simulateNull {BIGL}  R Documentation 
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"),
...
)
Arguments
data 
Doseresponse dataframe.

fitResult 
Monotherapy (onaxis) 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 nonnull, 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 nonlinear 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

Value
List with data
element containing simulated data and
fitResult
element containing marginal fit on the simulated data.
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")
[Package
BIGL version 1.6.6
Index]