Description Usage Arguments Value Author(s) References Examples
Obtaining c-efficiency for estimating the ED50 for a given design under the 4-parameter logistic model. This also can be used to compute the c-efficiency for the ED50 under the 2 or 3-parameter logistic models by setting the parameters values differently.
1 |
weight |
A numeric vector. Weights for a given design. The weights represent the proportional allocations of subjects to the dose levels in a given design. |
P |
A numeric vector. Solicited information on nominal values for the vector. P=(p1, p2, p3, p4), where p1 is the lower limit of the response (θ_4), p2 is Emax (θ_1), p3 is the ED50 (exp (-\frac{θ_3}{θ_2})) and p4 is the slope at the ED50 (-θ_2). For the 4-parameter logistic model, a user needs to specify all 4 nominal values in P: P = (p1, p2, p3, p4). For the 3-parameter logistic model, a user needs to specify only the 3 nominal values, Emax, the ED50, and the slope: P = (p2, p3, p4). For the 2-parameter logistic model, a user needs to specify only the 2 nominal values, the ED50 and the slope: P = (p3, p4). |
dose |
A vector. Dose levels for a given design. |
LB |
Numeric. Predetermined lower bound of the dose range for the log dose. |
UB |
Numeric. Predetermined upper bound of the dose range for the log dose. |
r |
Numeric. The number of iterations to select the initial design to search c-optimal design for estimating the ED50. Default is 10 and needed to be increased (for example, r = 30 or 50) if the searched c-optimal design is not a true optimal. |
grid |
Numeric. The grid density to discretize the predetermined dose interval. Default is 0.01. |
epsilon |
Numeric. Stopping criterion for the algorithm to search c-optimal design for the ED50. Default is 0.001. |
epsilon_w |
Numeric. Stopping criterion for the Newton Raphson method inside of the algorithm. Default is 10^-6. |
An object of class OPT.
Seung Won Hyun, Weng Kee Wong, and Yarong Yang
Hyun, S.W., Wong, W.K, Yang, Y. VNM: An R Package for Finding Multiple-Objective Optimal Designs for the 4-Parameter Logistic Model. (Journal of Statistical Software, 83, 1-19, 2018, doi: 10.18637/jss.v083.i05.)
Hyun, S. W., Wong, W.K. Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels. (International Journal of Biostatistics, 11, 253-271, 2015)
1 2 3 4 5 6 7 8 9 10 11 | ##The given design
dose=c(-6.91,-4.89,-4.18,6.91)
weight=c(.344,.323,.162,.171)
##Model parameter values for the 4PL
par.4PL=c(0.137,1.563,.00895,-1.790)
##Check c-efficiency of the given design for estimating the ED50 and its verification plot
Res.c1=ceff1(weight, P=par.4PL, dose, LB=log(.001), UB=log(1000))
summary(Res.c1)
plot(Res.c1)
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