get_optimizer_opts

Implementations of canonical associative learning models,
with tools to run experiment simulations, estimate model parameters,
and compare model representations. Experiments and results are
represented using S4 classes and methods.

Victor Navarro

calmr

Canonical Associative Learning Models and their Representations

get_optimizer_opts function

<dl><dt>model_pars</dt>
<dd>A character vector specifying the name of
the parameters to fit.</dd>
<dt>initial_pars</dt>
<dd>A numeric vector specifying the initial
parameter values to #' evaluate the model at (required by <code>optim</code>).
Defaults to 0 for each parameter.</dd>
<dt>ll, ul</dt>
<dd>A numeric vector specifying the lower and upper
limits of the parameters to fit, respectively</dd>
<dt>optimizer</dt>
<dd>A string specifying the optimizer to use.
One from <code>c("optim", "ga")</code></dd>
<dt>family</dt>
<dd>A string specifying the family function to
generate responses (and calculate the likelihood function with).
One from <code>c("identity", "normal", "poisson")</code>.</dd></dl>

Arguments

Get optimizer options — get_optimizer_opts

<dl>

<dt>model_pars</dt>
<dd>A character vector specifying the name of
the parameters to fit.</dd>


<dt>initial_pars</dt>
<dd>A numeric vector specifying the initial
parameter values to #' evaluate the model at (required by <code>optim</code>).
Defaults to 0 for each parameter.</dd>


<dt>ll, ul</dt>
<dd>A numeric vector specifying the lower and upper
limits of the parameters to fit, respectively</dd>


<dt>optimizer</dt>
<dd>A string specifying the optimizer to use.
One from <code>c("optim", "ga")</code></dd>


<dt>family</dt>
<dd>A string specifying the family function to
generate responses (and calculate the likelihood function with).
One from <code>c("identity", "normal", "poisson")</code>.</dd>

</dl>

get_optimizer_opts: Get optimizer options

Description

Usage

Value

Arguments

See Also