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hbmem (version 0.2)

dpsdRNSample: Fit DPSD model with R restricted to be function of N

Description

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.

Usage

dpsdRNSample(dat, M = 5000, keep = (M/10):M, getDIC = TRUE, jump = 0.001)

Arguments

dat
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 zer
M
Number of MCMC iterations.
keep
Which MCMC iterations should be included in estimates and returned. Use keep to both get ride of burn-in, and thin chains if necessary
getDIC
Logical. Should the function compute DIC value? This takes a while if M is large.
jump
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.

References

Pratte and Rouder (in review)