uvsdLogLike

Number of studied-item conditions.

Vector of responses, ranging from 0:(K-1).

cond

Vector of new/studied condition index; 0=new, 1=studied.

Scond

Vector of subject index, starting at 0 with no missing
 subject numbers.

subj

Vector of item index, starting at 0 with no missing
 item numbers.

item

Block of parameters for new-item means.

blockN

Block of parameters for studied-item means.

blockS

Block of parameters for Sigma2 values. If there is only
 one Sigma2 for all participants and items, then the first element of
 blockS2 should contain this value, and the other elements fo blockS2 should
 be zero.

blockS2

VECTOR of criteria including -Inf and Inf for top and bottom
critieria, respectively. Vector contains the (K+1) criteria for the
first subjects, followed by those for the second subject, etc.

crit

Computes log likelihood for UVSD model

Contains functions for fitting hierarchical versions of
EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and
our gamma signal detection model to recognition memory
confidence-ratings data.

Mike

hbmem

Hierarchical Bayesian Analysis of Recognition Memory

uvsdLogLike function

<dl><dt>R</dt>
<dd>Total number of trials.</dd><dt>NN</dt>
<dd>Number of new-item conditions.</dd><dt>NS</dt>
<dd>Number of studied-item conditions.</dd><dt>I</dt>
<dd>Number of subjects.</dd><dt>J</dt>
<dd>Number of items.</dd><dt>K</dt>
<dd>Number of response options.</dd><dt>dat</dt>
<dd>Vector of responses, ranging from 0:(K-1).</dd><dt>cond</dt>
<dd>Vector of condition index.</dd><dt>Scond</dt>
<dd>Vector of new/studied condition index; 0=new, 1=studied.</dd><dt>subj</dt>
<dd>Vector of subject index, starting at 0 with no missing
 subject numbers.</dd><dt>item</dt>
<dd>Vector of item index, starting at 0 with no missing
 item numbers.</dd><dt>lag</dt>
<dd>Vector of lag index.</dd><dt>blockN</dt>
<dd>Block of parameters for new-item means.</dd><dt>blockS</dt>
<dd>Block of parameters for studied-item means.</dd><dt>blockS2</dt>
<dd>Block of parameters for Sigma2 values. If there is only
 one Sigma2 for all participants and items, then the first element of
 blockS2 should contain this value, and the other elements fo blockS2 should
 be zero.</dd><dt>crit</dt>
<dd>VECTOR of criteria including -Inf and Inf for top and bottom
critieria, respectively. Vector contains the (K+1) criteria for the
first subjects, followed by those for the second subject, etc.</dd></dl>

Arguments

Author

Function uvsdLogLike — uvsdLogLike

<dl>

<dt>R</dt>
<dd>Total number of trials.</dd>

<dt>NN</dt>
<dd>Number of new-item conditions.</dd>

<dt>NS</dt>
<dd>Number of studied-item conditions.</dd>

<dt>I</dt>
<dd>Number of subjects.</dd>

<dt>J</dt>
<dd>Number of items.</dd>

<dt>K</dt>
<dd>Number of response options.</dd>

<dt>dat</dt>
<dd>Vector of responses, ranging from 0:(K-1).</dd>

<dt>cond</dt>
<dd>Vector of condition index.</dd>

<dt>Scond</dt>
<dd>Vector of new/studied condition index; 0=new, 1=studied.</dd>

<dt>subj</dt>
<dd>Vector of subject index, starting at 0 with no missing
 subject numbers.</dd>

<dt>item</dt>
<dd>Vector of item index, starting at 0 with no missing
 item numbers.</dd>

<dt>lag</dt>
<dd>Vector of lag index.</dd>

<dt>blockN</dt>
<dd>Block of parameters for new-item means.</dd>

<dt>blockS</dt>
<dd>Block of parameters for studied-item means.</dd>

<dt>blockS2</dt>
<dd>Block of parameters for Sigma2 values. If there is only
 one Sigma2 for all participants and items, then the first element of
 blockS2 should contain this value, and the other elements fo blockS2 should
 be zero.</dd>

<dt>crit</dt>
<dd>VECTOR of criteria including -Inf and Inf for top and bottom
critieria, respectively. Vector contains the (K+1) criteria for the
first subjects, followed by those for the second subject, etc.</dd>

</dl>

uvsdLogLike: Function uvsdLogLike

Description

Usage

Value

Arguments

Author

References

See Also