metaSDT.loglik

Parameters of the model, c(intercepts^T,d.n,d,d.s,beta^T)^T, where length(intercepts)=K0-1 and lenght(beta)=ncol(X).

parm

Meta SDT log-likelihood function, internal function. Used by <code>metaSDTreg</code>.

internal

Regression methods for the meta-SDT model. The package implements methods for cognitive experiments of metacognition as described in Kristensen, S. B., Sandberg, K., & Bibby, B. M. (2020). Regression methods for metacognitive sensitivity. Journal of Mathematical Psychology, 94. <doi:10.1016/j.jmp.2019.102297>.

Simon Bang Kristensen

metaSDTreg

Regression Models for Meta Signal Detection Theory

Kristian Sandberg

Bo Martin Bibby

metaSDT.loglik function

<dl><dt>parm</dt>
<dd>Parameters of the model, c(intercepts^T,d.n,d,d.s,beta^T)^T, where length(intercepts)=K0-1 and lenght(beta)=ncol(X).</dd>
<dt>df</dt>
<dd>Data frame</dd>
<dt>A</dt>
<dd>Matrix of responses</dd>
<dt>X</dt>
<dd>Matrix of covariates</dd>
<dt>X.n</dt>
<dd>Presently not in use</dd>
<dt>X.s</dt>
<dd>Presently not in use</dd>
<dt>L0</dt>
<dd>Levels of the type 2 task</dd>
<dt>K0</dt>
<dd>Levels of the ordinal variable A</dd></dl>

Arguments

Meta-SDT log-likelihood — metaSDT.loglik

<dl>

<dt>parm</dt>
<dd>Parameters of the model, c(intercepts^T,d.n,d,d.s,beta^T)^T, where length(intercepts)=K0-1 and lenght(beta)=ncol(X).</dd>


<dt>df</dt>
<dd>Data frame</dd>


<dt>A</dt>
<dd>Matrix of responses</dd>


<dt>X</dt>
<dd>Matrix of covariates</dd>


<dt>X.n</dt>
<dd>Presently not in use</dd>


<dt>X.s</dt>
<dd>Presently not in use</dd>


<dt>L0</dt>
<dd>Levels of the type 2 task</dd>


<dt>K0</dt>
<dd>Levels of the ordinal variable A</dd>

</dl>

metaSDT.loglik: Meta-SDT log-likelihood

Description

Usage

Arguments