This is a dual process model in which the person and item effects on probability of recollection are linear functions of those effects for the new-item distributuion.
dpsdRNSample(dat, M = 5000, keep = (M/10):M, getDIC = TRUE, jump = 0.001)
Data frame that must include variables Scond,cond,sub,item,lag,resp. Scond indexes studied/new, whereas cond indexes conditions nested within the studied or new conditions. Indexes for Scond,cond, sub, item, and respone must start at zero and have no gaps (i.e., no skipped subject numbers). Lags must be zero-centered.
Number of MCMC iterations.
Which MCMC iterations should be included in estimates and returned. Use keep to both get ride of burn-in, and thin chains if necessary
Logical. Should the function compute DIC value? This takes a while if M is large.
The criteria and decorrelating steps utilize Matropolis-Hastings sampling routines, which require tuning. All MCMC functions should self-tune during the burnin period (iterations before keep), and they will alert you to the success of tuning. If acceptance rates are too low, "jump" should be decreased, if they are too hight, "jump" should be increased. Alternatively, or in addition to adjusting "jump", simply increase the burnin period which will allow the function more time to self-tune.
Pratte and Rouder (2010)