Regression Models for Ordinal Data
Try all one-term additions to and deletions from a model
Likelihood ratio test of cumulative link models
ANODE Tables and Likelihood ratio test of cumulative link models
Set control parameters for cumulative link models
Set control parameters for cumulative link models
Fit Cumulative Link Models
Cumulative Link Models
Set control parameters for cumulative link mixed models
Set control parameters for cumulative link mixed models
Cumulative Link Mixed Models
Cumulative link mixed models
Cumulative link models
Confidence intervals and profile likelihoods for parameters in cumulat...
Confidence intervals and profile likelihoods for the standard deviatio...
Confidence intervals and profile likelihoods for parameters in cumulat...
Check convergence of cumulative link models
Ensure Full Rank Design Matrix
Gradients of common densities
The Gumbel Distribution
The log-gamma distribution
Likelihood ratio tests of model terms in scale and nominal formulae
Regression Models for Ordinal Data via Cumulative Link (Mixed) Models
Predict Method for CLM fits
Predict Method for CLM fits
Extract conditional modes and conditional variances from clmm objects
Slice the likelihood of a clm
Update method for cumulative link models
Extract variance and correlation parameters
Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence.