Generalized Additive Latent and Mixed Models
Compare likelihoods of galamm objects
Extract galamm coefficients
Confidence intervals for model parameters
Extract deviance of galamm object
Extract parameters from fitted model for use as initial values
Extract factor loadings from galamm object
Extract family or families from fitted galamm
Extract model fitted values
Extract fixed effects from galamm objects
Extract formula from fitted galamm object
Control values for galamm fit
galamm: Generalized Additive Latent and Mixed Models
Fit a generalized additive latent and mixed model
Extract log likelihood, AIC, and related statistics from a GALAMM
Extract Log-Likelihood of galamm Object
Extract the Number of Observations from a galamm Fit
Plot smooth terms for galamm fits
Diagnostic plots for galamm objects
Predictions from a model at new data values
Print method for summary GALAMM fits
Print method for variance-covariance objects
Extract random effects from galamm object.
Residuals of galamm objects
Extract square root of dispersion parameter from galamm object
Set up smooth term with factor loading
Summarizing GALAMM fits
Set up smooth term with factor loading
Extract variance and correlation components from model
Calculate variance-covariance matrix for GALAMM fit
Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to 'lme4' (Bates et al. (2015) <doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>).
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