generateData {BIGL}  R Documentation 
This function is used to generate data for bootstrapping of the null distribution for various estimates. Optional arguments such as specific choice of sampling vector or corrections for heteroskedasticity can be specified in the function arguments.
generateData(
pars,
sigma,
data = NULL,
transforms = NULL,
null_model = c("loewe", "hsa", "bliss", "loewe2"),
error = 1,
sampling_errors = NULL,
means = NULL,
model = NULL,
method = "equal",
wild_bootstrap = FALSE,
rescaleResids,
invTransFun,
newtonRaphson = FALSE,
bootmethod = method,
...
)
pars 
Coefficients of the marginal model along with their appropriate
naming scheme. These will typically be estimated using

sigma 
Standard deviation to use for randomly generated error terms. This
argument is unused if 
data 
Data frame with dose columns 
transforms 
Transformation functions. If nonnull, 
null_model 
Specified null model for the expected response surface.
Currently, allowed options are 
error 
Type of error for resampling. 
sampling_errors 
Sampling vector to resample errors from. Used only if

means 
The vector of mean values of the response surface, for variance modelling 
model 
The meanvariance model 
method 
What assumption should be used for the variance of on and
offaxis points. This argument can take one of the values from

wild_bootstrap 
Whether special bootstrap to correct for
heteroskedasticity should be used. If 
rescaleResids 
a boolean indicating whether to rescale residuals, or else normality of the residuals is assumed. 
invTransFun 
the inverse transformation function, back to the variance domain 
newtonRaphson 
A boolean, should NewtonRaphson be used to find Loewe response surfaces? May be faster but also less stable to switch on 
bootmethod 
The resampling method to be used in the bootstraps. Defaults to the same as method 
... 
Further arguments 
Doseresponse dataframe with generated data including "effect"
as well as "d1"
and "d2"
columns.
coefs < c("h1" = 1, "h2" = 1.5, "b" = 0,
"m1" = 1, "m2" = 2, "e1" = 0.5, "e2" = 0.1)
## Dose levels are set to be integers from 0 to 10
generateData(coefs, sigma = 1)
## Dose levels are taken from existing dataset with d1 and d2 columns
data < subset(directAntivirals, experiment == 1)
generateData(data = data[, c("d1", "d2")], pars = coefs, sigma = 1)