ED.drc {drc} | R Documentation |
'ED' estimates effective doses given (ECp/EDp/ICp) for given reponse levels.
## 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, ...)
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. |
od |
logical. If TRUE adjustment for over-dispersion is used. |
ci |
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 boltzmann ) models. |
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 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). |
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.
A matrix with two columns: one containing the estimates and one containing the corresponding estimated standard errors.
Christian Ritz
The related function SI
. For model-robust fits, examples are found
in the help of mrdrm
.
### 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), 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)