wine

This oenological data frame concerns the amount of bitterness
 in 78 bottles of white wine.

datasets

An implementation of about 6 major classes of
    statistical regression models. At the heart of it are the
    vector generalized linear and additive model (VGLM/VGAM)
    classes, and the book "Vector Generalized Linear and
    Additive Models: With an Implementation in R" (Yee, 2015)
    gives details of the statistical framework and VGAM package.
    Currently only fixed-effects models are implemented,
    i.e., no random-effects models. Many (150+) models and
    distributions are estimated by maximum likelihood estimation
    (MLE) or penalized MLE, using Fisher scoring. VGLMs can be
    loosely thought of as multivariate GLMs. VGAMs are data-driven
    VGLMs (i.e., with smoothing). The other classes are RR-VGLMs
    (reduced-rank VGLMs), quadratic RR-VGLMs, reduced-rank VGAMs,
    RCIMs (row-column interaction models)---these classes perform
    constrained and unconstrained quadratic ordination (CQO/UQO)
    models in ecology, as well as constrained additive ordination
    (CAO). Note that these functions are subject to change;
    see the NEWS and ChangeLog files for latest changes.

Thomas Yee

VGAM

Vector Generalized Linear and Additive Models

wine function

Christensen, R. H. B. (2013)
Analysis of ordinal data with cumulative link models---estimation
with the R-package <pkg>ordinal</pkg>.
R Package Version 2013.9-30.
<url>http://cran.r-project.org/package=ordinal</url>. Randall, J. H. (1989) 
 The analysis of sensory data by generalized linear model.
 Biometrical Journal 31(7), 781--793.
 Kosmidis, I. (2014)
 Improved estimation in cumulative link models.
Journal of the Royal Statistical Society, Series B, Methodological,
76, in press.

wine: Bitterness in Wine Data

Description

Usage

Arguments

source

Details

Examples