Designs for fitting Poisson regression model

Experiments related to herbicides or insecticides usually have the objective to find the effective concentration of the chemicals to control weeds or insects and to understand the relationship between the response and explanatory variables. The response is the number or proportion of organisms died and thus, is count data. The present study deals with the problem of developing experimental designs under Poisson regression model, which is a nonlinear model with count data as response. The focus here is to determine the unknown parameters of the model efficiently. The statistical designs generated are saturated and their performance is found better than traditionally used equally spaced designs. A simulation study is presented to demonstrate the application of the generated designs in actual experiment

D-Optimal Designs for Exponential and Poisson Regression Models

This paper deals with optimality aspects of block designs balanced for interference effects from neighboring units on both sides under a general non additive model along with random block effects. Here, a class of complete, circular block designs strongly balanced for interference effects has been shown to be universally optimal for the estimation of direct effects and interference effects (left and right) of treatments under a non additive mixed effects model

Algorithmic Approaches to Construct Smaller Experimental Designs

Experimentation has basically one of the two purposes, either to find some new piece of information or to validate any existing information. Many agricultural, veterinary and fisheries experiments have gone beyond just comparing treatments and testing hypothesis. Experiments related to ration optimization, growth models, biochemical reactions, toxicity, and chemical efficacy studies require much complex and nontraditional models, consequently needing customized specific statistical designs

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Not AvailableAn R package to Generate cost effective minimally changed run sequences for symmetrical as well as asymmetrical factorial designs.Not Availabl

minimalRSD: Minimally Changed CCD and BBD

Not AvailableR pachage to generate central composite designs (CCD)with full as well as fractional factorial points (half replicate) and Box Behnken designs (BBD) with minimally changed run sequence.Not Availabl

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Not AvailableIn the present study, the class of nonlinear models, with intrinsically linearly related mean response and input variables, were explored for the generation of locally D-optimal designs. It has been found that these models have the advantage of design construction in transformed or coded design space with suitable transformation in initial parameter guesses. Exponential and Poisson regression models with two continuous input variables were investigated. For the construction of D-optimal designs, the modified version of Fedorov algorithm was used that require a suitable candidate set representing the design space along with the initial parameter guesses. The efficient method of constructing the candidate sets with respect to each model is proposed. The optimality of generated designs was validated using general equivalence theorem.Not Availabl

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Version: 1.0.0 Imports: utils, minimalRSD, stats Published:2017-03-21 Author: Shwetank Lall [aut, cre], Arpan Bhowmik [ctb], Eldho Varghese [aut], Seema Jaggi [ctb], Cini Varghese [ctb] Maintainer: Shwetank Lall License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] NeedsCompilation: no Citation: FMC citation info In views: ExperimentalDesignAn R package to generate cost effective minimally changed run sequences for symmetrical as well as asymmetrical factorial designsNot Availabl

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Not AvailableExperiments related to herbicides or insecticides usually have the objective to find the effective concentration of the chemicals to control weeds or insects and to understand the relationship between the response and explanatory variables. The response is the number or proportion of organisms died and thus, is count data. The present study deals with the problem of developing experimental designs under Poisson regression model, which is a nonlinear model with count data as response. The focus here is to determine the unknown parameters of the model efficiently. The statistical designs generated are saturated and their performance is found better than traditionally used equally spaced designs. A simulation study is presented to demonstrate the application of the generated designs in actual experiment.Not Availabl

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Not AvailableAn R package which Generate central composite designs (CCD)with full as well as fractional factorial points (half replicate) and Box Behnken designs (BBD) with minimally changed run sequence.Not Availabl

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Not AvailableIn this paper, locally D-optimal saturated designs for a logistic model with one and two continuous input variables have been constructed by modifying the famous Fedorov exchange algorithm. A saturated design not only ensures the minimum number of runs in the design but also simplifies the row exchange computation. The basic idea is to exchange a design point with a point from the design space. The algorithm performs the best row exchange between design points and points form a candidate set representing the design space. Naturally, the resultant designs depend on the candidate set. For gain in precision, intuitively a candidate set with a larger number of points and the low discrepancy is desirable, but it increases the computational cost. Apart from the modification in row exchange computation, we propose implementing the algorithm in two stages. Initially, construct a design with a candidate set of affordable size and then later generate a new candidate set around the points of design searched in the former stage. In order to validate the optimality of constructed designs, we have used the general equivalence theorem. Algorithms for the construction of optimal designs have been implemented by developing suitable codes in R.Not Availabl