Returns information necessary to interpret piecewise structural equation models, including tests of directed separation, path coefficients, information criterion values, and Rsquared 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)
object  a list of structural equations 

...  additional arguments to summary 
direction  a vector of claims defining the specific directionality of any independence claim(s) 
conserve  whether the most conservative Pvalue should be returned (See Details) Default is FALSE 
conditioning  whether all conditioning variables should be shown in the table Default is FALSE 
add.claims  an optional vector of additional independence claims (Pvalues) to be added to the basis set 
standardize  whether standardized path coefficients should be reported Default is "scale" 
standardize.type  the type of standardized for nonGaussian responses:

test.type  the type of test ("II" or "III") for significance of categorical
variables (from 
intercepts  whether intercepts should be included in the coefficient table Default is FALSE 
.progressBar  an optional progress bar. Default is TRUE 
The function summary.psem
returns a list of summary
statistics:
A summary table of the tests of directed
separation, from dSep
.
Fisher's C statistic, degrees of freedom, and significance value based on a Chisquare test.
Information criterion (Akaike, Bayesian, corrected Akaike) as well as degrees of freedom and sample size.
A
summary table of the path coefficients, from link{coefs}
.
(Pseudo)R2 values, from rsquared
.
The forthcoming argument groups
splits the analysis based on an optional grouping
factor, conducts separate dsep tests, and reports goodnessoffit and path
coefficients for each submodel. The procedure is approximately similar to a
multigroup analysis in traditional variancecovariance SEM. Coming in version 2.1.
In cases involving nonnormally 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 Pvalue 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 goodnessoffit
tests. 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) Pvalue, 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
basisSet
.
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): 206218.
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): 363368.
Shipley, Bill. "The AIC model selection method applied to path analytic models compared using a dseparation test." Ecology 94.3 (2013): 560564.
The model fitting function psem
.