simreg

bootreg

Derive parameter estimates and standard errors by simulation, or
 by bootstrap resampling.

models

regression

Functions and data are provided that support a course
that emphasizes statistical issues of inference and generalizability.
The functions are designed to make it straightforward to illustrate
the use of cross-validation, the training/test approach, simulation,
and model-based estimates of accuracy.  Methods considered are
Generalized Additive Modeling, Linear and Quadratic Discriminant
Analysis, Tree-based methods, and Random Forests.

John Maindonald

gamclass

Functions and Data for a Course on Modern Regression and
Classification

simreg function

<dl> <dt>formula</dt>
<dd>Model formula</dd> <dt>data</dt>
<dd>Data frame from which names in formula can be taken</dd><dt>nsim</dt>
<dd>Number of repeats of the simulation (<code>simreg</code>)</dd><dt>nboot</dt>
<dd>Number of bootstrap resamples (<code>bootreg</code>)</dd></dl>

Arguments

Author

Simulate (repeated) regression calculations — simreg

<dl>

 <dt>formula</dt>
<dd>Model formula</dd>

 <dt>data</dt>
<dd>Data frame from which names in formula can be taken</dd>

<dt>nsim</dt>
<dd>Number of repeats of the simulation (<code>simreg</code>)</dd>

<dt>nboot</dt>
<dd>Number of bootstrap resamples (<code>bootreg</code>)</dd>

</dl>

simreg: Simulate (repeated) regression calculations

Description

Usage

Value

Arguments

Author

References

Examples