hbmem (version 0.3-3)

dpsdLogLike: Function dpsdLogLike

Description

Computes log likelihood for DPSD model

Usage

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

Value

The function returns the log likelihood.

Arguments

R

Total number of trials.

NN

Number of new-item conditions.

NS

Number of studied-item conditions.

I

Number of subjects.

JN

Number of items in new condition.

JS

Number of items in studied condition.

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.

Author

Michael S. Pratte

References

See Pratte, Rouder, & Morey (2009)

See Also

hbmem