The Development of Web-based Graphical User Interface for Learning and Fitting Generalized Estimating Equation with Spline Smoothers

In repeated measures data, the observations tend to be correlated within each subject, and such data are often analyzed using Generalized Estimating Equations (GEE), which are robust to assumptions that many methods hold. The main limitation of GEE is that its method of estimation is quasi-likelihood. The recent framework of the copula is very popular for handling repeated data. The maximum likelihood-based analysis for repeated data can be obtained using Gaussian copula regression. The purpose of this study is to show the handling and analysis of the repeated data using the Gaussian copula regression approach and compare the findings with GEE. The prospective, double-blinded, randomized controlled trial data for this study was obtained from the Department of Anesthesia, Christian Medical College, and Vellore. ASA I and II patients were randomized into three groups. Hemodynamic parameters were obtained for 88 patients at thirteen-time points. The outcome of interest was mean arterial pressure. Both GEE and Gaussian copula regression were compared assuming four different correlation structures. The optimal correlation structures were selected with the Akaike Information Criterion (AIC) and Correlation Information Criterion (CIC) goodness of fit criteria according to the method of estimation of Gaussian copula regression and GEE, respectively. The correlation structures, unstructured and autoregressive, were found to be optimal for Gaussian copula regression and GEE based on AIC and CIC criteria values respectively. A comparison between the estimated values of the selected models showed no major differences. Gaussian copula regression found that intrathecal morphine has a significant reduction in MAP over time, this significance is considered important as the study uses randomized controlled trial data. Both methods have almost similar results, but Gaussian copula regression provides better results by identifying significant findings associated with the outcome using maximum likelihood estimation that GEE fails to identify using quasi-likelihood estimation.

Diagnostic analysis is a fundamental part of regression modeling.With it, it is possible to identify unexpected behaviors in the data and checking the adequacy of the proposed model.This dissertation aims to extend some diagnostic techniques to models based on generalized estimating equations (GEE).Generalized estimating equations are similar to traditional generalized linear models, but they include a working matrix that represents the correlation structure between observations within the same experimental unit.This approach is commonly applied in longitudinal studies, repeated measures data, and clustered data.We start this work with a review on GEE from estimating equations and then we present a conection with the glmtoolbox library that provides a variety of GEE ts as well as goodness-of-t measures, diagnostic methods, model selection and hypothesis testing.The bootstrap resampling method is extended to GEE and applied to estimate the sample distributions of the coecients of the tted models, as well the dispersion parameter and the correlation parameters 1 , . . ., s .Simulation studies are also conducted to create envelope plots based on marginal quantile residuals under the assumption of known marginal distributions, in order to assess the goodness of t.In this work, we also applied the forward search technique to detect observations with masked inuence, to perform global and local inuence analysis, and to assess residuals using the Mahalanobis distance.Finally, two real datasets are analyzed using the procedures presented in this work.

This paper presents the development of Graphical User Interface (GUI) for determining energy saving under Option A i.e. retrofit isolation of the International Performance Measurement and Verification (IPMVP) protocol. The GUI is developed using Microsoft Visual Basic 2010. In Option A of retrofit isolation, savings are determined by partial field measurement of the energy used to which an Energy Conservative Measure (ECM) was installed separate from the energy use of the rest of the facility. The GUI consists of three most applied Option A ECM projects, i.e. 1) lighting efficiency improvement, 2) lighting system retrofitting with dimmer and 3) chiller efficiency improvement. This Option A Visual basic-based GUI could guide and help Energy Service Company (ESCO) and facility owner to calculate energy saving that involves retrofit isolation energy efficiency projects adhere to IPMVP protocol.

Discounting is the process by which outcomes lose value. Much of discounting research has focused on differences in the degree of discounting across various groups. This research has relied heavily on conventional null hypothesis significance tests that are familiar to psychologists, such as t-tests and ANOVAs. As discounting research questions have become more complex by simultaneously focusing on within-subject and between-group differences, conventional statistical testing is often not appropriate for the obtained data. Generalized estimating equations (GEE) are one type of mixed-effects model that are designed to handle autocorrelated data, such as within-subject repeated-measures data, and are therefore more appropriate for discounting data. To determine if GEE provides similar results as conventional statistical tests, we compared the techniques across 2,000 simulated data sets. The data sets were created using a Monte Carlo method based on an existing data set. Across the simulated data sets, the GEE and the conventional statistical tests generally provided similar patterns of results. As the GEE and more conventional statistical tests provide the same pattern of result, we suggest researchers use the GEE because it was designed to handle data that has the structure that is typical of discounting data.

Outcomes research in bipolar disorders, because of complex clinical variation over-time, offers demanding research design and statistical challenges. Longitudinal studies involving relatively large samples, with outcome measures obtained repeatedly over-time, are required. In this report, statistical methods appropriate for such research are reviewed. Analytic methods appropriate for repeated measures data include: (i) endpoint analysis; (ii) endpoint analysis with last observation carried forward; (iii) summary statistic methods yielding one summary measure per subject; (iv) random effects and generalized estimating equation (GEE) regression modeling methods; and (v) time-to-event survival analyses. Use and limitations of these several methods are illustrated within a randomly selected (33%) subset of data obtained in two recently completed randomized, double blind studies on acute mania. Outcome measures obtained repeatedly over 3 or 4 weeks of blinded treatment in active drug and placebo sub-groups included change-from-baseline Young Mania Rating Scale (YMRS) scores (continuous measure) and achievement of a clinical response criterion (50% YMRS reduction). Four of the methods reviewed are especially suitable for use with these repeated measures data: (i) the summary statistic method; (ii) random/mixed effects modeling; (iii) GEE regression modeling; and (iv) survival analysis. Outcome studies in bipolar illness ideally should be longitudinal in orientation, obtain outcomes data frequently over extended times, and employ large study samples. Missing data problems can be expected, and data analytic methods must accommodate missingness.

In schizophrenia research, little attention yet has been directed on methods for analyzing data from studies with repeated measurements over time. Motivation for this research stems from a project within the German Research Network on Schizophrenia, in which an algorithm is developed to guide prodrome-based early intervention strategies in stable first episode patients. We present two different approaches for the analysis of correlated response data, the Generalized Estimating Equations (GEE) method and the Artificial Neural Network (ANN) approach. We illustrate the methods using the data of the A.N.I. study, which is one of the largest German multicenter treatment studies in regard to the long-term treatment of schizophrenia conducted between 1983 and 1989. The results of statistical model selection prior to GEE analysis and various data presentation methods for ANNs are presented. The primary goal of our evaluation is to investigate if the defined prodromes are valid predictors for relapse. Additionally, it is shown that both methods are applicable on a realistic data set. It is concluded that both methods are suitable for predictor analysis especially since all variable time points of the patients are included instead of only selected, so that it can be assumed that results are not biased. With the GEE method a test of association for each predictor can be performed whereas with ANNs a general proposition can be made for prodromes depending on the type of data presentation. Using the A.N.I. data the prodrome 'trouble sleeping' seems to be the most informative predictor. Finally, the important differences of the two methods are discussed.

While univariate instances of binomial data are readily handled with generalized linear models, cases of multivariate or repeated measure binomial data are complicated by the possibility of correlated responses. Likelihood-based estimation can be applied by using mixture distribution models, though this approach can present computational challenges. The logistic transformation can be used to bypass these concerns and allow for alternative estimating procedures. One popular alternative is the generalized estimating equation (GEE) method, though systematic errors can lead to infeasible correlation estimates or nonconvergence problems. Our approach is the coupling of quasileast squares (QLSs) method with a rarely used matrix factorization, which achieves a simplified estimation platform—as compared to the mixture model approach—and does not suffer from the convergence problems in GEE method. A noncontrived example is provided that shows the mechanical breakdown of GEE using several statistical software packages and highlights the usefulness of the QLS approach.

This paper presents the development of graphical user interface (GUI) for Humanoid Head Robot Amir-II. The GUI serves as a monitoring tool for the robot operation which includes the visual input from a webcam as its eye and the positioning of the Dynamixel AX-12 robot actuators. Matlab Graphical User Interface Development Environment (Matlab GUIDE) is used in developing the GUI which enables Matlab codes on face tracking, facial expression recognition and servo callbacks to be integrated within the GUI. The humanoid head is programmed to detect a human face within its view and automatically follow the tracked face through the movement of its neck joints. There is also a dedicated control panel within the GUI to capture the facial expression screenshots and save them into the database with their related expression labels.

Application development of a large number of graphical user interface (GUI) screens and reports requires a labor‐intensive process. In the typical scenario, a GUI developer drags each component, such as the textfield, label, and error messages, on each of the new GUI screens or reports. They then set the component properties and perhaps write any validation routines for input data. Using the proposed Observation Panel Bean (OPB), the developer drags only one component per field, and the OPB dynamically builds the GUI components with consistent properties and links the observation field to the needed validation routine. With little development effort these fields are simply selected from the data dictionary and placed on the GUI. The proposed OPB uses Fowler's observation pattern as a high‐level abstraction representing attributes observed about information in the application domain. The panel collaborates with the observation pattern to set the label, error message, and properties of the various components. Yoder's validator pattern collaborates with the observation pattern and the data dictionary to validate the observations. The proposed OPB decreases GUI development time, adds adaptability to GUI design, and increases consistency in GUI component presentation. Copyright © 2002 John Wiley & Sons, Ltd.

Effective learning of complex dynamic systems, their analysis and synthesis are unthinkable without use of simulation tools. For repetitive, comfortable and friendly use it is advantageous to use graphic user interface (GUI) enabling easy evaluation of system behavior: visualization of phenomena, model performance, changes of parameters, input and output signals. Development of GUI is enough bothersome and there were searched ways to satisfy requirements to overcome these difficulties. MATLAB presents GUI a comfortable tool for development of virtual models. In the paper, we share methodology, procedures and our experiences at design and utilization of virtual models for dynamic systems developed in the MATLAB GUI environment.

An important component of Early Warning Systems (EWS) for man-made and natural hazards is the command and control unit’s Graphical User Interface (GUI). All relevant information of an EWS is concentrated in this GUI and offered to human operators. However, when designing the GUI, not only the user experience and the GUI’s screens are relevant, but also the frameworks and technologies that the GUI is built on and the implementation of the GUI itself are of great importance. Implementations differ based on their applications in different domains but the design and approaches to implement the GUIs of different EWS often show analogies. The design and development of such GUIs are performed repeatedly on some parts of the system for each EWS. Thus, the generic GUI framework of a geospatial EWS for tsunamis is introduced to enable possible synergistic effects on the development of other new related technology. The results presented here could be adopted and reused in other EWS for man-made and natural hazards.

BackgroundEven though R is one of the most commonly used statistical computing environments, it lacks a graphical user interface (GUI) that appeals to students, researchers, lecturers, and practitioners in medicine and pharmacy for conducting standard data analytics. Current GUIs built on top of R, such as EZR and R-Commander, aim to facilitate R coding and visualization, but most of the functionalities are still accessed through a command-line interface (CLI). To assist practitioners of medicine and pharmacy and researchers to run most routines in fundamental statistical analysis, we developed an interactive GUI; i.e., MEPHAS, to support various web-based systems that are accessible from laptops, workstations, or tablets, under Windows, macOS (and IOS), or Linux. In addition to fundamental statistical analysis, advanced statistics such as the extended Cox regression and dimensional analyses including partial least squares regression (PLS-R) and sparse partial least squares regression (SPLS-R), are also available in MEPHAS.ResultsMEPHAS is a web-based GUI (https://alain003.phs.osaka-u.ac.jp/mephas/) that is based on a shiny framework. We also created the corresponding R package mephas (https://mephas.github.io/). Thus far, MEPHAS has supported four categories of statistics, including probability, hypothesis testing, regression models, and dimensional analyses. Instructions and help menus were accessible during the entire analytical process via the web-based GUI, particularly advanced dimensional data analysis that required much explanation. The GUI was designed to be intuitive for non-technical users to perform various statistical functions, e.g., managing data, customizing plots, setting parameters, and monitoring real-time results, without any R coding from users. All generated graphs can be saved to local machines, and tables can be downloaded as CSV files.ConclusionMEPHAS is a free and open-source web-interactive GUI that was designed to support statistical data analyses and prediction for medical and pharmaceutical practitioners and researchers. It enables various medical and pharmaceutical statistical analyses through interactive parameter settings and dynamic visualization of the results.

Much recent work in the application of knowledge-based programming techniques to statistical problems has involved the development of the statistical expert systems which guide a user through a correct statistical analysis or assists the user in choosing an appropriate statistical tool. A more basic need for users of statistical software is support (guidance and training) in the use of statistical software from a programming standpoint versus an analysis standpoint. This research investigates the development of knowledge-based systems to help support the use of statistical software. Two systems are developed, EG Expert and EG Network. The design and development of each is described long with issues pertaining to computer implementation using Prolog.

Command Line Interface (CLI) is the most popular and basic user interface to interact with computers. Despite its simplicity, it has some limitations in terms of user experience. For example, sometimes it is hard to understand and interpret the textual output of the command. In this paper we describe the limitations of command line interfaces and propose Gracoli1: A graphical command line interface that takes advantage of both text-based interface and graphical user interface to provide better user experience and perform complex tasks. We demonstrate some of the useful applications and features of Gracoli. Sometime such a hybrid system provides and combines the strengths of CLI and GUI to perform specific tasks. We explore some useful applications of Gracoli to create a new kind of user experience. Command line interface makes accessibility faster and Graphical User-Interface makes output more interactive and understandable.

The contemporary theory of metaphor defines the metaphor as a major and indispensable part of our ordinary, conventional way of conceptualizing the world and it asserts that the metaphors are used to define our relationship with the physical environment and to create a context of communication. The metaphors are the core idioms of the contemporary graphical user interface (GUI) and have an important role in helping users interact with computer systems. The desktop metaphor began with the first GUI Xerox Star and was popularized by the Lisa of Apple Macintosh. Realizing the physical-office metaphor and reflecting the familiar world of the potential users by using icons of file, folder and trashcan, the metaphor-based GUIs made it relatively easy for all to use the computer productively. Nowadays almost all of the software industry is based on the desktop metaphor paradigm. However, besides the researchers that favor the use of metaphors in the development of the system image by accentuating their assistance to learning, there are also other researchers, who reject the metaphor-based design approach in GUIs by referring to their limitations. Considering these discussions and various approaches, this study aims to make an outline of the historical development of metaphor-based GUIs; to determine the current state of metaphor within GUI design by brief examples and finally to identify future prospects and research themes. The study claims that metaphor still remains to be an inevitable part of the GUI to communicate it with the users and despite its potential limitations -rather than rejecting the metaphor totally- it seems better-to try compatible and supplementary solutions like visual formalism.