ED.drc {drc}R Documentation

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"), 
  level = ifelse(!(interval == "none"), 0.95, NULL),
  reference = c("control", "upper"), type = c("relative", "absolute"), lref, uref,
  bound = TRUE, od = FALSE, display = 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 confidence intervals (in case the parameter in the model is log(ED50) as for the logistic) models. The only alternative for model-robust fits is using inverse regression.
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.
od logical. If TRUE adjustment for over-dispersion is used.
display logical. If TRUE results are displayed. Otherwise they are not (useful in simulations).
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 bisection method is faster than the grid search.
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.
n 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).

Details

For hormesis models (braincousens and cedergreen), the additional arguments lower and upper may be supplied. The lower and upper 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, but this may need to be increased. Notice 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.

Value

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

Author(s)

Christian Ritz

See Also

The related function SI. For model-robust fits, examples are found in the help of mrdrm.

Examples


### 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
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)  


[Package drc version 1.6-1 Index]