plotLoglik: Traceplot of log-likelihood.

In kshirley/LDAtools: Tools to fit a topic model using Latent Dirichlet Allocation (LDA)

Description

Trace plot of the log-likelihood (the x-axis is MCMC iteration, and the y-axis is the log-likelihood). The MCMC algorithm has converged when this plot levels out and the log-likelihood time series plot looks like white noise. If the log-likelihood is still increasing at the last iteration, you should go back and run the algorithm longer (using fitLDA.)

Usage

plotLoglik(data = matrix(), zoom = FALSE, prop = 0.5, start = 0)

Arguments

data	a matrix with log-likelihood values. Each column of the matrix refers to a different MCMC chain.
zoom	should a second plot of the latter iterations be output.
prop	If zoom == TRUE, this number (between 0 and 1) sets the proportion of final iterations to plot.
start	number of iterations (if any) performed before these chains.

Examples

one.chain <- matrix(rnorm(1000, mean=-500, sd=100), ncol=1) #hopefully our logliklihood is essentially random noise!
plotLoglik(one.chain)
par(mfrow=c(1,2))
plotLoglik(one.chain, zoom=TRUE)
three.chains <- matrix(rnorm(3000, mean=-500, sd=100), ncol=3)
plotLoglik(three.chains)
plotLoglik(three.chains, zoom=TRUE, prop=.1, start=5000)

kshirley/LDAtools documentation built on May 20, 2019, 7:03 p.m.