Comparison of relative potencies between dose-response curves

Relative potencies (also called selectivity indices) for arbitrary doses are compared between fitted dose-response curves.

Usage

EDcomp(
  object,
  percVec,
  percMat = NULL,
  compMatch = NULL,
  od = FALSE,
  vcov. = vcov,
  reverse = FALSE,
  interval = c("none", "delta", "fieller", "fls"),
  level = ifelse(!(interval == "none"), 0.95, NULL),
  reference = c("control", "upper"),
  type = c("relative", "absolute"),
  display = TRUE,
  pool = TRUE,
  logBase = NULL,
  multcomp = FALSE,
  ...
)

Arguments

object: an object of class 'drc'.
percVec: a numeric vector of dosage values.
percMat: a matrix with 2 columns providing the pairs of indices of percVec to be compared. By default all pairs are compared.
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. This argument only makes a difference for binomial data.
vcov.: function providing the variance-covariance matrix. vcov is the default, but sandwich is also an option (for obtaining robust standard errors).
reverse: logical. If TRUE the order of comparison of two curves is reversed.
interval: character string specifying the type of confidence intervals to be supplied. The default is "none". Use "delta" for asymptotics-based confidence intervals, "fieller" for confidence intervals based on Fieller's theorem, or "fls" for confidence intervals back-transformed from logarithm scale.
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.
display: logical. If TRUE results are displayed. Otherwise they are not (useful in simulations).
pool: logical. If TRUE curves are pooled. Otherwise they are not. This argument only works for models with independently fitted curves as specified in drm.
logBase: numeric. The base of the logarithm in case logarithm transformed dose values are used.
multcomp: logical to switch on output for use with the package multcomp. Default is FALSE.
...: additional arguments passed to the function doing the calculations.

Value

An invisible matrix containing the estimates and the corresponding estimated standard errors and possibly lower and upper confidence limits. Or, alternatively, a list with elements that may be plugged directly into parm in the package multcomp (when multcomp is TRUE).

Details

Fieller's theorem is incorporated using the formulas provided by 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 specifying limits for the bisection method.

Author

Christian Ritz

Examples

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

EDcomp(spinach.LL.4, c(50, 50))
#> 
#> Estimated ratios of effect doses
#> 
#>             Estimate Std. Error    t-value    p-value
#> 1/2:50/50  1.8983586  0.7118489  1.2620074  0.2103980
#> 1/3:50/50  1.3073016  0.5541592  0.5545367  0.5806678
#> 1/4:50/50  9.0963785  2.4686586  3.2796671  0.0015076
#> 1/5:50/50  8.5152294  2.3364483  3.2165186  0.0018362
#> 2/3:50/50  0.6886484  0.2908078 -1.0706439  0.2873603
#> 2/4:50/50  4.7917071  1.2883525  2.9430664  0.0041886
#> 2/5:50/50  4.4855747  1.2196032  2.8579579  0.0053607
#> 3/4:50/50  6.9581332  2.3220591  2.5658835  0.0120448
#> 3/5:50/50  6.5135923  2.1896041  2.5180773  0.0136744
#> 4/5:50/50  0.9361120  0.0781402 -0.8176069  0.4158677
EDcomp(spinach.LL.4, c(10, 50))
#> 
#> Estimated ratios of effect doses
#> 
#>              Estimate  Std. Error     t-value     p-value
#> 1/2:10/50  2.7644e-02  1.5249e-02 -6.3765e+01  7.3799e-74
#> 1/3:10/50  1.9037e-02  1.1155e-02 -8.7939e+01  1.5155e-85
#> 1/4:10/50  1.3246e-01  6.4530e-02 -1.3444e+01  4.7570e-23
#> 1/5:10/50  1.2400e-01  6.0615e-02 -1.4452e+01  6.4822e-25
#> 2/3:10/50  4.4298e-02  3.7850e-02 -2.5250e+01  1.4449e-41
#> 2/4:10/50  3.0823e-01  2.4349e-01 -2.8411e+00  5.6264e-03
#> 2/5:10/50  2.8854e-01  2.2823e-01 -3.1173e+00  2.4906e-03
#> 3/4:10/50  2.7742e-01  1.5001e-01 -4.8170e+00  6.2889e-06
#> 3/5:10/50  2.5969e-01  1.4081e-01 -5.2573e+00  1.0722e-06
#> 4/5:10/50  2.8449e-01  3.7366e-02 -1.9148e+01  7.0761e-33
EDcomp(spinach.LL.4, c(10, 50), reverse = TRUE)
#> 
#> Estimated ratios of effect doses
#> 
#>             Estimate Std. Error    t-value    p-value
#> 2/1:50/10 3.6174e+01 1.9955e+01 1.7627e+00 8.1542e-02
#> 3/1:50/10 5.2530e+01 3.0781e+01 1.6741e+00 9.7792e-02
#> 4/1:50/10 7.5494e+00 3.6778e+00 1.7808e+00 7.8519e-02
#> 5/1:50/10 8.0646e+00 3.9423e+00 1.7920e+00 7.6691e-02
#> 3/2:50/10 2.2575e+01 1.9289e+01 1.1185e+00 2.6650e-01
#> 4/2:50/10 3.2443e+00 2.5629e+00 8.7570e-01 3.8366e-01
#> 5/2:50/10 3.4658e+00 2.7414e+00 8.9945e-01 3.7095e-01
#> 4/3:50/10 3.6047e+00 1.9491e+00 1.3363e+00 1.8501e-01
#> 5/3:50/10 3.8507e+00 2.0880e+00 1.3653e+00 1.7576e-01
#> 5/4:50/10 3.5150e+00 4.6168e-01 5.4476e+00 4.8856e-07