plotRegressionError

targets

the values predicted/fitted by the model

fits

parameters passed to <code>plot</code>

The plot shows target values on the x-axis and fitted/predicted values on the y-axis. 
The optimal fit would yield a line through zero with gradient one.
This optimal line is shown in black color. A linear fit to the actual data
is shown in red color.

The Stuttgart Neural Network Simulator (SNNS) is a library
containing many standard implementations of neural networks. This
package wraps the SNNS functionality to make it available from
within R. Using the 'RSNNS' low-level interface, all of the
algorithmic functionality and flexibility of SNNS can be accessed.
Furthermore, the package contains a convenient high-level
interface, so that the most common neural network topologies and
learning algorithms integrate seamlessly into R.

Christoph Bergmeir

RSNNS

Neural Networks using the Stuttgart Neural Network Simulator
(SNNS)

José M. Benítez

Andreas Zell

Niels Mache

Günter Mamier

Michael Vogt

Sven Döring

Ralf Hübner

Kai-Uwe Herrmann

Tobias Soyez

Michael Schmalzl

Tilman Sommer

Artemis Hatzigeorgiou

Dietmar Posselt

Tobias Schreiner

Bernward Kett

Martin Reczko

Martin Riedmiller

Mark Seemann

Marcus Ritt

Jamie DeCoster

Jochen Biedermann

Joachim Danz

Christian Wehrfritz

Patrick Kursawe

Andre El-Ama

plotRegressionError function

<dl><dt>targets</dt>
<dd>the target values</dd>
<dt>fits</dt>
<dd>the values predicted/fitted by the model</dd>
<dt>...</dt>
<dd>parameters passed to <code>plot</code></dd></dl>

Arguments

Plot a regression error plot — plotRegressionError

<dl>

<dt>targets</dt>
<dd>the target values</dd>


<dt>fits</dt>
<dd>the values predicted/fitted by the model</dd>


<dt>...</dt>
<dd>parameters passed to <code>plot</code></dd>

</dl>

Plot a regression error plot

plotRegressionError: Plot a regression error plot

Description

Usage

Arguments