MetaHD is an R package that performs multivariate meta-analysis for high-dimensional metabolomics data for integrating and collectively analysing individual-level metabolomics data generated from multiple studies as well as for combining summary estimates. This approach accounts for correlation between metabolites, considers variability within and between studies, handles missing values and uses shrinkage estimation to allow for high dimensionality. Read more →

This online course describe how to extract and use open source data for factor analysis in R. […] Course Overview Hi All, Welcome to the Online Course on “Exploratory Factor Analysis in R”. This is an online course designed to deepen your understanding of how to conduct factor analysis in R. The target audience are graduate students, researchers, and anyone interested in learning how to use open-source data for factor analysis. Factor analysis is a data reduction method used to explore and validate the structure of observed variables in multivariate data. Please read the course syllabus … Read more →

This is a minimal example of using the bookdown package to write a book. The HTML output format for this example is bookdown::bs4_book, set in the _output.yml file. [...] This tutorial aims to show how methods described in the two papers by Simon S. Skene and Michael G. Kenward1,2 (paper I and paper II) can be applied in R. In these papers, it is assumed that the data can be represented by a multivariate Gaussian linear model. The model has the following form: \[\begin{equation} y_i \sim N(X_i \beta;\Sigma_i ), \quad i =1, \dots,n \tag{1.1} \end{equation}\] where \(y_i\) \((T_i \times ... Read more →

Lab notes for Statistics for Social Sciences II: Multivariate Techniques […] Welcome to the lab notes for Statistics for Social Sciences II: Multivariate Techniques. Along these notes we will see how to effectively implement the statistical methods presented in the lectures. The exposition we will follow is based on learning by analyzing datasets and real-case studies, always with the help of statistical software. While doing so, we will illustrate the key insights of some multivariate techniques and the adequate use of advanced statistical software. Be advised that these notes are neither … Read more →

Notes of Analysis of Functional MRI […] Brain activation to the corresponding task. Similarity between local BOLD signal and task design. “Where is the motion area?” “Where is the face recognition area?” 分析工具：General Linear Model (GLM)。 Brain encoding to the representation of stimulus classes. Classification or similarity analysis. “What are the varying brain states in an area?” “How do brain cortices encode different types of information?” 分析工具： Multivariate Pattern Analysis (MVPA). Classifier‐based MVPA, pattern similarity analysis. Brain … Read more →

Lecture notes for Calculus and Applications produced in bookdown […] These are lecture notes for the second part of Calculus and Applications first year module at the Department of Mathematics, Imperial College London. The notes are split into three parts on Fourier Transform, Ordinary Differential Equations and Introduction to Multivariate Calculus. Please refer to course Blackboard for additional materials recommended text books for further reading. These lecture notes are adobted from existing courses in our department. Part I of the course is based on the old M2AA2 course (Andrew … Read more →

The output format for this book is bookdown::gitbook. […] This book is an introduction to statistical methods used in monitoring, controlling, and improving quality. Topics covered are: acceptance sampling; Shewhart control charts for Phase I studies; graphical and statistical tools for discovering and eliminating the cause of out-of-control-conditions; Cusum and EWMA control charts for Phase II process monitoring; design and analysis of experiments for process troubleshooting and discovering ways to improve process output; and multivariate control charts for Phase I and Phase II studies … Read more →

Multivariate Statistical Analysis with R: PCA & Friends making a Hotdog […] Multivariate Analysis has been developed and evolved through many iterations by many different disciplines. Virtually all scientific domains need to use statistical methods under the Multivariate umbrella to analyze data with more than 1 variable. Thus, Multivariate Analysis has gotten many names and has been customized by many “-metric” disciplines throughout the years. Overall, Multivariate Analysis explore the relationships between observations and/or variables in a multivariate dataset. The strategies commonly … Read more →

This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data you have. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing informative data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data. Read more →

One, two, and multiple-table analyses. […] Advice: Use the simplest method that provides the clearest … Read more →

In 1980 members of the Department of Data Theory at the University of Leiden taught a post-doctoral course in Nonlinear Multivariate Analysis. The course content was sort-of-published, in Dutch, as Gifi (1980). The course was repeated in 1981, and this time the sort-of-published version (Gifi (1981)) was in English. The preface gives some details about the author. The text is the joint product of the members of the Department of Data Theory of the Faculty of Social Sciences, University of Leiden. ‘Albert Gifi’ is their ‘nom de plume’. The portrait, however, of Albert Gifi shown here, is that … Read more →

The book discusses block relaxation, alternating least squares, augmentation, and majorization algorithms to minimize loss functions, with applications in statistics, multivariate analysis, and multidimensional scaling. […] Many recent algorithms in computational statistics are variations on a common theme. In this book we discuss four such classes of algorithms. Or, more precisely, we discuss a single large class of algorithms, and we show how various well-known classes of statistical algorithms fit into this common framework. The types of algorithms we consider are, in logical order, … Read more →