bootConfInt {BIGL}

Obtain confidence intervals for the raw effect sizes on every off-axis point and overall

Description

Obtain confidence intervals for the raw effect sizes on every off-axis point and overall

Usage

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,
  ...
)

Arguments

Total

data frame with all effects and mean effects

idUnique

unique combinations of on-axis points, a character vector

bootStraps

precomputed bootstrap objects

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.

respS

the observed response surface

B.B

Number of iterations to use in bootstrapping null distribution for either meanR or maxR statistics.

method

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.

CP

Prediction covariance matrix. If not specified, it will be estimated by bootstrap using B.CP iterations.

reps

Numeric vector containing number of replicates for each off-axis dose combination. If missing, it will be calculated automatically from output of predictOffAxis function.

n1

the number of off-axis points

cutoff

Cut-off to use in maxR procedure for declaring non-additivity (default is 0.95).

R

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.

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.

bootRS

a boolean, should bootstrapped response surfaces be used in the calculation of the confidence intervals?

data_off

data frame with off -axis information

posEffect

a boolean, are effects restricted to be positive

transFun, invTransFun

the transformation and inverse transformation functions for the variance

model

The mean-variance model

rescaleResids

a boolean indicating whether to rescale residuals, or else normality of the residuals is assumed.

wild_bootstrap

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.

wild_bootType

Type of distribution to be used for wild bootstrap. If wild_bootstrap = TRUE, errors are generated from "rademacher", "gamma", "normal" or "two-point" distribution.

control

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

digits

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

Value

A list with components

offAxis

The off-axis bootstrapped confidence intervals

single

A mean effect and percentile and studentized boostrap intervals