fitnocorr.mcmc: Run mixed-effects model ('lmer') with MCMC p-value
In dalejbarr/simgen: Monte Carlo Simulations of Simultaneous Generalization

Description Usage Arguments Details Note See Also Examples

Uses lmer to fit a random-intercepts only model, and then uses mcmcsamp to obtain p-values.

1	fitnocorr.mcmc(mcr.data, wsbi = FALSE, nmcmc = 10000)

`mcr.data`	A dataframe formatted as described in `mkDf`.
`wsbi`	Whether the design is between-items (TRUE) or within-items (FALSE).
`nmcmc`	Number of Markov-Chain Monte Carlo simulations (default = 10000).

If the model does not converge, returns NA. The MCMC procedure is based on Baayen's pvals.fnc in package languageR.

This function no longer works and will throw an error, as the mcmcsamp function was removed starting with lme4 package 1.0; see mcmcsamp for details. slopes.

fitnocorr

nmc <- 10
pmx <- cbind(randParams(genParamRanges(), nmc, 1001), seed=mkSeeds(nmc, 1001))

# between-items dataset
x.bi <- mkDf(nsubj=24, nitem=24, mcr.params=pmx[1,], wsbi=TRUE)

# NB: small number of MCMC runs so that the example runs quickly
# increase the number of runs for stable results
fitnocorr.mcmc(x.bi, wsbi=TRUE, nmcmc=1000)