pkgdown/index.md

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

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Bayesian Pharmacology Modeling

Introduction

The BayesPharma package contains a collection of R tools for analyzing pharmacology data using Bayesian statistics and modeling. In comparison to likelihood-based inference, BayesPharma builds on the stan ecosystem and brms package. BayesPharma facilitates applying a principled Bayesian workflow to to fit and analyze several foundational pharmacology models, such as dose-response modeling, modeling Pnear and folding funnels from molecular modeling, and modeling potential docking 'hit-rate' curves as a function of dock score from ultra-large library docking (Lyu et al. (2019), Alon et al. (2021)).

Installation

Pre-requisites

Follow the instructions to install rstan

Install BayesPharma

In R do

::: {.cell}

```{.r .cell-code} install.packages("remotes") remotes::install_github("maomlab/BayesPharma")

:::


Usage
-----

::: {.cell}

```{.r .cell-code}
library(tidyverse)
library(BayesPharma)

data <- data.frame(
  response = ...,
  treatment = ...,
  <predictor columns>)

:::

The predictor columns are typically treatment variables like drug or batch variable like well_id.

To use the model to, for example fit a sigmoid agonist model:

::: {.cell}

```{.r .cell-code} model <- BayesPharma::sigmoid_model( data = data, formula = BayesPharma::sigmoid_agoinst_formula(), prior = BayesPharma::sigmoid_agonsit_prior(), init = BayesPharma::sigmoid_agonist_init())

:::

### Evaluate model fit

Once the model has been fit, to evaluate it 

#### Traceplot

::: {.cell}

```{.r .cell-code}
model |> BayesPharma::traceplot()

:::

Basic statistics

::: {.cell}

```{.r .cell-code} model |> posterior::summarize_draws()

:::

#### Regression plot

::: {.cell}

```{.r .cell-code}
model |> BayesPharma::plot_posterior_draws()

:::

Prior densities

::: {.cell}

```{.r .cell-code} model |> BayesPharma::density_distributions() model |> BayesPharma::posterior_densities() model |> BayesPharma::prior_posterior_densities()

:::

#### posterior predictive check

::: {.cell}

```{.r .cell-code}
model |> brms::pp_check(type = "dens_overlay", ndraws = 50)

:::

compare model fits

::: {.cell}

{.r .cell-code} model <- model |> brms::add_loo_criterion() model_fit_comparison <- brms::compare_models(model, model_alt) :::

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