BurnThin: Burning and thinning the MCMC samples.
In gpapadog/BAC: Bayesian Adjustment for Confounding
View source: R/BurnThin_function.R
Description
Burning and thinning the MCMC samples.
Usage
1
BurnThin(bac, burn = 0, thin = 1)
Arguments
bac
The fit from the BAC function.
burn
How many of the initial samples should be dropped. Default is 0.
thin
One every how many samples should be kept. Defaults to 1.
gpapadog/BAC documentation built on Feb. 15, 2021, 6:37 a.m.
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View source: R/BurnThin_function.R
Description
Burning and thinning the MCMC samples.
Usage
1 | BurnThin(bac, burn = 0, thin = 1)
|
Arguments
bac |
The fit from the BAC function. |
burn |
How many of the initial samples should be dropped. Default is 0. |
thin |
One every how many samples should be kept. Defaults to 1. |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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