MuSyC_formula: Create a Formula for the MuSyC Synergy Model

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

Create a Formula for the MuSyC Synergy Model

Description

Setup a MuSyC synergy model formula to predict the E0, E1, C1, h1, E2, C2, h2, log10alpha, and E3alpha parameters.

Usage

MuSyC_formula(
  treatment_1_variable = "logd1",
  treatment_1_units = "Log[Molar]",
  treatment_2_variable = "logd2",
  treatment_2_units = "Log[Molar]",
  response_variable = "response",
  response_units = NULL,
  predictors = 1,
  ...
)

Arguments

treatment_1_variable	character variable representing the treatment.
treatment_1_units	character the units of the treatment. The default is log base 10 of the molar concentration
treatment_2_variable	character variable representing the treatment.
treatment_2_units	character the units of the treatment. The default is log base 10 of the molar concentration
response_variable	character variable representing the response to treatment
response_units	character the units of the response
predictors	formula specify predictors of non-linear parameters. i.e. what perturbations/experimental differences should be modeled separately?
...	additional arguments passed to brms::brmsformula()

Value

brms::brmsformula()

Examples

## Not run: 
  # Data has a string column drug_id with drug identifiers
  # Fit a separate model for each drug
  BayesPharma::MuSyC_formula(predictors = 0 + drug_id)

  # Data has a string column plate_id with plate identifiers
  # Estimate the change in response for each plate relative to a global
  # baseline.
  BayesPharma::MuSyC_formula(predictors = plate_id)

  # data has columns drug_id and plate_id
  # fit a multilevel model where the drug effect depends on the plate
  BayesPharma::MuSyC_formula(predictors = 0 + (drug_id|plate_id))

## End(Not run)

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