Package: Opt5PL 0.1.1

Seung Won Hyun

Opt5PL: Optimal Designs for the 5-Parameter Logistic Model

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>.

Authors:Seung Won Hyun, Weng Kee Wong, and Yarong Yang

Opt5PL_0.1.1.tar.gz
Opt5PL_0.1.1.tar.gz(r-4.5-noble)Opt5PL_0.1.1.tar.gz(r-4.4-noble)
Opt5PL_0.1.1.tgz(r-4.4-emscripten)Opt5PL_0.1.1.tgz(r-4.3-emscripten)
Opt5PL.pdf |Opt5PL.html✨
Opt5PL/json (API)

# Install 'Opt5PL' in R:
install.packages('Opt5PL', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 6 scripts 105 downloads 38 exports 2 dependencies

Last updated 6 years agofrom:eca99ceea1. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-linux-x86_64OKNov 05 2024

Exports:c_weightc_weight_1c_weight_2D_weightD_weight_1D_weight_2D1d11DD_weightDD_weight_1DD_weight_2DD1dd11DeffDpDS1ds11DseffDsOPTEDpeffEDpOPTfgginvinforInvMinusMultiplePlusRDOPTS_weightSDMsmalld1smalldd1smallds1sMultipleTransupinfor

Dependencies:matrixcalcRcpp

Readme and manuals

Help Manual

Help pageTopics
One iteration to run Newton Raphson to get c-optimal weightsc_weight
The first derivative of the c-optimality criterion w.r.t the model parametersc_weight_1
The second derivative of the c-optimality criterion with respect to the model parametersc_weight_2
One iteration to run Newton Raphson to get D-optimal weightsD_weight
The first derivative of the D-optimality criterion w.r.t the model parametersD_weight_1
The second derivative of the D-optimality criterion w.r.t the model parametersD_weight_2
Computing each element of the function c_weight_1D1
Computing each element of the function DD_weight_1d11
One iteration to run Newton Raphson to get Ds-optimal weightsDD_weight
The first derivative of the Ds-optimality criterion with respect to the model parametersDD_weight_1
The second derivative of the Ds-optimality criterion with respect to the model parametersDD_weight_2
Computing each element of the function c_weight_2DD1
Computing each element of the function DD_weight_2dd11
Obtaining D-efficiency for estimating model parametersDeff
Target dose, EDpDp
Sensitivity function of c-optimality criterion for the EDpDS1
Sensitivity function of Ds-optimality criterionds11
Obtaining Ds-efficiency for estimating the asymmetric factor under the 5-parameter logistic model.Dseff
Search Ds-optimal design for estimating the asymmetric factor under the 5-parameter logistic model.DsOPT
Obtaining c-efficiency for estimating the EDp under the 5-parameter logistic model.EDpeff
Search c-optimal designs for estimating the EDp under the 5-parameter logistic modelEDpOPT
Gradient of the mean functionf
Partial derivative of the EDp with respect to the model parametersg
Generalized Inverse Matrixginv
Obtain a information matrix at a single design pointinfor
Adjusting invere information matrix being not singularInv
Matrix subtractionMinus
Matrix multiplicationMultiple
Matrix additionPlus
Search the robust D-optimal designs for estimating model parametersRDOPT
Newton Raphson method to get optimal weightsS_weight
Summation of diagonal elements in a matrixSDM
Sub-function of the function D_weight_1smalld1
Sub-function of the function D_weight_2smalldd1
Sensitivity function of D-optimality criterionsmallds1
Multiply a constant to a matrixsMultiple
Transpose of a matrixTrans
Obtain normalized Fisher information matrixupinfor