dpsdPosSample: Function dpsdPosSample

In hbmem: Hierarchical Bayesian Analysis of Recognition Memory

Function dpsdPosSample

Description

Function to sample posterior of DPSD model with positive d'

Usage

dpsdPosSample(dat, M = 5000, keep = (M/10):M, getDIC = TRUE, jump = 0.01)

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 zero and have no gaps (i.e., no skipped subject numbers). Lags must be zero-centered.
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. 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.

hbmem documentation built on Aug. 23, 2023, 1:10 a.m.