Extract Diagnostic Quantities of OncoBayes2 Models

Extract quantities that can be used to diagnose sampling behavior of the algorithms applied by Stan at the back-end of OncoBayes2.

Usage

# S3 method for class 'blrmfit'
log_posterior(object, ...)

# S3 method for class 'blrmfit'
nuts_params(object, pars = NULL, ...)

# S3 method for class 'blrmfit'
rhat(object, pars = NULL, ...)

# S3 method for class 'blrmfit'
neff_ratio(object, pars = NULL, ...)

Arguments

object

A blrmfit or blrmtrial object.

...

Arguments passed to individual methods.

pars

An optional character vector of parameter names. For nuts_params these will be NUTS sampler parameter names rather than model parameters. If pars is omitted all parameters are included.

Value

The exact form of the output depends on the method.

Details

For more details see bayesplot::bayesplot-extractors().

Examples

.user_mc_options <- options()

example_model("single_agent", silent = TRUE)

head(log_posterior(blrmfit))
#>   Chain Iteration     Value
#> 1     1         1 -13.30890
#> 2     1         2 -14.46850
#> 3     1         3 -14.05445
#> 4     1         4 -13.82799
#> 5     1         5 -15.67133
#> 6     1         6 -15.40357

np <- nuts_params(blrmfit)
str(np)
#> 'data.frame':	24000 obs. of  4 variables:
#>  $ Chain    : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ Iteration: int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ Parameter: Factor w/ 6 levels "accept_stat__",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ Value    : num  1 0.988 0.999 0.997 0.989 ...
# extract the number of divergence transitions
sum(subset(np, Parameter == "divergent__")$Value)
#> [1] 0

head(rhat(blrmfit))
#>                     log_beta_raw[1,1,1]                     log_beta_raw[2,1,1] 
#>                               1.0002393                               1.0022189 
#>                     log_beta_raw[1,1,2]                     log_beta_raw[2,1,2] 
#>                               0.9996421                               1.0021369 
#> mu_log_beta[log(drug_A/dref),intercept] mu_log_beta[log(drug_A/dref),log_slope] 
#>                               1.0013644                               1.0011005 
head(neff_ratio(blrmfit))
#>                     log_beta_raw[1,1,1]                     log_beta_raw[2,1,1] 
#>                               1.2565507                               1.1143313 
#>                     log_beta_raw[1,1,2]                     log_beta_raw[2,1,2] 
#>                               1.1332693                               1.0414032 
#> mu_log_beta[log(drug_A/dref),intercept] mu_log_beta[log(drug_A/dref),log_slope] 
#>                               0.6854192                               0.7189484 

## Recover user set sampling defaults
options(.user_mc_options)