ED.drc: Estimating effective doses

Description

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

Usage

## S3 method for class 'drc':
ED(object, respLev, interval = c("none", "delta", "fls", "tfls"), 
  clevel = NULL, level = ifelse(!(interval == "none"), 0.95, NULL),
  reference = c("control", "upper"), type = c("relative", "absolute"), lref, uref,
  bound = TRUE, od = FALSE, display = TRUE, pool = TRUE, logBase = NULL, ...)
  
  ## S3 method for class 'mrdrc':
ED(object, respLev, interval = c("none", "approximate", "bootstrap"), level = 0.95, 
  method = c("bisection", "grid"), cgridsize = 20, gridsize = 100, display = TRUE, lower, upper,
  intType = c("confidence", "prediction"), minmax = c("response", "dose"), n = 1000, seedVal = 200810311, ...)

Arguments

object

an object of class 'drc'.

respLev

a numeric vector containing the response levels.

interval

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

clevel

character string specifying the curve id in case on estimates for a specific curve or compound is requested. By default estimates are shown for all curves.

level

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

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).

lref

numeric value specifying the lower limit to serve reference.

uref

numeric value specifying the upper limit to serve reference (eg. 100%).

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.

display

logical. If TRUE results are displayed. Otherwise they are not (useful in simulations).

pool

logical. If TRUE curves are pooled. Otherwise they are not. This argument only works for models with independently fitted curves as specified in drm.

logBase

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

method

character string specifying if bisection or grid search should be used to determine ED estimates. Grid search may give better results for ED level close to the boundaries of the dose range or in case of a non-monotonous dose-response curves. The bis

cgridsize

numeric specifying the number of grid points in the initial grid used for both bisection and grid search to narrow down the interval where the ED estimate is to be found.

gridsize

numeric specifying the number of grid points in the second finer grid search.

lower

numeric value specifying the lower reference limit.

upper

numeric specifying the upper reference limit.

intType

character string specifying whether confidence or prediction intervals are requested.

minmax

character string indicating if the control level should be based on the the minimum dose or the maximum response. The latter is more suitable for dose-response data showing hormesis.

numeric specifying the number of simulations for the bootstrap confidence intervals.

seedVal

numeric giving the seed for the random number generator used for the bootstrap confidence intervals.

...

see the details section below.

Value

A matrix with two or more columns, containing the estimates and the corresponding estimated standard errors and possibly lower and upper confidence limits.

Details

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, ...). For model-robust fits the arguments lower and upper can be used to specify reference values for the lower and upper limits of the dose-response relationship. This only applies for the continuous responses. For quantal responses, the reference values are fixed 0 and 1, respectively.

Examples

Run this code

### How to use 'ED'

## 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 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]

# It 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

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