Linear Quantile Mixed Models
Summary for an lqm Object
Summary for an lqmm Object
Extract Variance-Covariance Matrix
Bootstrap functions for LQM and LQMM
Extract LQM Coefficients
Extract LQMM Coefficients
Variance-Covariance Matrix
The Asymmetric Laplace Distribution
Extract Fixed and Random Bootstrapped Parameters
Gaussian Quadrature
Gaussian Quadrature
Test for Positive Definiteness
Extract Log-Likelihood
Extract Log-Likelihood
Quantile Regression for Counts
Quantile Regression Fitting by Gradient Search
Fitting Linear Quantile Models
Control parameters for lqm estimation
Internal lqmm objects
Linear Quantile Models and Linear Quantile Mixed Models
Linear Quantile Mixed Models Fitting by Derivative-Free Optimization
Linear Quantile Mixed Models Fitting by Gradient Search
Fitting Linear Quantile Mixed Models
Control parameters for lqmm estimation
Compute Nearest Positive Definite Matrix
Functions for Asymmetric Laplace Distribution Parameters
Maximum Likelihood Estimation of Asymmetric Laplace Distribution
Predictions from LQM Objects
Predictions from an lqmm Object
Print LQM Objects
Print an lqmm Object
Print an lqm Summary Object
Print an lqmm Summary Object
Extract Random Effects
Residuals from an LQM Objects
Residuals from an lqmm Object
Summary for a boot.lqm Object
Summary for a boot.lqmm Object
Functions to fit quantile regression models for hierarchical data (2-level nested designs) as described in Geraci and Bottai (2014, Statistics and Computing) <doi:10.1007/s11222-013-9381-9>. A vignette is given in Geraci (2014, Journal of Statistical Software) <doi:10.18637/jss.v057.i13> and included in the package documents. The packages also provides functions to fit quantile models for independent data and for count responses.