OPLSDA
Orthogonal partial least squares discriminant analysis (OPLSDA) was introduced as an improvement of the PLSDA approach to discriminate two or more groups (classes) using multivariate data. In OPLSDA, a regression model is constructed between the multivariate data and a response variable that only contains class information. The obvious advantage of OPLSDA compared with PLSDA is that a single component serves as a predictor for the class, while the other components describe the variation orthogonal to the first predictive component. Wiklund et al. used the terms between treatment variation to depict the average effect of treatment and within treatment variation to describe the systematic remainder variation, which is not related to the treatment. The treatment effect is supposed to be equal for all subjects although the magnitude is allowed to be different for each subject. Treatment effects that differ from the average treatment effect are referred to as within treatment variation. Now, bioinformaticians at Creative Proteomics are proud to tell you we are open to help you with OPLSDA Service!
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OPLS
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Anserj339
Entertainment
5 months ago
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multivariate analysis
Multivariate analysis (MVA) is founded on the statistical principle of multivariate statistics, which involves simultaneous observation and analysis of more than two statistical outcome variables at a time. Multivariate analysis can be complex by the desire to include physicsbased analysis to measure the effect of variables for a hierarchical "systemofsystems". Studies aimed at multivariate analysis are often stalled by the dimensionality of the problem. These concerns are frequently eased by using surrogate models, highly accurate approximations of the physicsbased code. Since surrogate models take the form of an equation, they can be estimated very quickly. This makes it possible for largescale MVA: while a Monte Carlo simulation across the design space is very difficult with physicsbased codes, it becomes worthless when evaluating surrogate models, which frequently take the form of responsesurface equations. Multivariate statistics has been a useful tool for the analysis of metabolomic data.
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Analysis , Multivariate
By:
Anserj339
Others/ Misc
6 months ago
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How To Test Your Landing Pages
A detailed Infographic on how to test landing pages, difference between A/B testing and Multivariate testing,important landing page element , landing page testing mistakes A/B testing or split testing is an experimental approach to compare two versions of a landing page (A and B), which are identical except for one variation that might impact a user’s behavior and to find out the most effective version. https://digitaldeeksha.com/
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DigitalDeeksha
Education
9 months ago
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Open Journal of Pediatrics & Neonatal Care
Aim: To describe time trends in complications and respiratory support in Norwegian preterm infants 20022010. To discuss strengths and limitations of using a national patient registry in epidemiological research.Methods: A total population study using data from The Norwegian national patient registry (NPR) 20022010. Temporal changes in Respiratory Distress Syndrome (RDS), Bronchopulmonary Dysplasia (BPD), Retinopathy of Prematurity (ROP), Intraventricular Hemorrhage (IVH), Necrotizing Enterocolitis (NEC), inhospital mortality and respiratory support were measured in multivariate logistic regressions using 2002 as reference year and adjusting for potential confounders
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Journals , Openaccessjournals , Pediatrics
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Scires
Education
13 months ago
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Gaussian Processes
In probability theory and statistics, a Gaussian process is a stochastic process (a collection of random variables indexed by time or space), such that every finite collection of those random variables has a multivariate normal distribution, i.e. every finite linear combination of them is normally distributed. The distribution of a Gaussian process is the joint distribution of all those (infinitely many) random variables, and as such, it is a distribution over functions with a continuous domain, e.g. time or space. A machinelearning algorithm that involves a Gaussian process uses lazy learning and a measure of the similarity between points (the kernel function) to predict the value for an unseen point from training data. The prediction is not just an estimate for that point, but also has uncertainty information—it is a onedimensional Gaussian distribution (which is the marginal distribution at that point).
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Artificial Intelligence , Gaussian processes , Machine learning , Prediction , Probability theory
By:
SciForce
Science & Technology
14 months ago
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NET SET RESEARCH methodology
Types of Research Application Exploratory Research Conclusive Research Correlation Research Explanatory (Causal / experimental) Research Comparison between exploratory, descriptive and causal Experimentation and Market TestingType of Information Sought Qualitative and Quantitative research Other types of Research Design Data: Primary and Secondary Data Data Collection and Method of study in research Content analysis Game or roleplaying Primary Market Research Method Quantitative Experiments Quasi Experiment and Field Trials Sociogram Variable and their Types Sampling Methods a. Probability Sampling . Simple Random Sampling . Systematic Sampling:. Stratified random sampling . Cluster Sampling b. Non Probability Sampling . Convenience sampling . Purposive /Judgment Sampling . Snowball Sample Types of Errors: MeasurementData Exploration Univariate vs. Bivariate Data Analysis of Variance (ANOVA) Problem Solving Central Tendency and Normal Distribution Normal Distribution Variance Effect Size Frequency distribution: Skewed, Mesokurtic, Leptokurtic, PlatykurticHypothesis Testing "True" Mean and Confidence Interval Margin of Error (Confidence Interval) Type I errors and type II errors OneTailed and TwoTailed Tests Parametric and Nonparametric Tests Bi and Multivariate Inferential Statistical
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Experiment , Methodology , NET , Research , Set
By:
amitkuls09
Education
31 months ago
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Multivariate Software Testing  Testing One Component at a Time
Multivariate software testing formalizes checks and tests on the individual page components rather than considering full pages. With multivariate software tests, the quality assurance team identifies different elements and components of the website page where the tests are potentially planned to run. Multiple versions of the page are then visualized by different variations & combinations of individual elements. Effectively, multivariate software testing focuses on identifying the winning combination from a set of all possible combinations.Read more at: http://softwaretestingsolution.com/blog/testingonecomponenttimemultivariatesoftwaretestin gsolutions/
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software testing solutions , Testing , Third party software testing compan
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softwaretestingsolut
Others/ Misc
33 months ago
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