28 papers · ranked by Valyu relevance
Amira Ibrahim El-Desokey
In this paper, I outline several conceptual and methodological issues related to modeling individual and group processes embedded in clustered/hierarchical data structures. We position multilevel modeling techniques within a broader set of univariate and multivariate methods commonly used to study different types of…
Marléne Baumeister, Marc Ditzhaus, Markus Pauly
Multivariate analysis-of-variance (MANOVA) is a well established tool to examine multivariate endpoints. While classical approaches depend on restrictive assumptions like normality and homogeneity, there is a recent trend to more general and flexible procedures. In this paper, we proceed on this path, but do not follow…
Ellen F. Mosleth, Kristian Hovde Liland
Modern analysis technologies output large amounts of multivariate data. The data may come from an experimental design or from other collected observations. We here present a flexible tool, called General Effect Modelling (GEM), for the analysis of any type of multivariate data influenced by one or more qualitative…
Christophe Boetto, Arthur Frouin, Léo Henches, Antoine Auvergne + 11 more
Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix…
Diego Garrido-Martín, Miquel Calvo, Ferran Reverter, Roderic Guigó
The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations…
Cristiano Ialongo
Title: Graphical abstract
Will Penny, Tom Sambrook, Louis Renoult
Factorial designs are a mainstay of the scientific paradigm, allowing the effects of multiple experimental factors and their interactions to be efficiently studied within a single experiment. In brain imaging, however, multivariate data analyses commonly proceed using multivariate decoding and we argue that the…
Daniel Krähmer, Cristobal Young, Nate Breznau
Multiverse analysis is an increasingly popular tool for improving the robustness and transparency of empirical research. Yet, visualization techniques for multiverse analysis are underdeveloped. We identify critical weaknesses in existing multiverse visualizations-specification curves and density plots-and introduce a…
Shuichi Kawano, Toshikazu Fukushima, Junichi Nakagawa, Mamoru Oshiki
The multivariate regression model basically offers the analysis of a single dataset with multiple responses. However, such a single-dataset analysis often leads to unsatisfactory results. Integrative analysis is an effective method to pool useful information from multiple independent datasets and provides better…
José Camacho, Jokin Ezenarro, Daniel Schorn-García, Johan A. Westerhuis
ANOVA Simultaneous Component Analysis (ASCA) is the current state-of-theart chemometric tool for analyzing and interpreting high-dimensional experimental data from a Design of Experiment (DoE). Being a multivariate extension of the ANOVA, ASCA makes a perfect tandem with DoE. This tutorial review recommends best…
Paolo Girardi, Anna Vesely, Daniël Lakens, Gianmarco Altoè + 3 more
'Massimiliano Pastore' 'Antonio Calcagnì' 'Livio Finos'] When analyzing data researchers make some decisions that are either arbitrary, based on subjective beliefs about the data generating process, or for which equally justifiable alternative choices could have been made. This wide range of data-analytic choices can…
Anita Brobbey, Samuel Wiebe, Alberto Nettel-Aguirre, Colin Bruce Josephson + 3 more
generalized estimation equations Authors: ['Anita Brobbey' 'Samuel Wiebe' 'Alberto Nettel-Aguirre' 'Colin Bruce Josephson' 'Tyler Williamson' 'Lisa M Lix' 'Tolulope T. Sajobi'] Discriminant analysis procedures that assume parsimonious covariance and/or means structures have been proposed for distinguishing between two…
Mark de Rooij, Ligaya Breemer, Dion Woestenburg, Frank Busing
We present a multidimensional data analysis framework for the analysis of ordinal response variables. Underlying the ordinal variables, we assume a continuous latent variable, leading to cumulative logit models. The framework includes unsupervised methods, when no predictor variables are available, and supervised…
Alexander von Eye, Wolfgang Wiedermann
Unless very large samples are available, the number of variables and variable categories that can be simultaneously used in categorical data analysis is small when models are estimated. In this article, an approach is proposed that can help remedy this problem. Specifically, it is proposed to perform, in a first step…
Xihao Li, Han Chen, Margaret Sunitha Selvaraj, Eric Van Buren + 64 more
Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait…
Suzanne Jak, Mike W.-L. Cheung, Selcuk Acar, Reuben Kindred
Meta-analytic confirmatory factor analysis (CFA) is a type of meta-analytic structural equation modeling (MASEM) that is useful for evaluating the factor structure of measurement scales based on data from multiple studies. Modeling the factor structure is just one example of the many potentially interesting research…
Authors not listed
While Raman spectroscopy offers notable experimental advantages as a probe of complex mixtures, its application in practice often confronts samples that present an overwhelming fluorescence background. Here, we explore the efficacy of two particular Raman spectrometric strategies for quantitative analysis under…
Ludwig A. Hothorn
In bio-medical studies, the p-values of the F-tests in ANOVA are usually interpreted independently as measures of the significance of the associated factors. This ’hidden multiplicity’ effect increases the false positive rate. Therefore, Cramer et al. (2016) proposed the Bonferroni adjustment of the p-values to control…
Authors not listed
Carotenoids are naturally occurring biomolecules with potent antioxidant activity that humans must obtain through their diet, as they cannot synthesize them endogenously. These pigments are naturally produced by plants and microbes, including red yeast cells that act as micro-factories, particularly genera such as…
Authors not listed
Decades of extensive research have proved that β-amyloid (Aβ) peptides and their aggregation, inducing oxidative stress in the brain, play a key role in Alzheimer’s disease (AD) development. Moreover, Aβ peptides bind to Cu(II) ions, and the resulting complexes accelerate the aggregation process while promoting the…
Authors not listed
Presence of gold nanoparticles in an aqueous dispersion perturbs water molecules in their vicinity. Such water molecules form what is known as hydration shell and possess different vibrational attributes than those in the bulk dispersion. Raman spectroscopy was utilised to study these hydration shell water molecules…
Christian Acal, Ana M. Aguilera
The methodological contribution in this paper is motivated by biomechanical studies where data characterizing human movement are waveform curves representing joint measures such as flexion angles, velocity, acceleration, and so on. In many cases the aim consists of detecting differences in gait patterns when several…
Authors not listed
MALDI MS analysis of liquid biopsy combined with ML enables non-invasive disease screening and monitoring. Presented an open-source R-based workflow covering all steps from raw data preprocessing to predictive model evaluation. The pipeline is customizable, transparent, and validated on clinical plasma samples from…
Christian Acal, Ana M. Aguilera
The methodological contribution in this paper is motivated by biomechanical studies where data characterizing human movement are waveform curves representing joint measures such as flexion angles, velocity, acceleration, and so on. In many cases the aim consists of detecting differences in gait patterns when several…
Authors not listed
We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling…
Authors not listed
Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
Andry Yasmid Mera-Mamián, José Moreno-Montoya, Laura Andrea Rodriguez-Villamizar, Diana Isabel Muñoz + 2 more
'Laura Andrea Rodriguez-Villamizar' 'Diana Isabel Muñoz' 'Ángela María Segura' 'Héctor Iván García'] Title: Resumen Este trabajo tiene como objetivo presentar una mirada global de la aplicabilidad de los modelos de análisis multinivel en el ámbito de la investigación sanitaria. Ofrece información sobre los fundamentos…
Zinan Lu, Jonathan Anns, Yishan Mai, Rou Zhang + 10 more
Data analysis in experimental science mainly relies on null-hypothesis significance testing, despite its well-known limitations. A powerful alternative is estimation statistics, which focuses on effect-size quantification. However, current estimation tools struggle with the complex, multi-group comparisons common in…