没有合适的资源？快使用搜索试试~ 我知道了~

首页An Introduction to Applied Multivariate Analysis with R

An Introduction to Applied Multivariate Analysis with R

Multivariate

statistic

需积分: 10 132 浏览量更新于2023-05-24 评论收藏 5.16MB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

试读

284页

An Introduction to Applied Multivariate Analysis with R

资源详情

资源评论

资源推荐

Use R!

Series Editors:

Robert Gentleman Kurt Hornik Giovanni Parmigiani

For other titles published in this series, go to

http://www.springer.com/series/6991

An Introduction to Applied

Multivariate Analysis with R

Brian Everitt • Torsten Hothorn

Series Editors:

Robert Gentleman

Program in Computational Biology

Division of Public Health Sciences

Fred Hutchinson Cancer Research Center

1100 Fairview Avenue, N. M2-B876

USA

Kurt Hornik

Department of Statistik and Mathematik

Wirtschaftsuniversität Wien

A-1090 Wien

Austria

Giovanni Parmigiani

The Sidney Kimmel Comprehensive

Cancer Center at Johns Hopkins University

550 North Broadway

Baltimore, MD 21205-2011

USA

Augasse 2-6

Seattle, Washington 98109

Printed on acid-free paper

Springer New York Dordrecht Heidelberg London

software, or by similar or dissimilar methodology now known or hereafter developed is forbidden.

subject to proprietary rights.

Springer is part of Springer Science+Busi

ness Media (www.springer.com)

ISBN 978-1-4419-9649-7 e-ISBN 978-1-4419-9650-3

DOI 10.1007/978-1-4419-9650-3

Library of Congress Control Number: 2011926793

permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York,

NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in

connection with any form of information storage and retrieval, electronic adaptation, computer

The use in this publication of trade names, trademarks, service marks, and similar terms, even if they

are not identified as such, is not to be taken as an expression of opinion as to whether or not they are

King’s College

London, SE5 8AF

brian.everitt@btopenworld.com

Ludwigstr. 33

80539 München

Germany

Torsten.Hothorn@stat.uni-muenchen.de

Torsten Hothorn Brian Everitt

Institut für Statistik

Ludwig-Maximilians-Universität München

Professor Emeritus

剩余283页未读，继续阅读

请将以下内容进行压缩凝练（注意：可对内容进行删减，对错误进行修正，对语句顺序进行调整，符合美式英语标准，符合英语母语者语言习惯，句子简明易懂，术语使用准确，保留文章结构、不偏离论文主要内容）： The multivariate statistics are used to reduce the dimensionality of the data and thus achieve feature extraction, after which the feature components are applied to detect geochemical anomalies. Statistical analysis of geochemical data based on common statistical variables is required first, and then ANOVA, correlation analysis, regression analysis, cluster analysis, discriminant analysis, factor analysis, etc. are performed on this basis. However, multivariate statistics has obvious shortcomings. It uses mathematical-statistical methods to establish models, and after finding the functional relationship between variables, predictions can be made, but they tend to discuss whether the models or conclusions drawn on small-scale data are true and credible, and the prediction effect is poor. In addition, geochemical data usually do not satisfy normal distribution as well as log-normal distribution, which contradicts the premise of using methods of multivariate statistical analysis.

Multivariate statistics are used for dimensionality reduction and feature extraction, followed by geochemical anomaly detection. First, common statistical variables are analyzed, and then techniques

精简下面表达：Existing protein function prediction methods integrate PPI networks and multivariate bioinformatics data to improve the performance of function prediction. By combining multivariate information, the interactions between proteins become diverse. Different interactions’ functions in functional prediction are various. Combining multiple interactions simply between two proteins can effectively reduce the effect of false negatives and increase the number of predicted functions, but it can also increase the number of false positive functions, which contribute to nonobvious enhancement for the overall functional prediction performance. In this article, we have presented a framework for protein function prediction algorithms based on PPI network and semantic similarity with the addition of protein hierarchical functions to them. The framework relies on diverse clustering algorithms and the calculation of protein semantic similarity for protein function prediction. Classification and similarity calculations for protein pairs clustered by the functional feature are more accurate and reliable, allowing for the prediction of protein function at different functional levels from different proteomes, and giving biological applications greater flexibility.The method proposed in this paper performs well on protein data from wine yeast cells, but how well it matches other data remains to be verified. Yet until now, most unknown proteins have only been able to predict protein function by calculating similarities to their homologues. The predictions result of those unknown proteins without homologues are unstable because they are relatively isolated in the protein interaction network. It is difficult to find one protein with high similarity. In the framework proposed in this article, the number of features selected after clustering and the number of protein features selected for each functional layer has a significant impact on the accuracy of subsequent functional predictions. Therefore, when making feature selection, it is necessary to select as many functional features as possible that are important for the whole interaction network. When an incorrect feature was selected, the prediction results will be somewhat different from the actual function. Thus as a whole, the method proposed in this article has improved the accuracy of protein function prediction based on the PPI network method to a certain extent and reduces the probability of false positive prediction results.

本文提出了一种基于PPI网络和语义相似性，加上蛋白质分层功能的蛋白质功能预测算法框架，对酒葡萄酵母细胞的蛋白质数据表现出良好的效果，但其他数据的效果如何仍有待验证。此外，该框架中的功能特征选择的数量以及每个功能层中蛋白质特征的选择量，对于后续功能预测的准确度有很大影响。因此，在进行特征选择时，必须尽可能选择对整个交互网络重要的多种功能特征，从而提高蛋白质功能预测的准确性，减少错误预测的概率。

Deep Learning for Multivariate Financial Time Series

Matrix Diﬀerential Calculus with Applications in Statistics and Econometrics

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Part One — Matrices 1 Basic properties of vectors and matrices3 1Introduction . . . . . . . . . . . . . .

与Prediction and risk assessment of extreme weather events based on gumbel copula function类似的文献

以下是与“Prediction and risk assessment of extreme weather events based on gumbel copula function”类似的文献推荐： 1. "Multivariate Extreme Value Theory for Risk Assessment" by Alexander McNeil, Rüdiger Frey,

机器学习应用与趋势研究手册算法,方法与技术

Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques Emilio Soria Olivas University of Valencia, Spain José David Martín Guerrero University of Valenc

请帮我查找那些论文里面有大概表述“传统的机器学习算法通常无法处理时间序列数据，也缺乏考虑序列之间关系的能力”这句话意思的论文

NumPy的multivariate_normal函数中协方差矩阵与方差的关系

我知道，NumPy 的 multivariate_normal 函数中协方差矩阵是多变量正态分布的概率密度函数的参数之一，它是方差和相关系数的集合。其中，方差是变量独立分布的概率密度函数的参数，它是协方差矩阵的对角线元素，而相关系数是变量之间的线性关系，它是协方差矩阵的非对角线元素。

R Graphics Essentials for Great Data Visualization

R Graphics Essentials for Great Data Visualization: +200 Practical Examples You Want to Know for Data Science By 作者: Mr Alboukadel Kassambara ISBN-10 书号: 1979748101 ISBN-13 书号: 9781979748100 Edition

z_pred = pyro.sample('z',dist.Normal(z_loc,z_scale).to_event(1))怎么理解

这段代码用 Pyro 框架定义了一个随机变量 "z"，其概率分布为正态分布。具体来说，其均值为 z_loc，标准差为 z_scale。".to_event(1)" 表示这是一个向量分布（multivariate distribution），其中的 1 表示随机变量的维数为 1。可以理解为 z 是一个多元随机变量，其中每个元素都服从相同的正态分布。通过 Pyro 中的采样函数 pyro.sample

qq_42151575

粉丝: 0
资源: 3

上传资源快速赚钱

我的内容管理收起

我的资源快来上传第一个资源

我的收益

登录查看自己的收益

我的积分登录查看自己的积分

我的C币登录后查看C币余额

我的收藏

我的下载

下载帮助

会员权益专享

An Introduction to Applied Multivariate Analysis with R

评论0

会员权益专享

最新资源

An Introduction to Applied Multivariate Analysis with R

评论0

An introduction to multivariate statistical analysis 3rd edition

Functional Data Analysis Second Edition(J.O. Ramsay,B.W. Silverman).pdf

SIMCA14.01用户指南

Categorical Data Analysis 2nd Edition - Agresti

定性数据分析Categorical Data Analysis 2nd Edition - Agresti

统计学的矩阵代数:Matrix algebra from a statistician's perspective,2008

CRC.Social.Big.Data.Mining.149871093X

Deep Learning for Multivariate Financial Time Series

Matrix Diﬀerential Calculus with Applications in Statistics and Econometrics

与Prediction and risk assessment of extreme weather events based on gumbel copula function类似的文献

机器学习应用与趋势研究手册算法,方法与技术

请帮我查找那些论文里面有大概表述“传统的机器学习算法通常无法处理时间序列数据，也缺乏考虑序列之间关系的能力”这句话意思的论文

NumPy的multivariate_normal函数中协方差矩阵与方差的关系

R Graphics Essentials for Great Data Visualization

z_pred = pyro.sample('z',dist.Normal(z_loc,z_scale).to_event(1))怎么理解

会员权益专享

最新资源