Package index • galamm

Modeling

Fit a generalized additive latent and mixed model.

galamm(): Fit a generalized additive latent and mixed model

sl(): Set up smooth term with factor loading

t2l(): Set up smooth term with factor loading

galamm-package: galamm: Generalized Additive Latent and Mixed Models

High-level model output

Functions for summarising the result of model fits.

anova(<galamm>): Compare likelihoods of galamm objects

plot(<galamm>): Diagnostic plots for galamm objects

plot_smooth(<galamm>): Plot smooth terms for galamm fits

print(<galamm>): Print method for GALAMM fits

print(<summary.galamm>): Print method for summary GALAMM fits

summary(<galamm>): Summarizing GALAMM fits

Model details

Detailed information on specific parts of model fits.

VarCorr(<galamm>): Extract variance and correlation components from model

coef(<galamm>): Extract galamm coefficients

confint(<galamm>): Confidence intervals for model parameters

deviance(<galamm>): Extract deviance of galamm object

factor_loadings(<galamm>): Extract factor loadings from galamm object

family(<galamm>): Extract family or families from fitted galamm

fitted(<galamm>): Extract model fitted values

fixef(<galamm>): Extract fixed effects from galamm objects

formula(<galamm>): Extract formula from fitted galamm object

logLik(<galamm>): Extract Log-Likelihood of galamm Object

nobs(<galamm>): Extract the Number of Observations from a galamm Fit

predict(<galamm>): Predictions from a model at new data values

print(<VarCorr.galamm>): Print method for variance-covariance objects

ranef(<galamm>): Extract random effects from galamm object.

residuals(<galamm>): Residuals of galamm objects

response(): Extract response values

sigma(<galamm>): Extract square root of dispersion parameter from galamm object

vcov(<galamm>): Calculate variance-covariance matrix for GALAMM fit

Datasets

Simulated and real example datasets.

cognition: Simulated Data with Measurements of Cognitive Abilities

diet: Diet Data

epilep: Epilepsy Data

hsced: Example Data with Heteroscedastic Residuals

latent_covariates: Simulated Data with Latent and Observed Covariates Interaction

latent_covariates_long: Simulated Longitudinal Data with Latent and Observed Covariates Interaction

lifespan: Simulated Dataset with Lifespan Trajectories of Three Cognitive Domains

mresp: Simulated Mixed Response Data

mresp_hsced: Simulated Mixed Response Data with Heteroscedastic Residuals

Optimization

Functions to aid in optimization.

extract_optim_parameters(<galamm>): Extract parameters from fitted model for use as initial values

galamm_control(): Control values for galamm fit