# Summarize the result of a mcmcComposite object Summary for the result of a mcmcComposite object. ```r ## S3 method for class 'mcmcComposite' summary(object, chain = NULL, ...) ``` ## Arguments - `object`: A mcmcComposite object - `chain`: The chain to use - `...`: Not used ## Returns A summary of the result ## Details summary.mcmcComposite get info on the result of a mcmcComposite object ## Examples ```r ## Not run: library(HelpersMG) require(coda) x <- rnorm(30, 10, 2) dnormx <- function(data, x) { data <- unlist(data) return(-sum(dnorm(data, mean=x['mean'], sd=x['sd'], log=TRUE))) } parameters_mcmc <- data.frame(Density=c('dnorm', 'dlnorm'), Prior1=c(10, 0.5), Prior2=c(2, 0.5), SDProp=c(1, 1), Min=c(-3, 0), Max=c(100, 10), Init=c(10, 2), stringsAsFactors = FALSE, row.names=c('mean', 'sd')) mcmc_run <- MHalgoGen(n.iter=1000, parameters=parameters_mcmc, data=x, likelihood=dnormx, n.chains=1, n.adapt=100, thin=1, trace=1) plot(mcmc_run, xlim=c(0, 20)) plot(mcmc_run, xlim=c(0, 10), parameters="sd") mcmcforcoda <- as.mcmc(mcmc_run) #' heidel.diag(mcmcforcoda) raftery.diag(mcmcforcoda) autocorr.diag(mcmcforcoda) acf(mcmcforcoda[[1]][,"mean"], lag.max=20, bty="n", las=1) acf(mcmcforcoda[[1]][,"sd"], lag.max=20, bty="n", las=1) batchSE(mcmcforcoda, batchSize=100) # The batch standard error procedure is usually thought to # be not as accurate as the time series methods used in summary summary(mcmcforcoda)$statistics[,"Time-series SE"] summary(mcmc_run) as.parameters(mcmc_run) lastp <- as.parameters(mcmc_run, index="last") parameters_mcmc[,"Init"] <- lastp # The n.adapt set to 1 is used to not record the first set of parameters # then it is not duplicated (as it is also the last one for # the object mcmc_run) mcmc_run2 <- MHalgoGen(n.iter=1000, parameters=parameters_mcmc, data=x, likelihood=dnormx, n.chains=1, n.adapt=1, thin=1, trace=1) mcmc_run3 <- merge(mcmc_run, mcmc_run2) ####### no adaptation, n.adapt must be 0 parameters_mcmc[,"Init"] <- c(mean(x), sd(x)) mcmc_run3 <- MHalgoGen(n.iter=1000, parameters=parameters_mcmc, data=x, likelihood=dnormx, n.chains=1, n.adapt=0, thin=1, trace=1) ## End(Not run) ``` ## See Also Other mcmcComposite functions: `MHalgoGen()`, `as.mcmc.mcmcComposite()`, `as.parameters()`, `as.quantiles()`, `merge.mcmcComposite()`, `plot.PriorsmcmcComposite()`, `plot.mcmcComposite()`, `setPriors()` ## Author(s) Marc Girondot marc.girondot@gmail.com

summary.mcmcComposite function