457 research outputs found
Calibrate Your Eyes to Recognize High-Dimensional Shapes from Their Low-Dimensional Projections
This paper provides a suite of datasets from standard multivariate distributions and simple high-dimensional geomtric shapes that can be used to visually calibrate new users of grand tours. It contains animations of 1-D, 2-D, 3-D, 4-D and 5-D grand tours, links to starting XGobi or XLispStat on the calibration data sets, and C code for generating a grand tour. The purpose of the paper is two-fold: providing code for the grand tour that others could pick up and modify (it is not easy to code this version which is why there are very few implementations currently available), and secondly, provide a variety of training datasets to help new users get a visual sense for high-dimensional data.
Dynamical projections for the visualization of PDFSense data
A recent paper on visualizing the sensitivity of hadronic experiments to
nucleon structure [1] introduces the tool PDFSense which defines measures to
allow the user to judge the sensitivity of PDF fits to a given experiment. The
sensitivity is characterized by high-dimensional data residuals that are
visualized in a 3-d subspace of the 10 first principal components or using
t-SNE [2]. We show how a tour, a dynamic visualisation of high dimensional
data, can extend this tool beyond 3-d relationships. This approach enables
resolving structure orthogonal to the 2-d viewing plane used so far, and hence
finer tuned assessment of the sensitivity.Comment: Format of the animations changed for easier viewin
Model Choice and Diagnostics for Linear Mixed-Effects Models Using Statistics on Street Corners
The complexity of linear mixed-effects (LME) models means that traditional
diagnostics are rendered less effective. This is due to a breakdown of
asymptotic results, boundary issues, and visible patterns in residual plots
that are introduced by the model fitting process. Some of these issues are well
known and adjustments have been proposed. Working with LME models typically
requires that the analyst keeps track of all the special circumstances that may
arise. In this paper we illustrate a simpler but generally applicable approach
to diagnosing LME models. We explain how to use new visual inference methods
for these purposes. The approach provides a unified framework for diagnosing
LME fits and for model selection. We illustrate the use of this approach on
several commonly available data sets. A large-scale Amazon Turk study was used
to validate the methods. R code is provided for the analyses.Comment: 52 pages, 15 figures, 3 table
The effects of Bovine Somatotropin on milk production and milk composition
Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 43-44).Bovine Somatotropin is one of the first major biotechnological developments for agriculture and it is hypothesized that it increases milk production in dairy cattle. It is apparent that Bovine Somatotropin has the potential to be a powerful new tool for the dairy farmer. This study was undertaken to determine the effects of Bovine Somatotropin on milk production and milk composition for dairy cattle. The results of this study indicate that Bovine Somatotropin does influence milk production and milk composition. However, parity and days in milk are also significant variables affecting milk. Treated cows did produce milk longer on average than non-treated cows. However, it is not certain whether the longer length of lactation was due to BST. Therefore, it cannot be determine whether Bovine Somatotropin is the primary variable influencing milk production and milk composition
tourr: An R Package for Exploring Multivariate Data with Projections
This paper describes an R package which produces tours of multivariate data. The package includes functions for creating different types of tours, including grand, guided, and little tours, which project multivariate data (p-D) down to 1, 2, 3, or, more generally, d (⤠p) dimensions. The projected data can be rendered as densities or histograms, scatterplots, anaglyphs, glyphs, scatterplot matrices, parallel coordinate plots, time series or images, and viewed using an R graphics device, passed to GGobi, or saved to disk. A tour path can be stored for visualisation or replay. With this package it is possible to quickly experiment with different, and new, approaches to tours of data. This paper contains animations that can be viewed using the Adobe Acrobat PDF viewer.
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