Opt5PL0.1.1 package

Optimal Designs for the 5-Parameter Logistic Model

DD_weight_1

The first derivative of the Ds-optimality criterion with respect to th...

DD_weight_2

The second derivative of the Ds-optimality criterion with respect to t...

DD1

Computing each element of the function c_weight_2

dd11

Computing each element of the function DD_weight_2

Deff

Obtaining D-efficiency for estimating model parameters

Dp

Target dose, EDp

DS1

Sensitivity function of c-optimality criterion for the EDp

ds11

Sensitivity function of Ds-optimality criterion

Dseff

Obtaining Ds-efficiency for estimating the asymmetric factor under the...

DsOPT

Search Ds-optimal design for estimating the asymmetric factor under th...

EDpeff

Obtaining c-efficiency for estimating the EDp under the 5-parameter lo...

c_weight

One iteration to run Newton Raphson to get c-optimal weights

c_weight_1

The first derivative of the c-optimality criterion w.r.t the model par...

c_weight_2

The second derivative of the c-optimality criterion with respect to th...

D_weight

One iteration to run Newton Raphson to get D-optimal weights

D_weight_1

The first derivative of the D-optimality criterion w.r.t the model par...

D_weight_2

The second derivative of the D-optimality criterion w.r.t the model pa...

D1

Computing each element of the function c_weight_1

d11

Computing each element of the function DD_weight_1

DD_weight

One iteration to run Newton Raphson to get Ds-optimal weights

EDpOPT

Search c-optimal designs for estimating the EDp under the 5-parameter ...

f

Gradient of the mean function

g

Partial derivative of the EDp with respect to the model parameters

ginv

Generalized Inverse Matrix

infor

Obtain a information matrix at a single design point

Inv

Adjusting invere information matrix being not singular

Minus

Matrix subtraction

Multiple

Matrix multiplication

Plus

Matrix addition

RDOPT

Search the robust D-optimal designs for estimating model parameters

S_weight

Newton Raphson method to get optimal weights

SDM

Summation of diagonal elements in a matrix

smalld1

Sub-function of the function D_weight_1

smalldd1

Sub-function of the function D_weight_2

smallds1

Sensitivity function of D-optimality criterion

sMultiple

Multiply a constant to a matrix

Trans

Transpose of a matrix

upinfor

Obtain normalized Fisher information matrix

Obtain and evaluate various optimal designs for the 3, 4, and 5-parameter logistic models. The optimal designs are obtained based on the numerical algorithm in Hyun, Wong, Yang (2018) <doi:10.18637/jss.v083.i05>.

  • Maintainer: Seung Won Hyun
  • License: GPL-2
  • Last published: 2018-10-06