2043 search results for "Regression"

Online course on forecasting using R

September 10, 2013
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I am teaming up with Revolution Analytics to teach an online course on forecasting with R. Topics to be covered include seasonality and trends, exponential smoothing, ARIMA modelling, dynamic regression and state space models, as well as forecast accuracy methods and forecast evaluation techniques such as cross-validation. I will talk about some of my consulting experiences, and explain the...

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SPSS looked great! 20 years ago…

September 4, 2013
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SPSS looked great! 20 years ago…

For some reason someone dropped a pamphlet advertising SPSS for Windows 3.0 in my mail box at work. This means that the pamphlet, and the advertised version of SPSS, should be at least 20 years old! These days I’m happily using R for everything but if I was going to estimate any models 20 years ago SPSS actually looked...

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Showing results from Cox Proportional Hazard Models in R with simPH

September 2, 2013
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Showing results from Cox Proportional Hazard Models in R with simPH

Effectively showing estimates and uncertainty from Cox Proportional Hazard (PH) models, especially for interactive and non-linear effects, can be challenging with currently available software. So, researchers often just simply display a results table. These are pretty useless for Cox PH models. It is difficult to decipher a simple linear variable’s estimated effect and basically impossible to understand time...

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Latent Variable Analysis with R: Getting Setup with lavaan

September 1, 2013
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Latent Variable Analysis with R: Getting Setup with lavaan

Getting Started with Structural Equation Modeling Part 1Getting Started with Structural Equation Modeling: Part 1 Introduction For the analyst familiar with linear regression fitting structural equation models can at first feel strange. In the R environment, fitting structural equation models involves learning new modeling syntax, new plotting...

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Visualising Shrinkage

August 31, 2013
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Visualising Shrinkage

A useful property of mixed effects and Bayesian hierarchical models is that lower level estimates are shrunk towards the more stable estimates further up the hierarchy. To use a time honoured example you might be modelling the effect of a new teaching method on performance at the classroom level. Classes of 30 or so students … Continue reading...

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The joy and martyrdom of trying to be a Bayesian

August 30, 2013
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Some of my fellow scientists have it easy. They use predefined methods like linear regression and ANOVA to test simple hypotheses; they live in the innocent world of bivariate plots and lm(). Sometimes they notice that the data have odd histograms and they use glm(). The more educated ones use … Continue reading →

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New Video: Credit Scoring & R: Reject inference, nested conditional models, & joint scores

August 29, 2013
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This post shares the video from the talk presented in August 2013 by Ross Gayler on Credit Scoring and R at Melbourne R Users. Credit scoring tends to involve the balancing of mutually contradictory objectives spiced with a liberal dash … Continue reading →

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In-Hadoop R-based Analytics coming to Cloudera

August 27, 2013
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Revolution Analytics has teamed up with Cloudera to bring the scalable data manipulation and statistical modeling algorithms of Revolution R Enteprise to the massively-parallel computing environments of CDH3 and CDH4 Hadoop clusters. As ZDNet reports: Specifically, the upcoming version 7.0 of the Revolution R Enterpise distribution and its ScaleR algorithms will run inside CDH3 and CDH4, eliminating the need...

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Using a GBM for Classification in R

August 26, 2013
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I discuss some advantages of Generalized Boosted Models over logistic regression and discriminant analysis and demonstrate how to use a GBM for binary classification (predicting whether an event occurs or not). Using a GBM for Classification in R from...

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predictNLS (Part 2, Taylor approximation): confidence intervals for ‘nls’ models

August 26, 2013
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predictNLS (Part 2, Taylor approximation): confidence intervals for ‘nls’ models

Initial Remark: Reload this page if formulas don’t display well! As promised, here is the second part on how to obtain confidence intervals for fitted values obtained from nonlinear regression via nls or nlsLM (package ‘minpack.lm’). I covered a Monte Carlo approach in http://rmazing.wordpress.com/2013/08/14/predictnls-part-1-monte-carlo-simulation-confidence-intervals-for-nls-models/, but here we will take a different approach: First- and second-order

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