Obtain confidence intervals for the raw effect sizes on every off-axis point and overall
bootConfInt(
Total,
idUnique,
bootStraps,
transforms,
respS,
B.B,
method,
CP,
reps,
n1,
cutoff,
R,
fitResult,
bootRS,
data_off,
posEffect = all(Total$effect >= 0),
transFun,
invTransFun,
model,
rescaleResids,
wild_bootstrap,
wild_bootType,
control,
digits,
...
)A list with components
The off-axis bootstrapped confidence intervals
A mean effect and percentile and studentized boostrap intervals
data frame with all effects and mean effects
unique combinations of on-axis points, a character vector
precomputed bootstrap objects
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.
the observed response surface
Number of iterations to use in bootstrapping null distribution for either meanR or maxR statistics.
What assumption should be used for the variance of on- and
off-axis points. This argument can take one of the values from
c("equal", "model", "unequal"). With the value "equal" as the
default. "equal" assumes that both on- and off-axis points have the
same variance, "unequal" estimates a different parameter for on- and
off-axis points and "model" predicts variance based on the average
effect of an off-axis point. If no transformations are used the
"model" method is recommended. If transformations are used, only the
"equal" method can be chosen.
Prediction covariance matrix. If not specified, it will be estimated
by bootstrap using B.CP iterations.
Numeric vector containing number of replicates for each off-axis
dose combination. If missing, it will be calculated automatically from output
of predictOffAxis function.
the number of off-axis points
Cut-off to use in maxR procedure for declaring non-additivity (default is 0.95).
Numeric vector containing mean deviation of predicted response
surface from the observed one at each of the off-axis points. If missing,
it will be calculated automatically from output of
predictOffAxis function.
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.
a boolean, should bootstrapped response surfaces be used in the calculation of the confidence intervals?
data frame with off -axis information
a boolean, are effects restricted to be positive
the transformation and inverse transformation functions for the variance
The mean-variance model
a boolean indicating whether to rescale residuals, or else normality of the residuals is assumed.
Whether special bootstrap to correct for
heteroskedasticity should be used. If wild_bootstrap = TRUE, errors
are generated from sampling_errors multiplied by a random variable
following Rademacher distribution. Argument is used only if error = 4.
Type of distribution to be used for wild bootstrap. If wild_bootstrap = TRUE,
errors are generated from "rademacher", "gamma", "normal" or "two-point" distribution.
If control = "FCR" then algorithm controls false coverage rate, if control = "dFCR" then
algorithm controls directional false coverage rate, if control = "FWER" then
algorithm controls family wise error rate
Numeric value indicating the number of digits used for numeric values in confidence intervals
Further arguments that will be later passed to
generateData function during bootstrapping