drc (version 4.0-0)

ED.drc: Estimating effective doses

Description

ED estimates effective concentration or doses for one or more specified absolute or relative response levels.

Usage

# S3 method for drc
ED(object, respLev, interval = c("none", "delta", "fls", "tfls", "inv"), 
  clevel = NULL, level = ifelse(!(interval == "none"), 0.95, NULL),
  reference = c("control", "upper"), type = c("relative", "absolute"), lref, uref,
  bound = TRUE, vcov. = vcov, display = TRUE, logBase = NULL, 
  multcomp = FALSE, intType = "confidence", ...)

Value

An invisible matrix containing the shown matrix with two or more columns, containing the estimates and the corresponding estimated standard errors and possibly lower and upper confidence limits. Or, alternatively, a list with elements that may be plugged directly into parm

in the package multcomp (in case the argument multcomp is TRUE).

Details

There are several options for calculating confidence intervals through the argument interval. The option "delta" results in asymptotical Wald-type confidence intervals (using the delta method and the normal or t-distribution depending on the type of response). The option "fls" produces (possibly skewed) confidence intervals through back-transformation from the logarithm scale (only meaningful in case the parameter in the model is log(ED50) as for the llogistic2) models. The option "tfls" is for transforming back and forth from log scale (experimental). The option "inv" results in confidence intervals obtained through inverse regression.

For hormesis models (braincousens and cedergreen), the additional arguments lower and upper may be supplied. These arguments specify the lower and upper limits of the bisection method used to find the ED values. The lower and upper limits need to be smaller/larger than the EDx level to be calculated. The default limits are 0.001 and 1000 for braincousens and 0.0001 and 10000 for cedergreen and ucedergreen, but this may need to be modified (for cedergreen the upper limit may need to be increased and for ucedergreen the lower limit may need to be increased). Note that the lower limit should not be set to 0 (use instead something like 1e-3, 1e-6, ...).

See Also

backfit, isobole, and maED use ED for specific calculations involving estimated ED values.

The related function EDcomp may be used for estimating differences and ratios of ED values, whereas compParm may be used to compare other model parameters.

Examples

## Fitting 4-parameter log-logistic model
ryegrass.m1 <- drm(ryegrass, fct = LL.4())

## Calculating EC/ED values
ED(ryegrass.m1, c(10, 50, 90)) 
## first column: the estimates of ED10, ED50 and ED90
## second column: the corresponding estimated standard errors 

### How to use the argument 'ci'

## Also displaying 95% confidence intervals
ED(ryegrass.m1, c(10, 50, 90), interval = "delta")

## Comparing delta method and back-transformed 
##  confidence intervals for ED values

## Fitting 4-parameter log-logistic 
##  in different parameterisation (using LL2.4)
ryegrass.m2 <- drm(ryegrass, fct = LL2.4())  

ED(ryegrass.m1, c(10, 50, 90), interval = "fls")
ED(ryegrass.m2, c(10, 50, 90), interval = "delta")


### How to use the argument 'bound'

## Fitting the Brain-Cousens model
lettuce.m1 <- drm(weight ~ conc, 
data = lettuce, fct = BC.4())

### Calculating ED[-10]

# This does not work
#ED(lettuce.m1, -10)  

## Now it does work
ED(lettuce.m1, -10, bound = FALSE)  # works
ED(lettuce.m1, -20, bound = FALSE)  # works

## The following does not work for another reason: ED[-30] does not exist 
#ED(lettuce.m1, -30, bound = FALSE)