dgo_fit-class

dgo_fit

<code>dgo_fit</code> is a superclass for <code><a rd-options="" href="/link/dgirt_fit?package=dgo&version=0.2.15" data-mini-rdoc="dgo::dgirt_fit">dgirt_fit</a></code> and
<code><a rd-options="" href="/link/dgmrp_fit?package=dgo&version=0.2.15" data-mini-rdoc="dgo::dgmrp_fit">dgmrp_fit</a></code> that inherits from the
<code><a rd-options="rstan" href="/link/stanfit-class?package=dgo&version=0.2.15&to=rstan" data-mini-rdoc="rstan::stanfit-class">stanfit-class</a></code> in the <code><a rd-options="" href="/link/rstan?package=dgo&version=0.2.15" data-mini-rdoc="dgo::rstan">rstan</a></code> package.

Fit dynamic group-level item response theory (IRT) and multilevel
regression and poststratification (MRP) models from item response data. dgo
models latent traits at the level of demographic and geographic groups,
rather than individuals, in a Bayesian group-level IRT approach developed by
Caughey and Warshaw (2015) <doi:10.1093/pan/mpu021>. The package also
estimates subpopulations' average responses to single survey items with a
dynamic MRP model proposed by Park, Gelman, and Bafumi (2004)
<doi:10.11126/stanford/9780804753005.003.0011>.

James Dunham

Dynamic Estimation of Group-Level Opinion

Devin Caughey

Christopher Warshaw

dgo_fit-class function

<dl class="dl-horizontal">
<dt><code>dgirt_in</code></dt><dd><code><a rd-options="" href="/link/dgirtin-class?package=dgo&version=0.2.15" data-mini-rdoc="dgo::dgirtin-class">dgirtin-class</a></code> data used to fit the model.</dd></dl><dt><code>call</code></dt><dd>The function call that returned the <code>dgo_fit</code> object.</dd>

Slots

<code>dgo_fit</code> is a superclass for <code><a rd-options='' href='dgirt_fit'>dgirt_fit</a></code> and
<code><a rd-options='' href='dgmrp_fit'>dgmrp_fit</a></code> that inherits from the
<code><a rd-options='rstan' href='stanfit-class'>stanfit-class</a></code> in the <code><a rd-options='' href='rstan'>rstan</a></code> package.

dgo_fit-class: A class for fitted models

Description

Arguments

Slots

See Also