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Runs example models

Source: R/example_model.R
example_model.Rd

Runs example models

Usage

example_model(topic, envir = parent.frame(), silent = FALSE)

Arguments

topic

example to run

envir

environment which the example is loaded into. Defaults to the caller environment.

silent

logical controlling if execution is run silently (defaults to FALSE)

Value

When topic is not specified a list of all possible topics is return. Whenever a valid topic is specified, the function inserts the example into the environment given and returns (invisibly) the updated environment.

Examples

## Setting up dummy sampling for fast execution of example
## Please use 4 chains and 100x more warmup & iter in practice
.user_mc_options <- options(
  OncoBayes2.MC.warmup = 10, OncoBayes2.MC.iter = 20, OncoBayes2.MC.chains = 1,
  OncoBayes2.MC.save_warmup = FALSE
)


## get a list of available examples
example_model()
#> [1] "combo2_trial" "combo2"       "combo3"       "single_agent"

## run 3 component example
example_model("combo3")
#> Running combo3 example:
#>  ## example combo3
#> 
#>  library(abind)
#> 
#>  dref <- c(500, 500, 1000)
#>  num_comp <- 3
#>  num_inter <- choose(3, 2) + 1
#>  num_strata <- nlevels(hist_combo3$stratum_id)
#>  num_groups <- nlevels(hist_combo3$group_id)
#> 
#>  blrmfit <- blrm_exnex(
#>    cbind(num_toxicities, num_patients - num_toxicities) ~
#>      1 + I(log(drug_A / dref[1])) |
#>        1 + I(log(drug_B / dref[2])) |
#>        1 + I(log(drug_C / dref[3])) |
#>        0
#>        + I(drug_A / dref[1] * drug_B / dref[2])
#>          + I(drug_A / dref[1] * drug_C / dref[3])
#>          + I(drug_B / dref[2] * drug_C / dref[3])
#>          + I(drug_A / dref[1] * drug_B / dref[2] * drug_C / dref[3]) |
#>        stratum_id / group_id,
#>    data = hist_combo3,
#>    prior_EX_mu_comp = replicate(num_comp, mixmvnorm(c(1, logit(1/3), 0, diag(c(2^2, 1)))), FALSE),
#>    prior_EX_tau_comp = list(replicate(num_comp,
#>                                       mixmvnorm(c(1, log(c(0.25, 0.125)),
#>                                                 diag(c(log(4)/1.96, log(4)/1.96)^2))), FALSE),
#>                             replicate(num_comp,
#>                                       mixmvnorm(c(1, log(2 * c(0.25, 0.125)),
#>                                                 diag(c(log(4)/1.96, log(4)/1.96)^2))), FALSE)),
#>    prior_EX_mu_inter = mixmvnorm(c(1, rep.int(0, num_inter),
#>                                       diag((rep.int(sqrt(2) / 2, num_inter))^2))),
#>    prior_EX_tau_inter = replicate(num_strata,
#>                                   mixmvnorm(c(1, rep.int(log(0.25), num_inter),
#>                                               diag((rep.int(log(2) / 1.96, num_inter))^2))), FALSE),
#>    prior_EX_prob_comp = matrix(0.9, nrow = num_groups, ncol = num_comp),
#>    prior_EX_prob_inter = matrix(1.0, nrow = num_groups, ncol = num_inter),
#>    prior_is_EXNEX_comp = rep(TRUE, num_comp),
#>    prior_is_EXNEX_inter = rep(FALSE, num_inter),
#>    prior_tau_dist = 1,
#>    prior_PD = FALSE
#>  )
#> Warning: The largest R-hat is NA, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess

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