drc (version 1.2-0)

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(object, percVec, compMatch = NULL, od = FALSE, reverse = FALSE, 
  ci = c("none", "delta", "fieller", "fls"), 
  level = ifelse(!(ci=="none"), 0.95, NULL),
  reference = c("upper", "control"),  
  type = c("relative", "absolute"), logBase = NULL, ...)

Arguments

object

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.

od

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

reverse

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

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 "fieller" for confidence intervals based

level

numeric. The level for the confidence intervals. Default is 0.95.

reference

character string. Is the upper limit or the control level the reference?

type

character string specifying whether absolute or relative response levels are supplied.

logBase

numeric. The base of the logarithm in case logarithm transformed dose values are used.

...

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

Fieller's theorem is incorporated using the formulas Kotz and Johnson (1983) and Finney (1978). 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, ...).

References

Finney, D. J. (1978) Statistical method in Biological Assay, London: Charles Griffin House, 3rd edition (pp. 80--82). Kotz, S. and Johnson, N. L. (1983) Encyclopedia of Statistical Sciences Volume 3, New York: Wiley & Sons (pp. 86--87).

See Also

A related function is ED for calculating effect dosages.

Examples

m1 <- multdrc(SLOPE~DOSE, CURVE, data=PestSci)

SI(m1, c(50,50))
SI(m1, c(10,50))
SI(m1, c(10,50), reverse = TRUE)


## Relative potency of two herbicides
m2 <- multdrc(DryMatter~Dose, Herbicide, 
data = christensenetal2003, fct = l3())

SI(m2, c(50, 50))
SI(m2, c(50, 50), ci = "fieller")

## Comparison based on absolute
##  response level

m3 <- drm(SLOPE~DOSE, CURVE,
data = PestSci, fct = LL.4())

SI(m3, c(0.5,0.5), c(2,4), type="a", ci="fieller")

SI(m3, c(55,80), c(2,4))  
# same comparison using a relative response level


## Relative potency transformed from log scale
m4 <- drm(drymat~log(dose), curve, data=cabanne[-c(1:40), ], 
pmodels = data.frame(curve,curve,1,curve), fct = B.4())

SI(m4, c(50,50), ci="fls", logBase=exp(1))

rm(m1, m2, m3, m4)