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Titlebook: Chemometrics with R; Multivariate Data An Ron Wehrens Book 2020Latest edition Springer-Verlag GmbH Germany, part of Springer Nature 2020 Mu

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發(fā)表于 2025-3-21 16:51:22 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Chemometrics with R
副標題Multivariate Data An
編輯Ron Wehrens
視頻videohttp://file.papertrans.cn/225/224926/224926.mp4
概述Presents an easy, non-mathematical approach to multivariate statistics in the life sciences.Illustrated with examples that are fully reproducible.Provides the R codes discussed in the text
叢書名稱Use R!
圖書封面Titlebook: Chemometrics with R; Multivariate Data An Ron Wehrens Book 2020Latest edition Springer-Verlag GmbH Germany, part of Springer Nature 2020 Mu
描述.This book offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a comprehensive description of the general data analysis paradigm, from exploratory analysis (principal component analysis, self-organizing maps and clustering) to modeling (classification, regression) and validation (including variable selection). It also includes a special section discussing several more specific topics in the area of chemometrics, such as outlier detection, and biomarker identification. The corresponding R code is provided for all the examples in the book; and scripts, functions and data are available in a separate R package. This second revised edition features not only updates on many of the topics covered, but also several sections of new material (e.g., on handling missing values in PCA, multivariate process monitoring and batch correction)...?.
出版日期Book 2020Latest edition
關(guān)鍵詞Multivariate statistics; Clustering; Principal Component Analysis; R software; Variable Selection; Linear
版次2
doihttps://doi.org/10.1007/978-3-662-62027-4
isbn_softcover978-3-662-62026-7
isbn_ebook978-3-662-62027-4Series ISSN 2197-5736 Series E-ISSN 2197-5744
issn_series 2197-5736
copyrightSpringer-Verlag GmbH Germany, part of Springer Nature 2020
The information of publication is updating

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,Nützliches im Umgang mit SPSS,As we saw earlier in the visualizations provided by methods like PCA and SOM, it is often interesting to look for structure, or groupings, in the data. However, these methods do not explicitly define clusters; that is left to the pattern recognition capabilities of the scientist studying the plot.
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發(fā)表于 2025-3-22 07:14:18 | 只看該作者
Konzeptgebundene SteuerungsverfahrenThe goal of classification, also known as supervised pattern recognition, is to provide a model that yields the optimal discrimination between several classes in terms of predictive performance.
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Self-Organizing MapsIn PCA, the most outlying data points determine the direction of the PCs—these are the ones contributing most to the variance. This often results in score plots showing a large group of points close to the center.
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