BIGL (version 1.5.3)

predictOffAxis: 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(
  data,
  fitResult,
  transforms = fitResult$transforms,
  null_model = c("loewe", "hsa", "bliss", "loewe2"),
  ...
)

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.

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.

...

Further arguments that are currently unused

Value

This functions returns a list with 3 elements.

"offaxisZTable" is a dataframe containing dose levels, observed effects and effects predicted according to the specified null model. This dataframe also contains replicates, if there are any.

"predSurface" are the predicted effects (without replicates) according to the specified null model. These effects are arranged in a matrix form so that each direction of the matrix rightward or downward corresponds to increasing dose of one of the compounds.

"occupancy" contains occupancy levels at each dose combination as (always) computed by generalized Loewe model.

Examples

# NOT RUN {
  data <- subset(directAntivirals, experiment == 1)
  ## Data must contain d1, d2 and effect columns
  transforms <- getTransformations(data)
  fitResult <- fitMarginals(data, transforms)
  predictOffAxis(data, fitResult, null_model = "hsa")
# }