An Introduction to Applied Multivariate Analysis with R

  title={An Introduction to Applied Multivariate Analysis with R},
  author={B. S. Everitt and Torsten Hothorn},
Multivariate data and multivariate analysis.- Looking at multivariate data: visualization.- Principal components analysis.- Multidimensional scaling.- Exploratory factor analysis.- Cluster analysis.- Confirmatory factor analysis and structural equation models.- The analysis of repeated measures data.- 

Modelling Population Heterogeneity: A simulation study

This work compared several variables selection techniques for clustering high-dimensional data with underlying structure (similar variablepro les) and found K-means algorithm was used as a clustering techniquesthroughout.

Applied Multivariate Statistics with R

Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications, making these analytical methods accessible without lengthy mathematical derivations.

Principal Component Analysis using Singular Value Decomposition of Microarray Data

This work uses DNA microarray data for the small round blue cell tumors of childhood by Khan et al. (2001) to decide the number of components which account for sufficient amount of information in principal component analysis (PCA), and draws scree plot to reveal important features that exhibit relationship between variables.

Multivariate Analysis and Visualization using R Package muvis

The main aim of the package is to provide a wide range of users with different levels of expertise in R with a set of tools for comprehensive analysis of multivariate datasets, and exploited the NHANES dataset to declare the functionality of muvis in practice.

In Silico Comparative Evaluation of Classical and Robust Dimension Reduction for Psychological Assessment

Based on in silico benchmarking using various simulated multivariate conditions, ROBPCA was consistent in correctly identifying the true q-dimensions and can be improved through the adjustment of values for the cumulative proportion of variation and eigenvalue thresholds.

Multivariate Linear Models: General

This chapter introduces the basic theory for linear models with more than one dependent variable.

Correlated components

Principal components analysis is a much used and practical technique for analysing multivariate data, finding a particular set of linear compounds of the variables under consideration, such that

Utility of PCA and Other Data Transformation Techniques in Exoplanet Research

This paper focuses on the utility of various data transformation techniques, which might be under the principal component analysis (PCA) category, on exoplanet research. The first section introduces

Reproducible analysis of disease space via principal components using the novel R package syndRomics

A new software package is presented, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis, an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns.

Meeting Student Needs for Multivariate Data Analysis: A Case Study in Teaching an Undergraduate Multivariate Data Analysis Course

This case study describes the experience in designing and teaching an undergraduate course on multivariate data analysis with minimal prerequisites, using real data, active learning, and other interactive activities to help students tackle the material.



History of Factor Analysis: A Psychological Perspective

The history of unrestricted (exploratory) factor analysis is briefly reviewed from the perspective of its contribution to psychological theory, particularly models of mental abilities. Attention is


  • J. Wolfe
  • Mathematics
    Multivariate behavioral research
  • 1970
The maximum-likelihood theory and numerical solution techniques are developed for a fairly general class of distributions and the feasibility of the procedures is demonstrated by two examples of computer solutions for normal mixture models of the Fisher Iris data and of artify generated clusters with unequal covariance matrices.

Stability analysis in K-means clustering.

  • D. Steinley
  • Economics
    The British journal of mathematical and statistical psychology
  • 2008
This paper develops a new procedure, called stability analysis, for K-means clustering, that takes advantage of additional information from a K-Means cluster analysis to determine the overall structure of a data set, the number of clusters and the multidimensional relationships between the clusters.

Multiple imputation: a primer

  • J. Schafer
  • Psychology
    Statistical methods in medical research
  • 1999
Essential features of multiple imputation are reviewed, with answers to frequently asked questions about using the method in practice.

A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators

A structural equation model is proposed with a generalized measurement part, allowing for dichotomous and ordered categorical variables (indicators) in addition to continuous ones. A computationally

Statistical analysis of finite mixture distributions

This course discusses Mathematical Aspects of Mixtures, Sequential Problems and Procedures, and Applications of Finite Mixture Models.

Nonmetric multidimensional scaling: A numerical method

The numerical methods required in the approach to multi-dimensional scaling are described and the rationale of this approach has appeared previously.

Factor Score Estimation

Factors scores are measures of principal components or common factors. Under the principal components model, the factor scores are uniquely determined; under the common factor model, they are not. In

Generalized Latent Variable Modeling: Multilevel,Longitudinal, and Structural Equation Models

To fully grasp some of the more complex aspects of sensitivity analysis, additional references are needed to supplement this text, and the technical material seemed overly complicated and lacking sufficient explanation.

Market Segmentation Using Brand Strategy Research: Bayesian Inference with Respect to Mixtures of Log-Linear Models

This paper presents a Bayesian model based clustering approach for dichotomous item responses that deals with issues often encountered in model based clustering like missing data, large data sets and