ED.drc: Estimating effective doses

Description

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

Usage

## S3 method for class 'drc':
ED(object, respLev, bound = TRUE, od = FALSE, ci = c("none", "delta", "fls", "tfls"), 
  level = ifelse(!(ci=="none"), 0.95, NULL), logBase = NULL,
  reference = c("control", "upper"), type = c("relative", "absolute"), 
  display = TRUE, ...)
  
  ## S3 method for class 'mrdrc':
ED(object, respLev, level = 0.95, interval = c("none", "approximate", "bootstrap"), 
  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.

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).

display

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

interval

character string specifying the type of confidence intervals to be supplied. The default is "none". The only alternative for model-robust fits is using inverse regression.

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.

...

further arguments (none at the moment).

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


### How to use the argument 'bound'

## Fitting the Brain-Cousens model
lettuce.m1 <- multdrc(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)