Utility operations for mcmc.list objects

colMeans.mcmc.list is a "method" for (non-generic) colMeans() applicable to mcmc.list objects.

var.mcmc.list is a "method" for (non-generic) var() applicable to mcmc.list objects. Since MCMC chains are assumed to all be sampling from the same underlying distribution, their pooled mean is used.

sweep.mcmc.list is a "method" for (non-generic) sweep() applicable to mcmc.list objects.

lapply.mcmc.list is a "method" for (non-generic) lapply() applicable to mcmc.list objects.

colMeans.mcmc.list(x, ...)

var.mcmc.list(x, ...)

sweep.mcmc.list(x, STATS, FUN = "-", check.margin = TRUE, ...)

lapply.mcmc.list(X, FUN, ...)

Arguments

x

a mcmc.list object.

...

additional arguments to the functions evaluated on each chain.

STATS, FUN, check.margin

See help for sweep().

X

An mcmc.list object.

Value

colMeans.mcmc returns a vector with length equal to the number of mcmc chains in x with the mean value for each chain.

sweep.mcmc.list returns an appropriately modified version of x

lapply.mcmc.list returns an mcmc.list each of whose chains had been passed through FUN.

Details

These implementations should be equivalent (within numerical error) to the same function being called on as.matrix(x), while avoiding construction of the large matrix.

See also

mcmc.list

colMeans()

var()

sweep()

lapply()

Examples

data(line, package="coda")
colMeans(as.matrix(line)) # also coda
#>     alpha      beta     sigma 
#> 2.9875644 0.7991864 0.9680519 
colMeans.mcmc.list(line) # "Method"
#>     alpha      beta     sigma 
#> 2.9875644 0.7991864 0.9680519 
data(line, package="coda")
var(as.matrix(line)) # coda
#>             alpha        beta       sigma
#> alpha  0.24839754 -0.02399442  0.05650231
#> beta  -0.02399442  0.11335568 -0.05193148
#> sigma  0.05650231 -0.05193148  0.54952775
var.mcmc.list(line) # "Method"
#>             alpha        beta       sigma
#> alpha  0.24839754 -0.02399442  0.05650231
#> beta  -0.02399442  0.11335568 -0.05193148
#> sigma  0.05650231 -0.05193148  0.54952775
data(line, package="coda")
colMeans.mcmc.list(line)-1:3
#>     alpha      beta     sigma 
#>  1.987564 -1.200814 -2.031948 
colMeans.mcmc.list(sweep.mcmc.list(line, 1:3))
#>     alpha      beta     sigma 
#>  1.987564 -1.200814 -2.031948 
data(line, package="coda")
colMeans.mcmc.list(line)[c(2,3,1)]
#>      beta     sigma     alpha 
#> 0.7991864 0.9680519 2.9875644 
colMeans.mcmc.list(lapply.mcmc.list(line, `[`,,c(2,3,1)))
#>      beta     sigma     alpha 
#> 0.7991864 0.9680519 2.9875644