BIGL (version 1.9.3)

simulateNull: Simulate data from a given null model and monotherapy coefficients

Description

Simulate data from a given null model and monotherapy coefficients

Usage

simulateNull(
  data,
  fitResult,
  doseGrid,
  transforms = fitResult$transforms,
  startvalues,
  null_model = c("loewe", "hsa", "bliss", "loewe2"),
  ...
)

Value

List with data element containing simulated data and

fitResult element containing marginal fit on the simulated data.

Arguments

data

Dose-response dataframe.

fitResult

Monotherapy (on-axis) model fit, e.g. produced by fitMarginals. It has to be a "MarginalFit" object or a list containing df, sigma, coef, shared_asymptote and method elements for, respectively, marginal model degrees of freedom, residual standard deviation, named vector of coefficient estimates, logical value of whether shared asymptote is imposed and method for estimating marginal models during bootstrapping (see fitMarginals). If biological and power transformations were used in marginal model estimation, fitResult should contain transforms elements with these transformations. Alternatively, these can also be specified via transforms argument.

doseGrid

A grid of dose combinations

transforms

Transformation functions. If non-null, transforms is a list containing 5 elements, namely biological and power transformations along with their inverse functions and compositeArgs which is a list with argument values shared across the 4 functions. See vignette for more information.

startvalues

Starting values for the non-linear equation, from the observed data

null_model

Specified null model for the expected response surface. Currently, allowed options are "loewe" for generalized Loewe model, "hsa" for Highest Single Agent model, "bliss" for Bliss additivity, and "loewe2" for the alternative Loewe generalization.

...

Further parameters that will be passed to generateData

Examples

  data <- subset(directAntivirals, experiment == 1)
  ## Data must contain d1, d2 and effect columns
  fitResult <- fitMarginals(data)
  simDat <- simulateNull(data, fitResult, expand.grid(d1 = data$d1, d2 = data$d2),
  null_model = "hsa")