DoseFinding (version 0.6-3)

predict.MCPMod: Predict a MCPMod object.

Description

Predict the dose-response curve of a MCPMod object (in case of model averaging this will be an average of the significant and converged dose-response models)

Usage

## S3 method for class 'MCPMod':
predict(object, type = c("fullModel", "EffectCurve"), newdata = NULL,
        doseSeq = NULL,  lenSeq = 101, uGrad = NULL, ...)

Arguments

object

A MCPMod object

type

Predictions for all variables or only the Effect Curve?

newdata

Data frame containing values where to predict when using type="fullModel", if missing use the data, where the model was fitted on.

doseSeq

Numeric specifying doses where to predict in case of type="EffectCurve", if missing use lenSeq equally spaced values between smallest and largest dose in the data.

lenSeq

If doseSeq is not specified a equally spaced grid of "lenSeq" values between placebo and the maximal dose in the study is used.

uGrad

Function to return the gradient of a user defined model, see Examples of the fitDRModel function.

...

Additional arguments

Value

• A numeric consisting out of the predictions.

See Also

fitDRModel

Examples

data(IBScovars)
models <- list(emax = 0.2, quadratic = -0.2, linlog = NULL)
dfe <- MCPMod(resp ~ dose, IBScovars, models, addCovars = ~gender,
             alpha = 0.05, pVal = TRUE,
             selModel = "aveAIC", clinRel = 0.25, off = 1)
# predict only effect curve
predict(dfe,  type = "EffectCurve", doseSeq = 0:4)

# predict full model, specify where to predict via newdata
preddat <- data.frame(dose = 0:4, gender = as.factor(rep(1, 5)))
predict(dfe, type = "fullModel", newdata = preddat)