Resample draws
objects according to provided weights, for example weights
obtained through importance sampling.
resample_draws(x, ...) # S3 method for draws resample_draws(x, weights = NULL, method = "stratified", ndraws = NULL, ...)
x  (draws) A 

...  Arguments passed to individual methods (if applicable). 
weights  (numeric vector) A vector of positive weights of length

method  (string) The resampling method to use:
Currently, 
ndraws  (positive integer) The number of draws to be returned. By
default 
A draws
object of the same class as x
.
Upon usage of resample_draws()
, chains will automatically be merged
due to subsetting of individual draws (see subset_draws
for details).
Also, weights stored in the draws
object will be removed in the process,
as resampling invalidates existing weights.
Kitagawa, G., Monte Carlo Filter and Smoother for NonGaussian Nonlinear ' State Space Models, Journal of Computational and Graphical Statistics, 5(1):125, 1996.
resample_draws()
x < as_draws_df(example_draws()) # random weights for justr for demonstration w < runif(ndraws(x), 0, 10) # use default stratified sampling x_rs < resample_draws(x, weights = w)#>#> # A tibble: 10 x 7 #> variable mean median sd mad q5 q95 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 mu 4.24 4.59 3.45 3.25 1.17 9.48 #> 2 tau 4.54 3.49 3.81 3.29 0.307 11.7 #> 3 theta[1] 7.08 6.23 6.51 5.22 1.16 19.3 #> 4 theta[2] 5.37 5.13 4.94 4.03 2.32 14.2 #> 5 theta[3] 2.96 4.13 7.31 5.22 11.1 12.0 #> 6 theta[4] 5.10 5.24 5.01 4.32 3.63 12.3 #> 7 theta[5] 2.89 3.57 5.35 4.54 7.60 10.8 #> 8 theta[6] 3.88 4.34 5.34 5.00 6.04 11.1 #> 9 theta[7] 6.88 6.73 5.29 4.60 1.15 15.4 #> 10 theta[8] 4.60 4.73 5.71 5.23 5.31 14.5# use simple random sampling x_rs < resample_draws(x, weights = w, method = "simple")#>#> # A tibble: 10 x 7 #> variable mean median sd mad q5 q95 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 mu 3.95 4.08 3.51 4.00 1.16 8.74 #> 2 tau 4.25 3.18 3.66 2.66 0.332 11.4 #> 3 theta[1] 7.04 6.06 6.71 5.72 1.19 20.1 #> 4 theta[2] 4.66 4.73 4.68 4.51 2.60 12.4 #> 5 theta[3] 2.77 4.16 7.50 5.46 11.1 11.6 #> 6 theta[4] 4.99 5.50 4.89 4.49 2.65 12.2 #> 7 theta[5] 2.66 3.49 5.34 4.86 6.86 10.6 #> 8 theta[6] 3.71 4.07 5.14 4.92 4.98 10.8 #> 9 theta[7] 6.45 5.92 5.34 4.60 1.43 14.8 #> 10 theta[8] 4.34 4.76 5.56 5.22 5.29 13.2