kmbayes_continue {bkmrhat} | R Documentation |

## Continue sampling from existing bkmr fit

### Description

Use this when you've used MCMC sampling with the `kmbayes`

function, but you did not take enough samples and do not want to start over.

### Usage

kmbayes_continue(fit, ...)

### Arguments

### Details

Note this does not fully start from the prior values of the MCMC chains. The `kmbayes`

function does not allow full specification of the kernel function parameters, so this will restart the chain at the last values of all fixed effect parameters, and start the kernel `r`

parmeters at the arithmetic mean of all `r`

parameters from the last step in the previous chain.

### Value

a `bkmrfit.continued`

object, which inherits from `bkmrfit`

objects similar to `kmbayes`

output, and which can be used to make inference using functions from the `bkmr`

package.

### See Also

`kmbayes_parallel`

### Examples

set.seed(111)
dat <- bkmr::SimData(n = 50, M = 4)
y <- dat$y
Z <- dat$Z
X <- dat$X
## Not run:
fitty1 = bkmr::kmbayes(y=y,Z=Z,X=X, est.h=TRUE, tier=100)
# do some diagnostics here to see if 100 iterations (default) is enough
# add 100 additional iterations (for illustration - still will not be enough)
fitty2 = kmbayes_continue(fitty1, iter=100)
cobj = as.mcmc(fitty2)
varnames(cobj)
## End(Not run)

[Package

*bkmrhat* version 1.0.2

Index]