Abstract
Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.
Highlights
Regression analyss are among statistical methods which are frequently employed in analyzing quantitative data, especially to model dependences between response and several explanatory variables
In this paper we report the development Web-based-GUI interface that unifies most statistical models for independent responses using R and enriched by various options for data exploration, graphical visualisation and goodness of fit measures utilizing several selected R packages
2 Methods We develop an interface for unified online statistical models for independent responses, which covers linear models (LM), robust linear models approaches (RLM), Generalized linear model (GLM), generalized to accommodate additive predictors (GAM), GAMLSS, Bootsrap and Markov Chained Monte Carlo (MCMC) regression based on various previously mentioned R- packages
Summary
Regression analyss (statistical models) are among statistical methods which are frequently employed in analyzing quantitative data, especially to model dependences between response and several explanatory variables. Statistical models have been developed into various directions to handle various type and complex relationship of data. Rich variety of advanced and recent statistical modelings are mostly available on open source software (one of them is R). These advanced statistical models, are mostly based on programming script or command line interface, which mean, that they are not accessed by applied or practical researchers. It is essential to build interface to make advanced and most recent statistical methods, especially statistical model on R, becoming more user friendly and easier to access and to use.
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