Garson, G. David (north Carolina State University, Raleigh, Usa) Factor analysis and dimension reduction in r 9781032246697

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Автор: Garson, G. David (north Carolina State University, Raleigh, Usa)
Название: Factor analysis and dimension reduction in r
ISBN: 9781032246697
Издательство: Taylor&Francis
Классификация:

Методы исследований: Общие положения

Социальные исследования и статистика

Психологические теории и школы мысли

Психологическая методология

Экономическая статистика

Вероятность и статистика

ISBN-10: 1032246693
Обложка/Формат: Paperback
Страницы: 564
Вес: 1.08 кг.
Дата издания: 12.12.2022
Язык: English
Иллюстрации: 1 tables, black and white; 129 line drawings, color; 16 line drawings, black and white; 129 illustrations, color; 16 illustrations, black and white
Размер: 244 x 175 x 39
Подзаголовок: A social scientist`s toolkit
Рейтинг:
Поставляется из: Европейский союз
Описание: Factor Analysis and Dimension Reduction in R provides coverage, with worked examples, of a large number of dimension reduction procedures along with model performance metrics to compare them. Factor analysis in the form of principal components analysis (PCA) or principal factor analysis (PFA) is familiar to most social scientists. However, what is less familiar is understanding that factor analysis is a subset of the more general statistical family of dimension reduction methods.The social scientists toolkit for factor analysis problems can be expanded to include the range of solutions this book presents. In addition to covering FA and PCA with orthogonal and oblique rotation, this book’s coverage includes higher-order factor models, bifactor models, models based on binary and ordinal data, models based on mixed data, generalized low-rank models, cluster analysis with GLRM, models involving supplemental variables or observations, Bayesian factor analysis, regularized factor analysis, testing for unidimensionality, and prediction with factor scores. The second half of the book deals with other procedures for dimension reduction. These include coverage of kernel PCA, factor analysis with multidimensional scaling, locally linear embedding models, Laplacian eigenmaps, diffusion maps, force directed methods, t-distributed stochastic neighbor embedding, independent component analysis (ICA), dimensionality reduction via regression (DRR), non-negative matrix factorization (NNMF), Isomap, Autoencoder, uniform manifold approximation and projection (UMAP) models, neural network models, and longitudinal factor analysis models. In addition, a special chapter covers metrics for comparing model performance.Features of this book include:Numerous worked examples with replicable R codeExplicit comprehensive coverage of data assumptionsAdaptation of factor methods to binary, ordinal, and categorical dataRe
Дополнительное описание: PART I: MULTIVARIATE ANALYSIS OF FACTORS AND COMPONENTSChapter 1: Factor Analysis: Purposes and Research Questions Chapter 2: Dealing with the Assumptions and Limitations of Factor AnalysisChapter 3: Fundamental Concepts and Functions in Factor AnalysisCh

Factor analysis and dimension reduction in r, Garson, G. David (north Carolina State University, Raleigh, Usa)