make_missing

Truncate or censor data. is.na(rt) not truncated or censored.

Fit Bayesian (hierarchical) cognitive models
using a linear modeling language interface using particle metropolis Markov
chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM),
linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal
race model (LNR) are supported. Additionally, users can specify their own likelihood
function and/or choose for non-hierarchical
estimation, as well as for a diagonal, blocked or full multivariate normal
group-level distribution to test individual differences. Prior specification
is facilitated through methods that visualize the (implied) prior.
A wide range of plotting functions assist in assessing model convergence and
posterior inference. Models can be easily evaluated using functions
that plot posterior predictions or using relative model comparison metrics
such as information criteria or Bayes factors.
References: Stevenson et al. (2024) <doi:10.31234/osf.io/2e4dq>.

Niek Stevenson

EMC2

Bayesian Hierarchical Analysis of Cognitive Models of Choice

Michelle Donzallaz

Andrew Heathcote

Steven Miletić

Jochen Voss

Andreas Voss

make_missing function

<dl><dt>data</dt>
<dd>Data frame with rt and R columns</dd>
<dt>LT</dt>
<dd>lower truncation bound below which data are removed (scalar or subject named vector)</dd>
<dt>UT</dt>
<dd>upper truncation bound above which data are removed (scalar or subject named vector)</dd>
<dt>LC</dt>
<dd>lower censoring bound (scalar or subject named vector)</dd>
<dt>UC</dt>
<dd>upper censoring bound (scalar or subject named vector)</dd>
<dt>LCresponse</dt>
<dd>Boolean, default TRUE, if false set LC response to NA</dd>
<dt>UCresponse</dt>
<dd>Boolean, default TRUE, if false set UC response to NA</dd>
<dt>LCdirection</dt>
<dd>Boolean, default TRUE, set LC rt to 0, else to NA</dd>
<dt>UCdirection</dt>
<dd>Boolean, default TRUE, set LC rt to Inf, else to NA</dd></dl>

Arguments

make_missing — make_missing

<dl>

<dt>data</dt>
<dd>Data frame with rt and R columns</dd>


<dt>LT</dt>
<dd>lower truncation bound below which data are removed (scalar or subject named vector)</dd>


<dt>UT</dt>
<dd>upper truncation bound above which data are removed (scalar or subject named vector)</dd>


<dt>LC</dt>
<dd>lower censoring bound (scalar or subject named vector)</dd>


<dt>UC</dt>
<dd>upper censoring bound (scalar or subject named vector)</dd>


<dt>LCresponse</dt>
<dd>Boolean, default TRUE, if false set LC response to NA</dd>


<dt>UCresponse</dt>
<dd>Boolean, default TRUE, if false set UC response to NA</dd>


<dt>LCdirection</dt>
<dd>Boolean, default TRUE, set LC rt to 0, else to NA</dd>


<dt>UCdirection</dt>
<dd>Boolean, default TRUE, set LC rt to Inf, else to NA</dd>

</dl>

make_missing: make_missing

Description

Usage

Value

Arguments