector of claims defining the specific directionality of any independence claim(s)
whether the most conservative P-value should be returned (See Details) Default is FALSE
whether all conditioning variables should be shown in the table Default is FALSE
an optional vector of additional independence claims (P-values) to be added to the basis set
whether standardized path coefficients should be reported Default is "scale"
the type of standardized for non-Gaussian responses:
the type of test ("II" or "III") for significance of categorical
whether intercepts should be included in the coefficient table Default is FALSE
an optional progress bar. Default is TRUE
summary.psem returns a list of summary
A summary table of the tests of directed
Fisher's C statistic, degrees of freedom, and significance value based on a Chi-square test.
Information criterion (Akaike, Bayesian, corrected Akaike) as well as degrees of freedom and sample size.
summary table of the path coefficients, from
(Pseudo)-R2 values, from
The forthcoming argument
groups splits the analysis based on an optional grouping
factor, conducts separate d-sep tests, and reports goodness-of-fit and path
coefficients for each submodel. The procedure is approximately similar to a
multigroup analysis in traditional variance-covariance SEM. Coming in version 2.1.
In cases involving non-normally distributed responses in the independence
claims that are modeled using generalized linear models, the significance of
the independence claim is not reversable (e.g., the P-value of Y ~ X is not
the same as X ~ Y). This is due to the transformation of the response via
the link function. In extreme cases, this can bias the goodness-of-fit
summary.psem will issue a warning when this case is present
and provide guidance for solutions. One solution is to specify the
directionality of the relationship using the
direction argument, e.g.
direction = c("X <- Y"). Another is to run both tests (Y ~ X, X ~ Y)
and return the most conservative (i.e., lowest) P-value, which can be
toggled using the
conserve = TRUE argument.
In some cases, additional claims that were excluded from the basis set can
be added back in using the argument
add.claims. These could be, for
instance, independence claims among exogenous variables. See Details in
Standardized path coefficients are scaled by standard deviations.
Shipley, Bill. "A new inferential test for path models based on directed acyclic graphs." Structural Equation Modeling 7.2 (2000): 206-218.
Shipley, Bill. Cause and correlation in biology: a user's guide to path analysis, structural equations and causal inference. Cambridge University Press, 2002.
Shipley, Bill. "Confirmatory path analysis in a generalized multilevel context." Ecology 90.2 (2009): 363-368.
Shipley, Bill. "The AIC model selection method applied to path analytic models compared using a d-separation test." Ecology 94.3 (2013): 560-564.
The model fitting function