enzyme

Enzyme velocity and substrate concentration.

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)
<DOI:10.1007/978-1-4939-2818-7>
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

enzyme function

A data frame with 12 observations on the following 2 variables.<dl class="dl-horizontal">
 <dt>conc</dt><dd>a numeric explanatory vector; substrate concentration</dd>
 <dt>velocity</dt><dd>a numeric response vector; enzyme velocity</dd>
 </dl>

Format

Enzyme Data — enzyme

A data frame with 12 observations on the following 2 variables.<dl class='dl-horizontal'>
 <dt>conc</dt><dd>a numeric explanatory vector; substrate concentration</dd>
 <dt>velocity</dt><dd>a numeric response vector; enzyme velocity</dd>
 </dl>

enzyme: Enzyme Data

Description

Usage

Arguments

Format

Details

References

See Also

Examples