ED: Estimating effective doses

Description

'ED' estimates effective doses given (ECp/EDp/ICp) for given reponse levels.

Usage

ED(object, respLev, bound = TRUE, od = FALSE, ci = c("none", "delta", "fls"), 
  level = ifelse(!(ci=="none"), 0.95, NULL), logBase = NULL,
  reference = c("upper", "control"), type = c("relative", "absolute"), ...)

Arguments

object

an object of class 'drc'.

respLev

a numeric vector containing the response levels.

bound

logical. If TRUE only ED values between 0 and 100% are allowed. FALSE is useful for hormesis models.

logical. If TRUE adjustment for over-dispersion is used.

character string specifying the type of confidence intervals to be supplied. The default is "none". Use "delta" for asymptotics-based confidence intervals (using the delta method and the t-distribution). Use "fls" for from logarithm scale based con

level

numeric. The level for the confidence intervals. The default is 0.95.

logBase

numeric. The base of the logarithm in case logarithm transformed dose values are used.

reference

character string. Is the upper limit or the control level the reference?

type

character string. Whether the specified response levels are absolute or relative (default).

...

additional arguments for the function doing the calculations.

Value

A matrix with two columns: one containing the estimates and one containing the corresponding estimated standard errors.

Details

This function is only implemented for the built-in functions of class 'braincousens', 'gompertz', 'logistic' and 'mlogistic'. For objects of class 'braincousens' or 'mlogistic' the additional argument may be the 'upper' argument or the 'interval' argument. The 'upper' argument specifies the upper limit of the bisection method. The upper limit needs to be larger than the EDx level to be calculated. The default limit is 1000, but this may need to be increased. The 'interval' argument should specify a rough interval in which the dose yielding the maximum hormetical response lies. The default interval is 'c(0.001, 1000)'. Notice that the lower limit should not be set to 0 (use something like 1e-3, 1e-6, ...).

Examples

Run this code

model1<-multdrc(ryegrass)

ED(model1, c(10,50,90)) 
## first column: the estimates of ED10, ED50 and ED90
## second column: the estimated standard errors 

## Also displaying 95% confidence intervals
ED(model1, c(10,50,90), ci = "delta")

## The argument 'bound'
model2 <- multdrc(weight ~ conc, 
data = hormesis, fct = bcl3())

## Calculating ED[-10]
#ED(model2, -10)  
# does not work

ED(model2, -10, bound = FALSE)  # works
ED(model2, -20, bound = FALSE)  # works

#ED(model2, -30, bound = FALSE)  
# does not work: ED[-30] does not exist