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Segmentation and Classification of Accelerometer Data
Segmentation and classification procedures for data from the 'Activinsights GENEActiv' < https://www.activinsights.com/products/geneactiv/> accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing. Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the 'rpart' package to create classification models.
Mango Solutions Training Datasets
Datasets to be used primarily in conjunction with Mango Solutions training materials but also for the book 'SAMS Teach Yourself R in 24 Hours' (ISBN: 978-0-672-33848-9). Version 1.0-7 is largely for use with the book; however, version 1.1 has a much greater focus on use with training materials, whilst retaining compatibility with the book.
Full Factorial Breeding Analysis
We facilitate the analysis of full factorial mating designs with mixed-effects models. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap confidence intervals, which can then be plotted using a simple function. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses. The paper associated with the package including worked examples is: Houde ALS, Pitcher TE (2016)