meanR {BIGL}

Compute meanR statistic for the estimated model

Description

meanR computes the meanR statistic for the provided model and returns the computed F-statistic and the estimated p-value. p-value can be calculated either by assuming an exact distribution or using bootstrapping procedure. In the latter case, null distribution of bootstrapped F-statistics is also returned.

Usage

meanR(
  data_off,
  fitResult,
  transforms = fitResult$transforms,
  null_model = c("loewe", "hsa", "bliss", "loewe2"),
  R,
  CP,
  reps,
  nested_bootstrap = FALSE,
  B.B = NULL,
  B.CP = NULL,
  cl = NULL,
  method = c("equal", "model", "unequal"),
  bootStraps,
  paramsBootstrap,
  idUnique,
  n1,
  transFun,
  invTransFun,
  ...
)

Arguments

data_off

data frame with off -axis information

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.

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.

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.

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.

CP

Matrix which is part of covariance matrix for the R argument

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.

nested_bootstrap

When statistics are calculated, if nested_bootstrap = TRUE, CP matrix is recalculated at each bootstrap iteration of B.B using B.CP iterations. Using such nested bootstrap may however significantly increase computational time. If nested_bootstrap = FALSE, CP bootstrapped data reuses CP matrix calculated from the original data.

B.B

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

B.CP

Number of bootstrap iterations to use for CP matrix estimation

cl

If parallel computations are desired, cl should be a cluster object created by makeCluster. If parallel computing is active, progress reporting messages are not necessarily ordered as it should be expected.

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.

bootStraps

precomputed bootstrap objects

paramsBootstrap

parameters for the nested bootstrap

idUnique

unique combinations of on-axis points, a character vector

n1

the number of off-axis points

transFun, invTransFun

the transformation and inverse transformation functions for the variance

...

Further arguments that will be later passed to generateData function during bootstrapping

Value

This function returns a meanR object with estimates for the meanR statistical test. meanR object is essentially a list with appropriately named elements.

meanR object list includes notably the calculated F-statistic, p-value and degrees of freedom ("n1" and "df0" respectively) used to find the critical value of the F-distribution under the null.

If meanR test is run with bootstrapping, then p-value estimate is based on bootstrapped null distribution of test statistic and an additional element "FDist" (of class "ecdf") is returned.