maFitMod: Fit dose-response models via bootstrap model averaging...

In DoseFinding: Planning and Analyzing Dose Finding Experiments

Fit dose-response models via bootstrap model averaging (bagging)

Description

This function fits dose-response models in a bootstrap model averaging approach motivated by the bagging procedure (Breiman 1996). Given summary estimates for the outcome at each dose, the function samples summary data from the multivariate normal distribution. For each sample dose-response models are fit to these summary estimates and the best model according to the gAIC is selected.

Usage

maFitMod(dose, resp, S, models, nSim = 1000, control, bnds, addArgs = NULL)

## S3 method for class 'maFit'
predict(
  object,
  summaryFct = function(x) quantile(x, probs = c(0.025, 0.25, 0.5, 0.75, 0.975)),
  doseSeq = NULL,
  ...
)

## S3 method for class 'maFit'
plot(
  x,
  plotData = c("means", "meansCI", "none"),
  xlab = "Dose",
  ylab = "Response",
  title = NULL,
  level = 0.95,
  trafo = function(x) x,
  lenDose = 201,
  ...
)

Arguments

dose	Numeric specifying the dose variable.
resp	Numeric specifying the response estimate corresponding to the doses in dose
S	Covariance matrix associated with the dose-response estimate specified via resp
models	dose-response models to fit
nSim	Number of bootstrap simulations
control	Same as the control argument in fitMod.
bnds	Bounds for non-linear parameters. This needs to be a list with list entries corresponding to the selected bounds. The names of the list entries need to correspond to the model names. The defBnds function provides the default selection.
addArgs	List containing two entries named "scal" and "off" for the "betaMod" and "linlog" model. When addArgs is NULL the following defaults are used ‘⁠list(scal = 1.2*max(doses), off = 0.01*max(doses))⁠’
object	Object of class maFit
summaryFct	If equal to NULL predictions are calculated for each sampled parameter value. Otherwise a summary function is applied to the dose-response predictions for each parameter value. The default is to calculate 0.025, 0.25, 0.5, 0.75, 0.975 quantiles of the predictions for each dose.
doseSeq	Where to calculate predictions.
...	Additional parametes (unused)
x	object of class maFit
plotData	Determines how the original data are plotted: Either as means or as means with CI or not at all. The level of the CI is determined by the argument ‘⁠level⁠’.
xlab	x-axis label
ylab	y-axis label
title	plot title
level	Level for CI, when plotData is equal to ‘⁠meansCI⁠’.
trafo	Plot the fitted models on a transformed scale (e.g. probability scale if models have been fitted on log-odds scale). The default for ‘⁠trafo⁠’ is the identity function.
lenDose	Number of grid values to use for display.

Value

An object of class ‘⁠maFit⁠’, which contains the fitted dose-response models ‘⁠DRMod⁠’ objects, information on which model was selected in each bootstrap and basic input parameters.

Author(s)

Bjoern Bornkamp

References

Breiman, L. (1996). Bagging predictors. Machine learning, 24, 123-140.

See Also

fitMod, bFitMod, drmodels

Examples

data(biom)
## produce first stage fit (using dose as factor)
anMod <- lm(resp~factor(dose)-1, data=biom)
drFit <- coef(anMod)
S <- vcov(anMod)
dose <- sort(unique(biom$dose))
## fit an emax and sigEmax model (increase nSim for real use)
mFit <- maFitMod(dose, drFit, S, model = c("emax", "sigEmax"), nSim = 10)
mFit
plot(mFit, plotData = "meansCI")
ED(mFit, direction = "increasing", p = 0.9)

DoseFinding documentation built on April 3, 2025, 8:59 p.m.