dpsdLogLike {hbmem} | R Documentation |
Computes log likelihood for DPSD model
dpsdLogLike(R,NN,NS,I,J,K,dat,cond,Scond,sub,item,lag,blockN,blockS,blockR,crit)
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. |
The function returns the log likelihood.
Michael S. Pratte
See Pratte, Rouder, & Morey (2009)
hbmem