A generic function that extracts the
 response/dependent variable from objects.

depvar

An implementation of about 6 major classes of
statistical regression models. The central algorithm is
Fisher scoring and iterative reweighted least squares.
At the heart of this package are the vector generalized linear
and additive model (VGLM/VGAM) classes. VGLMs can be loosely
thought of as multivariate GLMs. VGAMs are data-driven
VGLMs that use smoothing. 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 the package. Currently only
fixed-effects models are implemented. Many (100+) models and
distributions are estimated by maximum likelihood estimation
(MLE) or penalized MLE. The other classes are RR-VGLMs
(reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained
RR-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). Hauck-Donner effect detection is implemented.
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

Cleve Moler

depvar function

Dependent (Response) Variable Extracted — depvar

<dl>

<dt>object</dt>
<dd>An object that has some response/dependent variable.

</dd>

<dt>...</dt>
<dd>


Other arguments fed into the specific methods
 function of the model.
 The following mainly applies to
 <code>"vglm"</code> class <code>object</code>s.
 In particular, sometimes
 <code>type = c("lm", "lm2")</code>
 is available, in
 which case the first one is chosen if the
 user does not input a value.
 The latter value corresponds to argument
 <code>form2</code>, and sometimes a
 response for that is optional.
 
 
Another argument is <code>muxypw</code>
(whose default is <code>FALSE</code>)
which is whether <code>object@y</code>
needs to be multiplied by the prior weight.
 This feature is useful for family functions
 that store sample
 proportions in <code>object@y</code> and the prior
 weights in <code>object@prior.weights</code>
 because sometimes the count response is
 needed,
 e.g., for <code>vglmnet()</code>.
 So if on <code>object@family@infos()</code>
 <code>muxypw == FALSE</code> and if
 <code>Mux4vglmnet = TRUE</code> then set
 <code>depvar(object, muxypw = TRUE)</code>
 to get the raw counts.
 Also, because <code>roundmux = FALSE</code> is
 the default, it is a good idea to set
 <code>roundmux = TRUE</code> to make sure the result
 is integer-valued, i.e.,
 <code>depvar(object, muxypw = TRUE, roundmux = TRUE)</code>.
 Note that recycling is used.


</dd>

</dl>

depvar: Dependent (Response) Variable Extracted

Description

Usage

Arguments

Value

Details

See Also

Examples