Usage
drm(formula, curveid, pmodels, weights, data = NULL, subset, fct,
type = c("continuous", "binomial", "Poisson", "quantal", "survival"), bcVal = NULL, bcAdd = 0,
start, na.action = na.fail, robust = "mean", logDose = NULL,
control = drmc(), lowerl = NULL, upperl = NULL)Arguments
formula
a symbolic description of the model to be fit. Either of the form 'response $~$ dose'
or as a data frame with response values in first column and dose values in second column.
curveid
a numeric vector or factor containing the grouping of the data.
pmodels
a data frame with a many columns as there are parameters in the non-linear function.
Or a list containing a formula for each parameter in the non-linear function.
weights
a numeric vector containing weights.
data
an optional data frame containing the variables in the model.
subset
an optional vector specifying a subset of observations to be used in the fitting process.
fct
a list with three or more elements specifying the non-linear
function, the accompanying self starter function, the names of the parameter in the non-linear function and,
optionally, the first and second derivatives as well as information used for c
bcVal
a numeric value specifying the lambda parameter to be used in the Box-Cox transformation.
bcAdd
a numeric value specifying the constant to be added on both sides prior to Box-Cox transformation.
The default is 0.
type
a character string specifying the data type: continuous is the only option currently.
start
an optional numeric vector containing starting values for all mean parameters in the model.
Overrules any self starter function.
na.action
a function which indicates what should happen when the data contain 'NA's.
The default is link{na.fail}. To omit 'NA's use na.omit. robust
a character string specifying the rho function for robust estimation. Default is non-robust
least squares estimation ("mean"). Available robust methods are: median estimation ("median"),
least median of squares ("lms"), least trimmed squares ("lts
logDose
a numeric value or NULL. If log doses value are provided the base of the logarithm should be specified (exp(1) for the natural logarithm
and 10 for 10-logarithm).
control
a list of arguments controlling constrained optimisation (zero as boundary),
maximum number of iteration in the optimisation,
relative tolerance in the optimisation, warnings issued during the optimisation.
lowerl
a numeric vector of lower limits for all parameters in the model
(the default corresponds to minus infinity for all parameters).
upperl
a numeric vector of upper limits for all parameters in the model
(the default corresponds to plus infinity for all parameters).