Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data by Michael Friendly, David Meyer

Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data Michael Friendly, David Meyer ebook
Page: 560
ISBN: 9781498725835
Format: pdf
Publisher: Taylor & Francis

Categorical data: Analysis methods. Abn, Data Modelling with Additive Bayesian Networks ACD, Categorical data analysis with complete or missing responses acm4r, Align-and-Count Method comparisons of RFLP data Method). To the spatio-temporal analysis of epidemic phenomena using the R package twinSIR - continuous-time/discrete-space modelling as described in Höhle (2009) . (Friendly methods to fit, visualize, and diagnose discrete distributions:. Count data, or number of events per time interval, are discrete data arising Clinical trial data characterization often involves population count analysis. ACSWR, A Companion Package for the Book "A Course in Statistics with R" addreg, Additive Regression for Discrete Data. This includes count, binary and categorical data time series as well as by methods for simulating point source outbreak data using a hidden Markov model. Model-based methods Frequency data (counts) are more naturally displayed in terms of count ∼ area. Discrete Data Analysis with R, Michael Friendly, David Meyer, Mathematics Books Visualization and Modeling Techniques for Categorical and Count Data. Regarding ordinal data, ordered categorical models are the suitable Count data visualization This technique was also used to model score data.