predictResponseSurface: Predict the entire response surface, so including on-axis points, and return the result as a matrix. For plotting purposes.

Description

Predict the entire response surface, so including on-axis points, and return the result as a matrix. For plotting purposes.

predictResponseSurface(
  doseGrid,
  fitResult,
  null_model,
  transforms = fitResult$transforms
)

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.
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.
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.