## File: olbm.Rd

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r-cran-mcmc 0.9-7-1
 12345678910111213141516171819202122232425262728293031323334353637383940 \name{olbm} \alias{olbm} \title{Overlapping Batch Means} \description{ Variance of sample mean of time series calculated using overlapping batch means. } \usage{ olbm(x, batch.length, demean = TRUE) } \arguments{ \item{x}{a matrix or time series object. Each column of \code{x} is treated as a scalar time series.} \item{batch.length}{length of batches.} \item{demean}{when \code{demean = TRUE} (the default) the sample mean is subtracted from each batch mean when estimating the variance. Using \code{demean = FALSE} would essentially assume the true mean is known to be zero, which might be useful in a toy problem where the answer is known.} } \value{ The estimated variance of the sample mean. } \seealso{ \code{\link{ts}} } \examples{ h <- function(x) if (all(x >= 0) && sum(x) <= 1) return(1) else return(-Inf) out <- metrop(h, rep(0, 5), 1000) out <- metrop(out, scale = 0.1) out <- metrop(out, nbatch = 1e4) foo <- olbm(out$batch, 150) # monte carlo estimates (true means are same by symmetry) apply(out$batch, 2, mean) # monte carlo standard errors (true s. d. are same by symmetry) sqrt(diag(foo)) # check that batch length is reasonable acf(out\$batch, lag.max = 200) } \keyword{ts}