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Assemble a distributional piecewise SEM from fitted drmTMB models

Source: R/drm_sem.R
drm_psem.Rd

drm_psem() is the piecewiseSEM-style core: you fit each endogenous node yourself with drmTMB::drmTMB() and pass the fitted models. drmSEM extracts the component-labelled graph, validates it as a DAG, and provides path tables, d-separation tests, and simulation-based effects on top.

Usage

drm_psem(
  ...,
  data = NULL,
  covariances = NULL,
  composites = NULL,
  feedback = NULL
)

# S3 method for class 'drm_sem'
summary(object, ...)

Arguments

...

Named fitted drmTMB objects, one per endogenous node. The name is the node identifier used in path queries; predictors are matched to a node by its name or its response variable.

data

The data frame all nodes were fitted to. Defaults to the data of the first node.

covariances

Optional covary() declaration(s) (one object or a list) recording residual (rho12) or higher-level (corpair) covariance edges between responses. These are reported by covariances() and respected by basis_set() / dsep(), but never enter paths() or the effect decomposition.

composites

Optional drm_composite() declaration(s). For drm_psem() the construct column(s) must already be present in the data the nodes were fitted on; the declarations are recorded so loadings() can report them.

feedback

Optional drm_cycle() declaration(s) naming a feedback (cyclic) motif. Cycles are an error unless declared; a declared motif relaxes the topological-order requirement and is reported by cycles(). Node-wise fitting of a declared cycle is inconsistent under simultaneity (a warning is emitted); see docs/design/10-cyclic-feedback.md.

object

A drm_sem object.

Value

A drm_sem object.

Details

Fit nodes with control = drmTMB::drm_control(se = TRUE) so that vcov(), Wald intervals, and d-separation refits are available.

References

Shipley B (2009). “Confirmatory Path Analysis in a Generalized Multilevel Context.” Ecology, 90(2), 363–368. doi:10.1890/08-1034.1 .

Lefcheck JS (2016). “piecewiseSEM: Piecewise Structural Equation Modelling in R for Ecology, Evolution, and Systematics.” Methods in Ecology and Evolution, 7(5), 573–579. doi:10.1111/2041-210X.12512 .

Brooks ME, Kristensen K, van Benthem KJ, Magnusson A, Berg CW, Nielsen A, Skaug HJ, Maechler M, Bolker BM (2017). “glmmTMB Balances Speed and Flexibility Among Packages for Zero-Inflated Generalized Linear Mixed Models.” The R Journal, 9(2), 378–400. doi:10.32614/RJ-2017-066 .

Rigby RA, Stasinopoulos DM (2005). “Generalized Additive Models for Location, Scale and Shape.” Journal of the Royal Statistical Society Series C: Applied Statistics, 54(3), 507–554. doi:10.1111/j.1467-9876.2005.00510.x .

See also

drm_sem() for the declarative interface that fits nodes for you.

Examples

if (FALSE) { # \dontrun{
ctrl <- drmTMB::drm_control(se = TRUE)
size_fit <- drmTMB::drmTMB(
  drmTMB::bf(size ~ temp + habitat, sigma ~ temp),
  family = stats::gaussian(), data = dat, control = ctrl)
abundance_fit <- drmTMB::drmTMB(
  drmTMB::bf(abundance ~ size + temp, zi ~ habitat),
  family = drmTMB::nbinom2(), data = dat, control = ctrl)
sem <- drm_psem(size = size_fit, abundance = abundance_fit, data = dat)
paths(sem)
} # }