Power Analysis for Research Experiments
Create data frame for split-plot design
Expand terms with '||' notation into separate '|' terms
Convert variables to factors as necessary
Determine random-effects expressions from a formula
Create a Design Object for Power Calculation
Design Matrices and Variance Components for Random Effects
Residual Variance-Covariance Matrices
Building Design Matrices and Covariance Structures for Linear Mixed Mo...
Omit terms separated by vertical bars in a formula
Power of omnibus tests
Computes power of t-test for one-dimensional contrast matrices
Computes power of F-test for multi-dimensional contrast matrices
AR(1) Correlation Structure
ARMA(p,q) Correlation Structure
Continuous AR(1) Correlation Structure
Correlation Structure Classes
Compound Symmetry Correlation Structure
Exponential Correlation Structure
Gaussian Correlation Structure
Linear Correlation Structure
Rational Quadratics Correlation Structure
Spherical Correlation Structure
General Correlation Structure
Creation of Standard Experimental Designs
Create a data frame for Crossover design
Create a data frame of completely randomized design
Create a data frame for Latin square design
Create a data frame of randomized complete block design
Power of contrasts
Power for model coefficients
Substitute Bars
Provides tools for calculating statistical power for experiments analyzed using linear mixed models. It supports standard designs, including randomized block, split-plot, and Latin Square designs, while offering flexibility to accommodate a variety of other complex study designs.
Useful links