In maomlab/BayesPharma: Tools for Bayesian Analysis of Non-Linear Pharmacology Models

## here() starts at /Users/maom/opt/bayespharma

## Error in library(tidymodels): there is no package called 'tidymodels'

## Loading required package: brms

## Loading required package: Rcpp

## Loading 'brms' package (version 2.21.0). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').

## 
## Attaching package: 'brms'

## The following object is masked from 'package:bayesplot':
## 
##     rhat

## The following object is masked from 'package:stats':
## 
##     ar

## Loading required package: parsnip

Using BayesPharma models with Tidymodels

Tidymodels is a platform for running and analyzing models in R. This vignette demonstrates how to use the BayesPharma models with Tidy Models.

load(here::here("data", "kor_antag.rda"))
kor_antag <- kor_antag |> dplyr::transmute(
  substance_id = Drug,
  cell_id = cell_number,
  log_dose = log_dose,
  response = normalized_measurement)

Define a recipe and assign roles to different columns

kor_recipe <- kor_antag |>
  recipes::recipe() |>
  recipes::update_role(response, new_role = "outcome") |>
  recipes::update_role(log_dose, new_role = "predictor") |>
  recipes::update_role(substance_id, new_role = "predictor")

Define the model using the \code{bayesian} wrapper package for Tidymodels.

kor_model <- bayesian::bayesian(
  cores = 4,
  chains = 4,
  iter = 2000,
  control = list(adapt_delta = 0.99),
  stanvars = BayesPharma::sigmoid_stanvar(),
  init = BayesPharma::sigmoid_antagonist_init(top = 100),
  prior = BayesPharma::sigmoid_antagonist_prior(top = 100)) |>
  parsnip::set_engine("brms") |>
  parsnip::set_mode("regression") |>
  recipes::update(
    formula.override = bayesian::bayesian_formula(
      ..y ~ sigmoid(ic50, hill, top, bottom, log_dose),
      ic50 + hill + top + bottom ~ 0 + substance_id,
      nl = TRUE))

Build and run the workflow by adding the recipe and model and then running fit.

kor_workflow <- workflows::workflow() |>
  workflows::add_recipe(kor_recipe) |>
  workflows::add_model(spec = kor_model) |>
  parsnip::fit(data = kor_antag)

## Compiling Stan program...

## Start sampling

## Warning in .local(object, ...): some chains had errors; consider specifying
## chains = 1 to debug

## here are whatever error messages were returned

## [[1]]
## Stan model 'anon_model' does not contain samples.
## 
## [[2]]
## Stan model 'anon_model' does not contain samples.
## 
## [[3]]
## Stan model 'anon_model' does not contain samples.
## 
## [[4]]
## Stan model 'anon_model' does not contain samples.

kor_workflow

## ══ Workflow [trained] ════════════════════════════════════════════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: bayesian()
## 
## ── Preprocessor ──────────────────────────────────────────────────────────────────────────────────────────────────────
## 0 Recipe Steps
## 
## ── Model ─────────────────────────────────────────────────────────────────────────────────────────────────────────────
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: ..y ~ sigmoid(ic50, hill, top, bottom, log_dose) 
##          ic50 ~ 0 + substance_id
##          hill ~ 0 + substance_id
##          top ~ 0 + substance_id
##          bottom ~ 0 + substance_id
##    Data: ~data (Number of observations: 73) 
## 
## The model does not contain posterior draws.

maomlab/BayesPharma documentation built on Aug. 24, 2024, 8:45 a.m.