umxSummaryACE_GLM: Summary for a GLM ACE twin model (Cholesky)

In umx: Structural Equation Modeling and Twin Modeling in R

Summary for a GLM ACE twin model (Cholesky)

Description

Path loadings a, c, e on the eta scale, intercepts, -2LL / AIC. No CFI. Extra-Gaussian e paths stay in the model (residual unique covariance). For binomial items the e Cholesky diagonal is fixed at 1 (scale). Bernoulli leftover is not that path (e11 = leftover would double-count). Leftover is added only in the E used for shares: ⁠E = ee' + D⁠, with D = 1 (probit) or \pi^2/3 (logit). The printed e matrix is the Cholesky of that E. Poisson has D = 0. Standardized values divide each row by that phenotype's eta-scale sd (sqrt of diag(A+C+E) with leftover in E). For two traits, prints rA, rC, and rE = cov2cor(E_total) so unique correlation is diluted by leftover on the diagonal (genetic rA is not).

Usage

umxSummaryACE_GLM(
  model,
  digits = 2,
  comparison = NULL,
  std = TRUE,
  report = c("markdown", "html"),
  ...
)

Arguments

model	a model from umxACE_GLM()
digits	rounding
comparison	optional comparison model for umxCompare()
std	if TRUE, report eta-scale variance shares
report	"markdown" or "html"
...	unused

Value

parameter table (invisibly)

See Also

umxACE_GLM()

Other Twin Modeling Functions: power.ACE.test(), umx, umxACE(), umxACE_DE(), umxACE_GLM(), umxACEcov(), umxACEv(), umxCP(), umxCloud(), umxDiffMZ(), umxDiscTwin(), umxDoC(), umxDoCp(), umxGxE(), umxGxE_window(), umxGxEbiv(), umxIP(), umxMRDoC(), umxReduce(), umxReduceACE(), umxReduceGxE(), umxRotate.MxModelCP(), umxSexLim(), umxSimplex(), umxSummarizeTwinData(), umxSummaryACE(), umxSummaryACEv(), umxSummaryDoC(), umxSummaryGxEbiv(), umxSummarySexLim(), umxSummarySimplex(), umxTwinMaker(), umx_make_double_entry_data()

umx documentation built on Oct. 11, 2026, 5:07 p.m.