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Opt5PL (version 0.1.1)

Optimal Designs for the 5-Parameter Logistic Model

Description

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) .

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Version

Install

install.packages('Opt5PL')

Monthly Downloads

162

Version

0.1.1

License

GPL-2

Maintainer

Seung Hyun

Last Published

October 6th, 2018

Functions in Opt5PL (0.1.1)

Plus

Matrix addition
Minus

Matrix subtraction
Inv

Adjusting invere information matrix being not singular
infor

Obtain a information matrix at a single design point
sMultiple

Multiply a constant to a matrix
EDpeff

Obtaining c-efficiency for estimating the EDp under the 5-parameter logistic model.
c_weight_2

The second derivative of the c-optimality criterion with respect to the model parameters
Dseff

Obtaining Ds-efficiency for estimating the asymmetric factor under the 5-parameter logistic model.
c_weight_1

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

Search c-optimal designs for estimating the EDp under the 5-parameter logistic model
Multiple

Matrix multiplication
RDOPT

Search the robust D-optimal designs for estimating model parameters
Trans

Transpose of a matrix
DS1

Sensitivity function of c-optimality criterion for the EDp
f

Gradient of the mean function
ds11

Sensitivity function of Ds-optimality criterion
Dp

Target dose, EDp
d11

Computing each element of the function DD_weight_1
dd11

Computing each element of the function DD_weight_2
DsOPT

Search Ds-optimal design for estimating the asymmetric factor under the 5-parameter logistic model.
SDM

Summation of diagonal elements in a matrix
smalld1

Sub-function of the function D_weight_1
c_weight

One iteration to run Newton Raphson to get c-optimal weights
smalldd1

Sub-function of the function D_weight_2
g

Partial derivative of the EDp with respect to the model parameters
S_weight

Newton Raphson method to get optimal weights
ginv

Generalized Inverse Matrix
smallds1

Sensitivity function of D-optimality criterion
upinfor

Obtain normalized Fisher information matrix
D_weight_2

The second derivative of the D-optimality criterion w.r.t the model parameters
D_weight_1

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

One iteration to run Newton Raphson to get D-optimal weights
D1

Computing each element of the function c_weight_1
DD_weight_2

The second derivative of the Ds-optimality criterion with respect to the model parameters
DD_weight_1

The first derivative of the Ds-optimality criterion with respect to the model parameters
DD1

Computing each element of the function c_weight_2
DD_weight

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

Obtaining D-efficiency for estimating model parameters