derive_vcov

The function is a wrapper around <code>derive_vcov_mnl</code> and
<code>derive_vcov_rpl</code> and calculates the variance-covariance matrix
of the specified model and design given the priors.

Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. This is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods. For an overview of stated choice experimental design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. & Daly. A. <doi:10.4337/9781781003152>. The package website can be accessed at <https://spdesign.edsandorf.me>. We acknowledge funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163).

Erlend Dancke Sandorf

spdesign

Designing Stated Preference Experiments

Danny Campbell

derive_vcov function

<dl><dt>design_env</dt>
<dd>An environment containing all the elements
necessary to derive the variance-covariance matrix</dd>
<dt>model</dt>
<dd>A string indicating the model for which you wish to derive the
variance covariance matrix. Can be either "mnl" or "rpl"</dd></dl>

Arguments

Derive the variance covariance matrix of the design — derive_vcov

<dl>

<dt>design_env</dt>
<dd>An environment containing all the elements
necessary to derive the variance-covariance matrix</dd>


<dt>model</dt>
<dd>A string indicating the model for which you wish to derive the
variance covariance matrix. Can be either "mnl" or "rpl"</dd>

</dl>

derive_vcov: Derive the variance covariance matrix of the design

Description

Usage

Value

Arguments