Stub example for mcmc_ functions

Suppose you generate some MCMC samples of params

library(hayeslib)

## Welcome to hayeslib.

library(tidyverse)  # hack for now

## -- Attaching packages --------------------- tidyverse 1.2.1 --

## v ggplot2 2.2.1     v purrr   0.2.4
## v tibble  1.3.4     v dplyr   0.7.4
## v tidyr   0.7.2     v stringr 1.2.0
## v readr   1.1.1     v forcats 0.2.0

## Warning: package 'tidyr' was built under R version 3.4.2

## Warning: package 'purrr' was built under R version 3.4.2

## Warning: package 'dplyr' was built under R version 3.4.2

## -- Conflicts ------------------------ tidyverse_conflicts() --
## x dplyr::combine() masks hayeslib::combine()
## x dplyr::filter()  masks stats::filter()
## x dplyr::lag()     masks stats::lag()

library(coda)

num_samples <- 10^3

postr <- tibble(alpha = rnorm(num_samples),
                beta = rnorm(num_samples))

postr %>% 
  mcmc_trace()

Get it to a form that nice for easy mapping

postr %>% 
  t_mcmc()

## # A tibble: 2 x 2
##   param       samples
##   <chr>        <list>
## 1 alpha <dbl [1,000]>
## 2  beta <dbl [1,000]>

MCMC simple summary for plotting

postr %>% 
  t_mcmc() %>% 
  mcmc_stats()

## # A tibble: 2 x 5
##   param       samples        mean        lo       hi
##   <chr>        <list>       <dbl>     <dbl>    <dbl>
## 1 alpha <dbl [1,000]> -0.03262095 -1.700069 2.036954
## 2  beta <dbl [1,000]> -0.02757512 -2.106014 1.813507

Do the plotting

postr %>% 
  t_mcmc() %>% 
  mcmc_stats() %>% 
  mcmc_hpd_int()