dpsdLogLike {hbmem}R Documentation

Function dpsdLogLike

Description

Computes log likelihood for DPSD model

Usage

dpsdLogLike(R,NN,NS,I,J,K,dat,cond,Scond,sub,item,lag,blockN,blockS,blockR,crit)

Arguments

R Total number of trials.
NN Number of new-item conditions.
NS Number of studied-item conditions.
I Number of subjects.
J Number of items.
K Number of response options.
dat Vector of responses, ranging from 0:(K-1).
cond Vector of condition index.
Scond Vector of new-studied condition index; 0=new, 1=studied.
sub Vector of subject index, starting at 0 with no missing subject numbers.
item Vector of item index, starting at 0 with no missing item numbers.
lag Vector of lag index.
blockN Block of parameters for new-item means.
blockS Block of parameters for studied-item means.
blockR Block of parameters for recollection values.
crit 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.

Value

The function returns the log likelihood.

Author(s)

Michael S. Pratte

References

See Pratte, Rouder, & Morey (2009)

See Also

hbmem


[Package hbmem version 0.2 Index]