bootConfInt {BIGL} R Documentation

## 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,
...
)


### 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 the transformation and inverse transformation functions for the variance 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. ... 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

[Package BIGL version 1.6.6 Index]