ldnbinom: Locally D-optimal designs for Negative Binomial model In LDOD: Finding Locally D-optimal optimal designs for some nonlinear and generalized linear models.

Description

Finds Locally D-optimal designs for Negative Binomial regression model which is defined as E(y) = λ(x) with Var(y) = σ^2λ(x)(1+(λ(x)/θ)), where y ~ NB(θ, λ(x)), λ(x) = a\exp(-bx) and a, b and σ are unknown parameters.

Usage

 1 2 ldnbinom(a, b, theta, lb, ub, user.points = NULL, user.weights = NULL, ..., n.restarts = 1, n.sim = 1, tol = 1e-8, prec = 53, rseed = NULL)

Arguments

 a initial value for paremeter a. b initial value for paremeter b. theta initial value for paremeter θ which is the number of successes in a sequence of Bernoulli trials, must be a Natural number. lb lower bound of design interval. ub upper bound of design interval. user.points (optional) vector of user design points which calculation of its D-efficiency is aimed. Each element of user.points must be within the design interval. user.weights (optional) vector of weights which its elements correspond to user.points elements. The sum of weights should be 1; otherwise they will be normalized. ... (optional) additional parameters will be passed to function curve. prec (optional) a number, the maximal precision to be used for D-efficiency calculation, in bite. Must be at least 2 (default 53), see 'Details'. n.restarts (optional optimization parameter) number of solver restarts required in optimization process (default 1), see 'Details'. n.sim (optional optimization parameter) number of random parameters to generate for every restart of solver in optimization process (default 1), see 'Details'. tol (optional optimization parameter) relative tolerance on feasibility and optimality in optimization process (default 1e-8). rseed (optional optimization parameter) a seed to initiate the random number generator, else system time will be used.

Details

While D-efficiency is NaN, an increase in the value of prec can be beneficial to achieve a numeric value, however, can slow down the calculation speed.

Values of n.restarts and n.sim should be chosen according to the length of design interval.

Value

plot of derivative function, see 'Note'.

a list containing the following values:

 points obtained design points weights corresponding weights to the obtained design points det.value value of Fisher information matrix determinant at the obtained design user.eff D-efficeincy of user design, if user.design and user.weights are not NULL.

Note

To verify optimality of obtained design, derivate function (symmetry of Frechet derivative with respect to the x-axis) will be plotted on the design interval. Based on the equivalence theorem (Kiefer, 1974), a design is optimal if and only if its derivative function are equal or less than 0 on the design interval. The equality must be achieved just at the obtained points.

Author(s)

Ehsan Masoudi, Majid Sarmad and Hooshang Talebi

References

Masoudi, E., Sarmad, M. and Talebi, H. 2012, An Almost General Code in R to Find Optimal Design, In Proceedings of the 1st ISM International Statistical Conference 2012, 292-297.

Rodriguez-Torreblanca, C. Rodriguez-Diaz, J.M. (2007), Locally D- and c-optimal designs for Poisson and negative binomial regression models, Metrika, 66, 161-172.

Kiefer, J. C. 1974, General equivalence theory for optimum designs (approximate theory), Ann. Statist., 2, 849-879.

cfisher, cfderiv and eff.

Examples

 1 2 3 4 5 6 ldnbinom(a = 2, b = 3, theta = 10, lb = -3, ub =3) # $points: -3.0000000 -0.8115872 ## D-effecincy computation: ldnbinom(a = 2, b = 3, theta = 10, lb = -3, ub =3, user.points = c(2, -3), user.weights = rep(.5, 2)) #$user.eff: 0.06099

Example output  Attaching package: 'gmp'

The following objects are masked from 'package:base':

%*%, apply, crossprod, matrix, tcrossprod

C code of R package 'Rmpfr': GMP using 64 bits per limb

Attaching package: 'Rmpfr'

The following objects are masked from 'package:stats':

dbinom, dnorm, dpois, pnorm

The following objects are masked from 'package:base':

cbind, pmax, pmin, rbind

Iter: 1 fn: -3.0350	 Pars:  -3.00000 -0.81159
Iter: 2 fn: -3.0350	 Pars:  -3.00000 -0.81159
Iter: 3 fn: -3.0350	 Pars:  -3.00000 -0.81159
solnp--> Completed in 3 iterations
$points  -3.0000000 -0.8115872$weights
 0.5 0.5

$det.value  20.80097 Iter: 1 fn: -3.0350 Pars: -0.81159 -3.00000 Iter: 2 fn: -3.0350 Pars: -0.81159 -3.00000 Iter: 3 fn: -3.0350 Pars: -0.81159 -3.00000 solnp--> Completed in 3 iterations$points
 -3.0000000 -0.8115872

$weights  0.5 0.5$det.value
 20.80097

\$user.eff
 0.06099

LDOD documentation built on May 2, 2019, 3:26 a.m.