predictOffAxis {BIGL}

Compute off-axis predictions

Description

Given a dataframe with dose-response data, this function uses coefficient estimates from the marginal (on-axis) monotherapy model to compute the expected values of response at off-axis dose combinations using a provided null model.

Usage

predictOffAxis(
  doseGrid,
  fitResult,
  transforms = fitResult$transforms,
  null_model = c("loewe", "hsa", "bliss", "loewe2"),
  fit = NULL,
  ...
)

Arguments

doseGrid

A dose grid with unique combination of doses

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.

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.

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.

fit

a pre-calculated off-axis fit

...

Further arguments passed on to the Loewe fitters

Value

This functions returns a named vector with predicted off-axis points

Examples

  data <- subset(directAntivirals, experiment == 1)
  ## Data must contain d1, d2 and effect columns
  transforms <- getTransformations(data)
  fitResult <- fitMarginals(data, transforms)
    uniqueDoses <- with(data, list("d1" = sort(unique(data$d1)),
    "d2" = sort(unique(data$d2))))
    doseGrid <- expand.grid(uniqueDoses)
  predictOffAxis(fitResult, null_model = "hsa", doseGrid = doseGrid)