Problem Solving Tools Participant Guide
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Image Segmentation Based on Histogram Analysis Utilizing the Cloud Model
Computers and Mathematics with Applications 62 (2011) 2824–2833 Contents lists available at SciVerse ScienceDirect Computers and Mathematics with Applications journal homepage: www.elsevier.com/locate/camwa Image segmentation based on histogram analysis utilizing the cloud model Kun Qin a,∗, Kai Xu a, Feilong Liu b, Deyi Li c a School of Remote Sensing Information Engineering, Wuhan University, Wuhan, 430079, China b Bahee International, Pleasant Hill, CA 94523, USA c Beijing Institute of Electronic System Engineering, Beijing, 100039, China article info a b s t r a c t Keywords: Both the cloud model and type-2 fuzzy sets deal with the uncertainty of membership Image segmentation which traditional type-1 fuzzy sets do not consider. Type-2 fuzzy sets consider the Histogram analysis fuzziness of the membership degrees. The cloud model considers fuzziness, randomness, Cloud model Type-2 fuzzy sets and the association between them. Based on the cloud model, the paper proposes an Probability to possibility transformations image segmentation approach which considers the fuzziness and randomness in histogram analysis. For the proposed method, first, the image histogram is generated. Second, the histogram is transformed into discrete concepts expressed by cloud models. Finally, the image is segmented into corresponding regions based on these cloud models. Segmentation experiments by images with bimodal and multimodal histograms are used to compare the proposed method with some related segmentation methods, including Otsu threshold, type-2 fuzzy threshold, fuzzy C-means clustering, and Gaussian mixture models. The comparison experiments validate the proposed method. ' 2011 Elsevier Ltd. All rights reserved. 1. Introduction In order to deal with the uncertainty of image segmentation, fuzzy sets were introduced into the field of image segmentation, and some methods were proposed in the literature. -
Propensity Scores up to Head(Propensitymodel) – Modify the Number of Threads! • Inspect the PS Distribution Plot • Inspect the PS Model
Overview of the CohortMethod package Martijn Schuemie CohortMethod is part of the OHDSI Methods Library Cohort Method Self-Controlled Case Series Self-Controlled Cohort IC Temporal Pattern Disc. Case-control New-user cohort studies using Self-Controlled Case Series A self-controlled cohort A self-controlled design, but Case-control studies, large-scale regressions for analysis using fews or many design, where times preceding using temporals patterns matching controlss on age, propensity and outcome predictors, includes splines for exposure is used as control. around other exposures and gender, provider, and visit models age and seasonality. outcomes to correct for time- date. Allows nesting of the Estimation methods varying confounding. study in another cohort. Patient Level Prediction Feature Extraction Build and evaluate predictive Automatically extract large models for users- specified sets of featuress for user- outcomes, using a wide array specified cohorts using data in of machine learning the CDM. Prediction methods algorithms. Empirical Calibration Method Evaluation Use negative control Use real data and established exposure-outcomes pairs to reference sets ass well as profile and calibrate a simulations injected in real particular analysis design. data to evaluate the performance of methods. Method characterization Database Connector Sql Render Cyclops Ohdsi R Tools Connect directly to a wide Generate SQL on the fly for Highly efficient Support tools that didn’t fit range of databases platforms, the various SQLs dialects. implementations of regularized other categories,s including including SQL Server, Oracle, logistic, Poisson and Cox tools for maintaining R and PostgreSQL. regression. libraries. Supporting packages Under construction Technologies CohortMethod uses • DatabaseConnector and SqlRender to interact with the CDM data – SQL Server – Oracle – PostgreSQL – Amazon RedShift – Microsoft APS • ff to work with large data objects • Cyclops for large scale regularized regression Graham study steps 1. -
Generalized Scatter Plots
Generalized scatter plots Daniel A. Keim a Abstract Scatter Plots are one of the most powerful and most widely used b techniques for visual data exploration. A well-known problem is that scatter Ming C. Hao plots often have a high degree of overlap, which may occlude a significant Umeshwar Dayal b portion of the data values shown. In this paper, we propose the general a ized scatter plot technique, which allows an overlap-free representation of Halldor Janetzko and large data sets to fit entirely into the display. The basic idea is to allow the Peter Baka ,* analyst to optimize the degree of overlap and distortion to generate the best possible view. To allow an effective usage, we provide the capability to zoom ' University of Konstanz, Universitaetsstr. 10, smoothly between the traditional and our generalized scatter plots. We iden Konstanz, Germany. tify an optimization function that takes overlap and distortion of the visualiza bHewlett Packard Research Labs, 1501 Page tion into acccount. We evaluate the generalized scatter plots according to this Mill Road, Palo Alto, CA94304, USA. optimization function, and show that there usually exists an optimal compro mise between overlap and distortion . Our generalized scatter plots have been ' Corresponding author. applied successfully to a number of real-world IT services applications, such as server performance monitoring, telephone service usage analysis and financial data, demonstrating the benefits of the generalized scatter plots over tradi tional ones. Keywords: scatter plot; overlapping; distortion; interpolation; smoothing; interactions Introduction Motivation Large amounts of multi-dimensional data occur in many important appli cation domains such as telephone service usage analysis, sales and server performance monitoring. -
Propensity Score Matching with R: Conventional Methods and New Features
812 Review Article Page 1 of 39 Propensity score matching with R: conventional methods and new features Qin-Yu Zhao1#, Jing-Chao Luo2#, Ying Su2, Yi-Jie Zhang2, Guo-Wei Tu2, Zhe Luo3 1College of Engineering and Computer Science, Australian National University, Canberra, ACT, Australia; 2Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China; 3Department of Critical Care Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, China Contributions: (I) Conception and design: QY Zhao, JC Luo; (II) Administrative support: GW Tu; (III) Provision of study materials or patients: GW Tu, Z Luo; (IV) Collection and assembly of data: QY Zhao; (V) Data analysis and interpretation: QY Zhao; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Guo-Wei Tu. Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China. Email: [email protected]; Zhe Luo. Department of Critical Care Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen 361015, China. Email: [email protected]. Abstract: It is increasingly important to accurately and comprehensively estimate the effects of particular clinical treatments. Although randomization is the current gold standard, randomized controlled trials (RCTs) are often limited in practice due to ethical and cost issues. Observational studies have also attracted a great deal of attention as, quite often, large historical datasets are available for these kinds of studies. However, observational studies also have their drawbacks, mainly including the systematic differences in baseline covariates, which relate to outcomes between treatment and control groups that can potentially bias results. -
Permutation Tests
Permutation tests Ken Rice Thomas Lumley UW Biostatistics Seattle, June 2008 Overview • Permutation tests • A mean • Smallest p-value across multiple models • Cautionary notes Testing In testing a null hypothesis we need a test statistic that will have different values under the null hypothesis and the alternatives we care about (eg a relative risk of diabetes) We then need to compute the sampling distribution of the test statistic when the null hypothesis is true. For some test statistics and some null hypotheses this can be done analytically. The p- value for the is the probability that the test statistic would be at least as extreme as we observed, if the null hypothesis is true. A permutation test gives a simple way to compute the sampling distribution for any test statistic, under the strong null hypothesis that a set of genetic variants has absolutely no effect on the outcome. Permutations To estimate the sampling distribution of the test statistic we need many samples generated under the strong null hypothesis. If the null hypothesis is true, changing the exposure would have no effect on the outcome. By randomly shuffling the exposures we can make up as many data sets as we like. If the null hypothesis is true the shuffled data sets should look like the real data, otherwise they should look different from the real data. The ranking of the real test statistic among the shuffled test statistics gives a p-value Example: null is true Data Shuffling outcomes Shuffling outcomes (ordered) gender outcome gender outcome gender outcome Example: null is false Data Shuffling outcomes Shuffling outcomes (ordered) gender outcome gender outcome gender outcome Means Our first example is a difference in mean outcome in a dominant model for a single SNP ## make up some `true' data carrier<-rep(c(0,1), c(100,200)) null.y<-rnorm(300) alt.y<-rnorm(300, mean=carrier/2) In this case we know from theory the distribution of a difference in means and we could just do a t-test. -
Scatter Plots with Error Bars
NCSS Statistical Software NCSS.com Chapter 165 Scatter Plots with Error Bars Introduction The Scatter Plots with Error Bars procedure extends the capability of the basic scatter plot by allowing you to plot the variability in Y and X corresponding to each point. Each point on the plot represents the mean or median of one or more values for Y and X within a subgroup. Error bars can be drawn in the Y or X direction, representing the variability associated with each Y and X center point value. The error bars may represent the standard deviation (SD) of the data, the standard error of the mean (SE), a confidence interval, the data range, or percentiles. This plot also gives you the capability of graphing the raw data along with the center and error-bar lines. This procedure makes use of all of the additional enhancement features available in the basic scatter plot, including trend lines (least squares), confidence limits, polynomials, splines, loess curves, and border plots. The following graphs are examples of scatter plots with error bars that you can create with this procedure. This chapter contains information only about options that are specific to the Scatter Plots with Error Bars procedure. For information about the other graphical components and scatter-plot specific options available in this procedure (e.g. regression lines, border plots, etc.), see the chapter on Scatter Plots. 165-1 © NCSS, LLC. All Rights Reserved. NCSS Statistical Software NCSS.com Scatter Plots with Error Bars Data Structure This procedure accepts data in four different input formats. The type of plot that can be created depends on the input format. -
Estimation of Average Total Effects in Quasi-Experimental Designs: Nonlinear Constraints in Structural Equation Models
Estimation of Average Total Effects in Quasi-Experimental Designs: Nonlinear Constraints in Structural Equation Models Dissertation zur Erlangung des akademischen Grades doctor philosophiae (Dr. phil.) vorgelegt dem Rat der Fakultät für Sozial- und Verhaltenswissenschaften der Friedrich-Schiller-Universität Jena von Dipl.-Psych. Joachim Ulf Kröhne geboren am 02. Juni 1977 in Jena Gutachter: 1. Prof. Dr. Rolf Steyer (Friedrich-Schiller-Universität Jena) 2. PD Dr. Matthias Reitzle (Friedrich-Schiller-Universität Jena) Tag des Kolloquiums: 23. August 2010 Dedicated to Cora and our family(ies) Zusammenfassung Diese Arbeit untersucht die Schätzung durchschnittlicher totaler Effekte zum Vergleich der Wirksam- keit von Behandlungen basierend auf quasi-experimentellen Designs. Dazu wird eine generalisierte Ko- varianzanalyse zur Ermittlung kausaler Effekte auf Basis einer flexiblen Parametrisierung der Kovariaten- Treatment Regression betrachtet. Ausgangspunkt für die Entwicklung der generalisierten Kovarianzanalyse bildet die allgemeine Theo- rie kausaler Effekte (Steyer, Partchev, Kröhne, Nagengast, & Fiege, in Druck). In dieser allgemeinen Theorie werden verschiedene kausale Effekte definiert und notwendige Annahmen zu ihrer Identifikation in nicht- randomisierten, quasi-experimentellen Designs eingeführt. Anhand eines empirischen Beispiels wird die generalisierte Kovarianzanalyse zu alternativen Adjustierungsverfahren in Beziehung gesetzt und insbeson- dere mit den Propensity Score basierten Analysetechniken verglichen. Es wird dargestellt, -
Algebra 1 Regular/Honors – Phase 2 Part 1 TEXTBOOK CHECKOUT
Algebra 1 Phase 2 – Part 1 Directions: In the pack you will find completed notes with blank TRY IT! and BEAT THE TEST! questions. Read, study, and work through these problems. Focus on trying to do the TRY IT! and BEAT THE TEST! problem to assess your understanding before checking the answers provided at PHASE 2 INSTRUCTIONAL CONTINUITY PLAN the end of the packet. Also included are Independent Practice questions. Complete these and return to your BEGINNING APRIL 16, 2020 teacher/school at a later date. Later, you may be expected to complete the TEST YOURSELF! problems online. (Topic Number 1H after Topic 9 is HONORS ONLY) Topic Date Topic Name Number Completed 9-1 Dot Plots Algebra 1 Regular/Honors – Phase 2 Part 1 9-2 Histograms 9-3 Box Plots – Part 1 TEXTBOOK CHECKOUT: No Textbook Needed 9-4 Box Plots – Part 2 Measures of Center and 9-5 Shapes of Distributions 9-6 Measures of Spread – Part 1 SCHOOL NAME: _____________________ 9-7 Measures of Spread – Part 2 STUDENT NAME: _____________________ 9-8 The Empirical Rule 9-9 Outliers in Data Sets TEACHER NAME: _____________________ 9-1H Normal Distribution 1 What are some things you learned from these sections? Topic Date Topic Name Number Completed Relationship between Two Categorical Variables – Marginal 10-1 and Joint Relative Frequency – Part 1 Relationship between Two Categorical Variables – Marginal 10-2 and Joint Relative Frequency – Part 2 Relationship between Two 10-3 Categorical Variables – Conditional What questions do you still have after studying these Relative Frequency sections? 10-4 Scatter Plot and Function Models 10-5 Residuals and Residual Plots – Part 1 10-6 Residuals and Residual Plots – Part 2 10-7 Examining Correlation 2 Section 9: One-Variable Statistics Section 9 – Topic 1 Dot Plots Statistics is the science of collecting, organizing, and analyzing data. -
Shinyitemanalysis for Psychometric Training and to Enforce Routine Analysis of Educational Tests
ShinyItemAnalysis for Psychometric Training and to Enforce Routine Analysis of Educational Tests Patrícia Martinková Dept. of Statistical Modelling, Institute of Computer Science, Czech Academy of Sciences College of Education, Charles University in Prague R meetup Warsaw, May 24, 2018 R meetup Warsaw, 2018 1/35 Introduction ShinyItemAnalysis Teaching psychometrics Routine analysis of tests Discussion Announcement 1: Save the date for Psychoco 2019! International Workshop on Psychometric Computing Psychoco 2019 February 21 - 22, 2019 Charles University & Czech Academy of Sciences, Prague www.psychoco.org Since 2008, the international Psychoco workshops aim at bringing together researchers working on modern techniques for the analysis of data from psychology and the social sciences (especially in R). Patrícia Martinková ShinyItemAnalysis for Psychometric Training and Test Validation R meetup Warsaw, 2018 2/35 Introduction ShinyItemAnalysis Teaching psychometrics Routine analysis of tests Discussion Announcement 2: Job offers Job offers at Institute of Computer Science: CAS-ICS Postdoctoral position (deadline: August 30) ICS Doctoral position (deadline: June 30) ICS Fellowship for junior researchers (deadline: June 30) ... further possibilities to participate on grants E-mail at [email protected] if interested in position in the area of Computational psychometrics Interdisciplinary statistics Other related disciplines Patrícia Martinková ShinyItemAnalysis for Psychometric Training and Test Validation R meetup Warsaw, 2018 3/35 Outline 1. Introduction 2. ShinyItemAnalysis 3. Teaching psychometrics 4. Routine analysis of tests 5. Discussion Introduction ShinyItemAnalysis Teaching psychometrics Routine analysis of tests Discussion Motivation To teach psychometric concepts and methods Graduate courses "IRT models", "Selected topics in psychometrics" Workshops for admission test developers Active learning approach w/ hands-on examples To enforce routine analyses of educational tests Admission tests to Czech Universities Physiology concept inventories .. -
Lecture 8 Sample Mean and Variance, Histogram, Empirical Distribution Function Sample Mean and Variance
Lecture 8 Sample Mean and Variance, Histogram, Empirical Distribution Function Sample Mean and Variance Consider a random sample , where is the = , , … sample size (number of elements in ). Then, the sample mean is given by 1 = . Sample Mean and Variance The sample mean is an unbiased estimator of the expected value of a random variable the sample is generated from: . The sample mean is the sample statistic, and is itself a random variable. Sample Mean and Variance In many practical situations, the true variance of a population is not known a priori and must be computed somehow. When dealing with extremely large populations, it is not possible to count every object in the population, so the computation must be performed on a sample of the population. Sample Mean and Variance Sample variance can also be applied to the estimation of the variance of a continuous distribution from a sample of that distribution. Sample Mean and Variance Consider a random sample of size . Then, = , , … we can define the sample variance as 1 = − , where is the sample mean. Sample Mean and Variance However, gives an estimate of the population variance that is biased by a factor of . For this reason, is referred to as the biased sample variance . Correcting for this bias yields the unbiased sample variance : 1 = = − . − 1 − 1 Sample Mean and Variance While is an unbiased estimator for the variance, is still a biased estimator for the standard deviation, though markedly less biased than the uncorrected sample standard deviation . This estimator is commonly used and generally known simply as the "sample standard deviation". -
Exploring Spatial Data with Geoda: a Workbook
Exploring Spatial Data with GeoDaTM : A Workbook Luc Anselin Spatial Analysis Laboratory Department of Geography University of Illinois, Urbana-Champaign Urbana, IL 61801 http://sal.uiuc.edu/ Center for Spatially Integrated Social Science http://www.csiss.org/ Revised Version, March 6, 2005 Copyright c 2004-2005 Luc Anselin, All Rights Reserved Contents Preface xvi 1 Getting Started with GeoDa 1 1.1 Objectives . 1 1.2 Starting a Project . 1 1.3 User Interface . 3 1.4 Practice . 5 2 Creating a Choropleth Map 6 2.1 Objectives . 6 2.2 Quantile Map . 6 2.3 Selecting and Linking Observations in the Map . 10 2.4 Practice . 11 3 Basic Table Operations 13 3.1 Objectives . 13 3.2 Navigating the Data Table . 13 3.3 Table Sorting and Selecting . 14 3.3.1 Queries . 16 3.4 Table Calculations . 17 3.5 Practice . 20 4 Creating a Point Shape File 22 4.1 Objectives . 22 4.2 Point Input File Format . 22 4.3 Converting Text Input to a Point Shape File . 24 4.4 Practice . 25 i 5 Creating a Polygon Shape File 26 5.1 Objectives . 26 5.2 Boundary File Input Format . 26 5.3 Creating a Polygon Shape File for the Base Map . 28 5.4 Joining a Data Table to the Base Map . 29 5.5 Creating a Regular Grid Polygon Shape File . 31 5.6 Practice . 34 6 Spatial Data Manipulation 36 6.1 Objectives . 36 6.2 Creating a Point Shape File Containing Centroid Coordinates 37 6.2.1 Adding Centroid Coordinates to the Data Table . -
HW: Scatter Plots
HW: Scatter Plots Name: Date: 1. The scatter plot below shows the average traffic 2. Ms. Ochoa recorded the age and shoe size of each volume and average vehicle speed on a certain student in her physical education class. The graph freeway for 50 days in 1999. below shows her data. Which of the following statements about the graph Which statement best describes the relationship is true? between average traffic volume and average vehicle speed shown on the scatter plot? A. The graph shows a positive trend. A. As traffic volume increases, vehicle speed B. The graph shows a negative trend. increases. C. The graph shows a constant trend. B. As traffic volume increases, vehicle speed decreases. D. The graph shows no trend. C. As traffic volume increases, vehicle speed increases at first, then decreases. D. As traffic volume increases, vehicle speed decreases at first, then increases. page 1 3. Which graph best shows a positive correlation 4. Use the scatter plot below to answer the following between the number of hours studied and the test question. scores? A. B. The police department tracked the number of ticket writers and number of tickets issued for the past 8 weeks. The scatter plot shows the results. Based on the scatter plot, which statement is true? A. More ticket writers results in fewer tickets issued. B. There were 50 tickets issued every week. C. When there are 10 ticket writers, there will be 800 tickets issued. C. D. More ticket writers results in more tickets issued. D. page 2 HW: Scatter Plots 5.