uvsdSample {hbmem} | R Documentation |
Runs MCMC for the hierarchical UVSD model
uvsdSample(dat, M = 10000, keep = (M/10):M, getDIC = TRUE, freeCrit=TRUE, equalVar=FALSE, freeSig2=FALSE, jump=.01)
dat |
Data frame that must include variables cond,sub,item,lag,resp. Indexes for cond, sub, item, and respone must start at zero and have no gapes (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. |
freeCrit |
Logical. If TRUE (default) individual criteria vary across people. If false, all participants have the same criteria (but note that overall response biases are still modeled in the means) |
equalVar |
Logical. If FALSE (default), unequal-variance model is fit. If TRUE, equal-variance model is fit. |
freeSig2 |
Logical. If FALSE (default), one sigma is fit for all participants and items (as in Pratte, et al., 2009). If TRUE, then an additive model is placed on the log of sigma2. |
jump |
The criteria and decorrelating steps utilize Matropolis-Hastings sampling routines, which require tuning. All MCMC functions should self tune during the burnin perior (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. |
The function returns an internally defined "uvsd" structure that includes the following components
mu |
Indexes which element of blocks contain mu |
alpha |
Indexes which element of blocks contain participant effects, alpha |
beta |
Indexes which element of blocks contain item effects, beta |
s2alpha |
Indexes which element of blocks contain variance of participant effects (alpha). |
s2beta |
Indexes which element of blocks contain variance of item effects (beta). |
theta |
Indexes which element of blocks contain theta, the slope of the lag effect |
estN |
Posterior means of block parameters for new-item means |
estS |
Posterior means of block parameters for studied-item means |
estS2 |
Posterior means of block for studied-item variances. |
estCrit |
Posterior means of criteria |
blockN |
Each iteration for each parameter in the new-item mean block. Rows index iteration, columns index parameter. |
blockS |
Same as blockN, but for the studied-item means |
blockS2 |
Same as blockN, but for variances of studied-item distribution. If equalVar=TRUE, then these values are all zero. If UVSD is fit but freeSig2=FALSE, then only the first element is non-zero (mu). |
s.crit |
Samples of each criteria. |
pD |
Number of effective parameters used in DIC. Note that this should be smaller than the actual number of parameters, as constraint from the hierarchical structure decreases the number of effective parameters. |
DIC |
DIC value. Smaller values indicate better fits. Note that DIC is notably biased toward complexity. |
M |
Number of MCMC iterations run |
keep |
MCMC iterations that were used for estimation and returned |
b0 |
Metropolis-Hastings acceptance rates for decorrelating steps. These should be between .2 and .6. If they are not, the M, keep, or jump need to be adjusted. |
b0S2 |
If additive model is placed on Sigma2 (i.e., freeSigma2=TRUE), then all parameters on S2 must be tuned. b0S2 are the acceptance probabilities for these parameters. |
Michael S. Pratte
See Pratte, Rouder, & Morey (2009)
hbmem
#In this example we generate data from EVSD, then fit it with both #hierarchical UVSD and UVSD assuming no participant or item effects. #Note that there is variance in the studied-item distribution, but not #the new-item distribution. library(hbmem) sim=uvsdSim(I=70,J=400,s2aN = 0, s2bN = 0,s2aS=.25,s2bS=.25) dat=as.data.frame(cbind(sim@subj,sim@item,sim@cond,sim@lag,sim@resp)) colnames(dat)=c("sub","item","cond","lag","resp") M=100 UVSD=uvsdSample(dat,M=M) #Now make data frame for data-averaged analysis adat=dat adat$sub=0 adat$item=0 aUVSD=uvsdSample(adat,M=M,freeCrit=FALSE) par(mfrow=c(1,2)) plot(sqrt(exp(UVSD@blockS2[,UVSD@mu])),t='l',main="Hierarchical") plot(sqrt(exp(aUVSD@blockS2[,aUVSD@mu])),t='l',main="Data-Averaged")