This package contains functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.
The osDesign serves for planning an observational study. Currently, functionality is focused on the two-phase and case-control designs. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model.
The support.CEs package provides seven basic functions that support an implementation of choice experiments.
Simulation functions to assess the optimal design and performance of random intercept and slope models (i.e. mixed models), which can be used to a priori determine adequate sampling designs for e.g. reaction norm studies. Functions allow users to vary the sampling design in terms of number of grouping units sampled (e.g. individuals, schools, populations, etc.) and replicates per grouping unit (unbalanced as well balanced datasets) and also allow users to vary the parameter conditions used to generate the data. Subsequently, the performance of mixed models (based on lme4 package) fitted on these datasets is assessed in terms of the accuracy and the precision of estimates of fixed and random parameter, as well as the statistical power.
This package calls a modification of the published FORTRAN code for producing variance dispersion graphs. For more details on variance dispersion graphs see "A Computer Program for Generating Variance Dispersion Graphs" by G. Vining, Journal of Quality Technology, Vol. 25 No. 1 January 1993.
The target equivalence range (TEQR) design is a frequentist implementation of the modified toxicity probability interval (mTPI) design and a competitor to the standard 3+3 design (3+3). The 3+3 is the work horse design in Phase I. It is good at determining if a safe dose exits, but provides poor accuracy and precision in estimating the level of toxicity at the maximum tolerated dose (MTD). The TEQR is better than the 3+3 when compared on: 1) the number of times the dose at or nearest the target toxicity level was selected as the MTD, 2) the number of subjects assigned to doses levels, at or nearest the MTD, and 3) the overall trial DLT rate. TEQR more accurately and more precisely estimates the rate of toxicity at the MTD because a larger number of subjects are studied at the MTD dose. The TEQR on average uses fewer subjects and provide reasonably comparable results to the continual reassessment method (CRM) in the number of times the dose at or nearest the target toxicity level was selected as the MTD and the number of subjects assigned doses, at, or nearest the target and in overall DLT rate.
mxkssd is a package that generates efficient balanced mixed-level k-circulant supersaturated designs by interchanging the elements of the generator vector. The package tries to generate a supersaturated design that has EfNOD efficiency more than user specified efficiency level (mef). The package also displays the progress of generation of an efficient mixed-level k-circulant design through a progress bar. The progress of 100 per cent means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs.
View 2D/3D sections or contours of computer experiments designs, surrogates or test functions.
mkssd is a package that generates efficient balanced non-aliased multi-level k-circulant supersaturated designs by interchanging the elements of the generator vector. The package tries to generate a supersaturated design that has chisquare efficiency more than user specified efficiency level (mef). The package also displays the progress of generation of an efficient multi-level k-circulant design through a progress bar. The progress of 100% means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs.
This package analyses complex ANOVA models with any combination of orthogonal/nested and fixed/random factors, as described by Underwood (1997). There are two restrictions: (i) data must be balanced; (ii) fixed nested factors are not allowed. Homogeneity of variances is checked using Cochran's C test and 'a posteriori' comparisons of means are done using Student-Newman-Keuls (SNK) procedure.
