# Geweke's convergence diagnostic Geweke (1992) proposed a convergence diagnostic for Markov chains based on a test for equality of the means of the first and last part of a Markov chain (by default the first 10% and the last 50%). If the samples are drawn from the stationary distribution of the chain, the two means are equal and Geweke's statistic has an asymptotically standard normal distribution. The test statistic is a standard Z-score: the difference between the two sample means divided by its estimated standard error. The standard error is estimated from the spectral density at zero and so takes into account any autocorrelation. The Z-score is calculated under the assumption that the two parts of the chain are asymptotically independent, which requires that the sum of `frac1` and `frac2` be strictly less than 1. Adapted from the `geweke.diag` function of the coda package which passes `mcmc` objects as arguments rather than matrices. ```r gewekediag(x, frac1=0.1, frac2=0.5) ``` ## Arguments - `x`: Matrix of MCMC chains: the rows are the samples and the columns are different "parameters". For BART, generally, the columns are estimates of $f$. For `pbart`, they are different subjects. For `surv.bart`, they are different subjects at a grid of times. - `frac1`: fraction to use from beginning of chain - `frac2`: fraction to use from end of chain ## Returns Z-scores for a test of equality of means between the first and last parts of the chain. A separate statistic is calculated for each variable in each chain. ## See Also `spectrum0ar`. ## References Geweke J. (1992) Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments. In JM Bernado, JO Berger, AP Dawid, AFM Smith (eds.), Bayesian Statistics 4, pp. 169-193. Oxford University Press, Oxford. Plummer M., Best N., Cowles K. and Vines K. (2006) CODA: Convergence Diagnosis and Output Analysis for MCMC. R News, vol 6, 7-11. ## Examples ```r ## load survival package for the advanced lung cancer example data(lung) group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno times <- lung[group, 2] ##lung$time delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead ##delta: 0=censored, 1=dead ## this study reports time in days rather than months like other studies ## coarsening from days to months will reduce the computational burden times <- ceiling(times/30) summary(times) table(delta) x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates ## lung$age: Age in years ## lung$sex: Male=1 Female=2 ## lung$ph.karno: Karnofsky performance score (dead=0:normal=100:by=10) ## rated by physician dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)') summary(x.train[ , 1]) table(x.train[ , 2]) table(x.train[ , 3]) x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios dimnames(x.test)[[2]] <- dimnames(x.train)[[2]] i <- 1 for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) { x.test[i, ] <- c(age, sex, ph.karno) i <- i+1 } ## Not run: set.seed(99) post <- surv.bart(x.train=x.train, times=times, delta=delta, x.test=x.test) ## in the interest of time, consider speeding it up by parallel processing ## run "mc.cores" number of shorter MCMC chains in parallel processes ## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta, ## x.test=x.test, mc.cores=8, seed=99) N <- nrow(x.test) K <- post$K ## select 10 lung cancer patients uniformly spread out over the data set h <- seq(1, N*K, floor(N/10)*K) for(i in h) { post.mcmc <- post$yhat.test[ , (i-1)+1:K] z <- gewekediag(post.mcmc)$z y <- max(c(4, abs(z))) ## plot the z scores vs. time for each patient if(i==1) plot(post$times, z, ylim=c(-y, y), type='l', xlab='t', ylab='z') else lines(post$times, z, type='l') } ## add two-sided alpha=0.05 critical value lines lines(post$times, rep(-1.96, K), type='l', lty=2) lines(post$times, rep( 1.96, K), type='l', lty=2) ## End(Not run) ```

gewekediag function