Package index • betaregscale

Model Fitting

Core functions for fitting beta interval regression models.

brs(): Fit a beta interval regression model
brsmm(): Fit a mixed-effects beta interval regression model
brs_fit_fixed(): Fit a fixed-dispersion beta interval regression model
brs_fit_var(): Fit a variable-dispersion beta interval regression model
brs_coef(): Internal coefficient table (deprecated, use brs_est() or summary())

Data simulation for Monte Carlo studies.

Standard methods for fitted model objects of class brs.

anova(<brs>): Model comparison by analysis of deviance (LR test) for `brs`
coef(<brs>): Extract model coefficients
vcov(<brs>): Variance-covariance matrix of estimated coefficients
summary(<brs>): Summarize a fitted model (betareg style)
print(<brs>): Print a fitted model (brief betareg style)
print(<summary.brs>): Print a model summary (betareg style)
logLik(<brs>): Extract log-likelihood
AIC(<brs>): Akaike information criterion
BIC(<brs>): Bayesian information criterion
nobs(<brs>): Number of observations
formula(<brs>): Extract model formula
model.matrix(<brs>): Extract design matrix
fitted(<brs>): Extract fitted values
residuals(<brs>): Extract residuals
predict(<brs>): Predict from a fitted model
confint(<brs>): Wald confidence intervals
plot(<brs>): Diagnostic plots for beta interval regression

Standard methods for fitted model objects of class brsmm.

anova(<brsmm>): Model comparison by analysis of deviance (LR test) for `brsmm`
coef(<brsmm>): Extract coefficients from a brsmm fit
vcov(<brsmm>): Variance-covariance matrix for brsmm coefficients
summary(<brsmm>): Summarize a fitted brsmm model
print(<brsmm>): Print a fitted brsmm model
print(<summary.brsmm>): Print summary for brsmm models
logLik(<brsmm>): Log-likelihood for brsmm models
AIC(<brsmm>): AIC for brsmm models
BIC(<brsmm>): BIC for brsmm models
nobs(<brsmm>): Number of observations in a brsmm fit
formula(<brsmm>): Extract model formula
model.matrix(<brsmm>): Extract design matrix
fitted(<brsmm>): Fitted values from a brsmm model
residuals(<brsmm>): Residuals from a brsmm model
predict(<brsmm>): Predict from a brsmm model
confint(<brsmm>): Wald confidence intervals for brsmm models
ranef(): Extract random effects
ranef(<brsmm>): Extract random effects from a brsmm model
plot(<brsmm>): Diagnostic plots for mixed beta interval regression
brsmm_re_study(): Random-effects study for brsmm models
print(<brsmm_re_study>): Print a random-effects study

Functions for model diagnostics and censoring summaries.

brs_bootstrap() print(<brs_bootstrap>): Parametric bootstrap confidence intervals for brs models
autoplot(<brs_bootstrap>): ggplot2 autoplot for bootstrap results
brs_cens(): Graphical and tabular censoring summary
brs_est(): Coefficient estimates with inference
brs_gof(): Goodness-of-fit measures
brs_hessian(): Extract the Hessian matrix
autoplot(<brs>): ggplot2 autoplot for brs models
autoplot(<brsmm>): ggplot2 autoplot for brsmm models

Post-estimation tables, effects, score probabilities, and validation.

brs_table(): Compare fitted brs models in a single table
brs_marginaleffects(): Marginal effects for brs models
autoplot(<brs_marginaleffects>): ggplot2 autoplot for marginal effects
brs_predict_scoreprob(): Predict score probabilities from a fitted brs model
brs_cv(): K-fold cross-validation for brs models

Functions for preparing response data and beta reparameterization.