SI: Comparing selectivity indices across curves

Description

'SI' compares selectivity indices for arbitrary dosage across curves. The selectivity is the ratio between effective dosages from different curves.

Usage

SI(obj, percVec, compMatch = NULL, od = FALSE, reverse = FALSE, ...)

Arguments

obj

an object of class 'drc'.

percVec

a numeric vector of dosage values.

compMatch

an optional character vector of names of assays to be compared. If not specified all comparisons are supplied.

logical. If TRUE adjustment for over-dispersion is used.

reverse

logical. If TRUE the order of comparison of two curves is reversed.

...

additional arguments to the function doing the calculations. For instance Upper limit for bisection method. Need to be larger than ED values to be calculated in ratio.

Value

A matrix with columns containing the estimates, estimated standard errors, t-statistics for testing indices equal to 1 and the corresponding p-values.

Details

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 limits needs to be larger than the EDx level to be calculated. The default limit is 1000. 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, ...).

Examples

Run this code

model1 <- multdrc(SLOPE~DOSE, CURVE, data=PestSci)

SI(model1, c(50,50))
SI(model1, c(10,50))
SI(model1, c(10,50), reverse=TRUE)

rm(model1)