drc (version 0.8-2)

mdControl: Sets control arguments

Description

Set control arguments in the control argument in the function 'multdrc'.

Usage

mdControl(bcAdd=0, constr = FALSE, errorm = TRUE, maxIt = 500, method="BFGS", 
          relTol = 1e-07, rmNA=FALSE, warnVal = -1, zeroTol=0)

Arguments

bcAdd

numeric. The constant to be added in Box-Cox transformation.

constr

logical. If TRUE optimisation is constrained, only yielding non-negative parameters.

errorm

logical specifying whether failed convergence in multdrc should result in an error or only a warning.

maxIt

numeric. The maximum number of iterations in the optimisation procedure.

method

character string. The method used in the optimisation procedure. See optim for available methods.

relTol

numeric. The relative tolerance in the optimisation procedure.

rmNA

logical. Should NAs be removed from sum of squares used for estimation? Default is FALSE (not removed).

warnVal

numeric. If equal to 0 then the warnings are stored and displayed at the end. See under 'warn' in options. The default results in suppression of warnings.

zeroTol

numeric. The threshold below which doses are considered as control.

Value

• A list with 8 components, one for each of the above arguments.

See Also

See the documentation on the data set PestSci.

Examples

### Displaying the default settings
mdControl()  

### Using 'method' argument
model1 <- multdrc(FA)

model2 <- multdrc(FA, varPower = TRUE, startVal = coef(model1),
control = mdControl(method = "Nelder-Mead"))

rm(model1, model2)


### Using 'relTol' argument to improve accuracy

## Fitting a model to the PestSci dataset
model1 <- multdrc(SLOPE~DOSE, CURVE, data=PestSci)

## Fitting the same model in a different parametrisation
model2 <- multdrc(SLOPE~DOSE, CURVE, data=PestSci,
collapse = list(~factor(CURVE),~factor(CURVE),~factor(CURVE),~factor(CURVE)))

## Reducing the relative tolerance
model3 <- multdrc(SLOPE~DOSE, CURVE, data=PestSci,
collapse = list(~factor(CURVE),~factor(CURVE),~factor(CURVE),~factor(CURVE)),
control = mdControl(relTol = 1e-12))

## Comparing the parameter estimates
coef(model1)
coef(model2)
coef(model3)  # model3 is closer to model1 than model2 is 

rm(model1, model2, model3)