Optimal Designs for the 5-Parameter Logistic Model

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Documentation for package ‘Opt5PL’ version 0.1.1

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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 parameters
c_weight_2 The second derivative of the c-optimality criterion with respect to the model parameters
D1 Computing each element of the function c_weight_1
d11 Computing each element of the function DD_weight_1
DD1 Computing each element of the function c_weight_2
dd11 Computing each element of the function DD_weight_2
DD_weight One iteration to run Newton Raphson to get Ds-optimal weights
DD_weight_1 The first derivative of the Ds-optimality criterion with respect to the model parameters
DD_weight_2 The second derivative of the Ds-optimality criterion with respect to the model parameters
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 5-parameter logistic model.
DsOPT Search Ds-optimal design for estimating the asymmetric factor under the 5-parameter logistic model.
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 parameters
D_weight_2 The second derivative of the D-optimality criterion w.r.t the model parameters
EDpeff Obtaining c-efficiency for estimating the EDp under the 5-parameter logistic model.
EDpOPT Search c-optimal designs for estimating the EDp under the 5-parameter logistic model
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
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
S_weight Newton Raphson method to get optimal weights
Trans Transpose of a matrix
upinfor Obtain normalized Fisher information matrix