Scidoe

Scidoe

Design of Experiments Toolbox

Purpose

The goal of this toolbox is to provide design of experiments techniques, along with functions for model building.

Features

Here is the list of functions which are available.

Designs

  • scidoe_bbdesign — Box-Benhken design of experiments
  • scidoe_ccdesign — A Central Composite Design of Experiments
  • scidoe_ff2n — Full factorial design with 2 levels
  • scidoe_fracfact — Fractional Factorial Design
  • scidoe_fullfact — Full factorial design
  • scidoe_lhsdesign — Latin Hypercube Sampling
  • scidoe_star — Produces a star point design of experiments

Models

  • scidoe_ryates — Reverse Yates algorithm to give estimated responses
  • scidoe_yates — Calculates main and interaction effects using Yate's algorithm.

Support

  • scidoe_compare — The default comparison function used in the sort-merge.
  • scidoe_pdist — Pairwise point distances of a matrix
  • scidoe_plotcube — Plots a d dimensions cube in [-1,1].
  • scidoe_sort — A flexible sorting function.
  • scidoe_sortdesign — Sort the experiments of a design of experiments
  • scidoe_squareform — Format distance matrix
  • scidoe_string — Sort the experiments of a design of experiments

Goals

Here is the list of functions which will be created at the end of the project.

Optimal Designs

  • scidoe_optdesign: Optimal design (a-optimal)
  • scidoe_optdesign: Optimal Design based on a criterion.
  • mtlb_doptdesign: Matlab compatible D-optimal Design

Supersaturated Designs

  • scidoe_comp_ssd: Supersaturated Design ('a-optimal').
  • scidoe_comp_ssd: Supersaturated Design based on a criterion.

Model Building

  • scidoe_poly_model: Produces a polynomial model
  • scidoe_model_select: Produces a new polynomial model using forward or backward selection.
  • scidoe_plot_model: Plots regression line and residuals distribution
  • scidoe_build_regression_matrix: Regression matrix of a model
  • scidoe_var_regression_matrix: Regression matrix of the variance of a model
  • scidoe_lars: Least Angle Regression or Lasso Regression
  • scidoe_rsquared: R2 Computation

General Functions

  • scidoe_unnorm_doe_matrix: Adjusts high and low values of a design to specified maximum and minimum values
  • scidoe_comp_WD2_crit: Wrap-around L2 discrepancy criterion
  • scidoe_comp_CL2_crit: Centered L2 discrepancy criterion
  • scidoe_crossvalidate: K-flod cross validation
  • scidoe_cvplot: Plots cross validation results
  • scidoe_prbs: A pseudo random binary signal generator
  • scidoe_merge: Merges two samples
  • scidoe_diff: Computes the difference of two samples
  • scidoe_scramble: Permutes a sample
  • scidoe_standardize: Center and normalize a sample
  • scidoe_normalize: Normalises a sample

Dependencies

  • This module depends on the helptbx module (to update the help pages).
  • This module depends on the apifun module (>= v0.3).
  • This module depends on the assert module.
  • This module depends on the specfun module (>=v0.2).
  • This module depends on the stixbox module (>=v2.2).

Authors

  • Copyright (C) 2012-2013 - Michael Baudin
  • Copyright (C) 2012 - Maria Christopoulou
  • Copyright (C) 2009 - Yann Collette
  • Copyright (C) 2001 - Per A. Brodtkorb

License

This toolbox is released under the terms of the CeCILL license :

http://www.cecill.info/index.en.html

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