# 2161 search results for "regression"

## Top 100 R packages for 2013 (Jan-May)!

June 13, 2013
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(This article was first published on R-statistics blog » RR-statistics blog, and kindly contributed to R-bloggers) What are the top 100 (most downloaded) R packages in 2013? Thanks to the recent release of RStudio of their “0-cloud” CRAN log files (but without including downloads from the primary CRAN mirror or any of the 88 other CRAN mirrors), we can now answer this question...

## Data imputation I

June 12, 2013
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I recently entered kaggle titanic learning competition for fun and to see where my out of the box utilization of random forest would rank me (303 out of 5,882). It was interesting to see that much of the scoring differentiation came from score imputation, that is filling missing values based on other data. For example, we might have

## Using Quandl in R

June 12, 2013
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Image by Jan Zander Our mantra here at Quandl is making data easy to find and easy to use. Following that goal we (and subsequently the community) have created packages that integrate Quandl’s API into a number of software platforms. Today we’ll take a look at R. R is a free statistical computing language created

## Sobol Sensitivity Analysis

June 10, 2013
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Sensitivity analysis is the task of evaluating the sensitivity of a model output Y to input variables (X1,…,Xp). Quite often, it is assumed that this output is related to the input through a known function f :Y= f(X1,…,Xp). Sobol indices are generalizing the coefficient of the coefficient of determination in regression. The ith first order indice is the proportion of...

## At what sample size do correlations stabilize?

June 6, 2013
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Maybe you have encountered this situation: you run a large-scale study over the internet, and out of curiosity, you frequently check the correlation between two variables. My experience with this practice is usually frustrating, as in small sample sizes (and we will see what “small” means in this context) correlations go up and down, change sign,

## The Frisch–Waugh–Lovell Theorem for Both OLS and 2SLS

June 5, 2013
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The Frisch–Waugh–Lovell (FWL) theorem is of great practical importance for econometrics. FWL establishes that it is possible to re-specify a linear regression model in terms of orthogonal complements. In other words, it permits econometricians to partial out right-hand-side, or control, variables. This is useful in a variety of settings. For example, there may be cases

## Veterinary Epidemiologic Research: Modelling Survival Data – Semi-Parametric Analyses

June 4, 2013
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Next on modelling survival data from Veterinary Epidemiologic Research: semi-parametric analyses. With non-parametric analyses, we could only evaluate the effect one or a small number of variables. To evaluate multiple explanatory variables, we analyze data with a proportional hazards model, the Cox regression. The functional form of the baseline hazard is not specified, which make

## Understanding the value of Predictive Analytics on Web Data

June 3, 2013
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In this blogpost, I will be talking briefly about Predictive Analytics and why it holds value from a web analytics perspective. Broadly speaking, Predictive Analytics is a set of methodologies that assist us in anticipating customer behavior. The customer behavior of interest could be anything ranging from spend, buying habits, page views, response to a

June 1, 2013
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In my previous post (http://statcompute.wordpress.com/2013/05/25/test-drive-of-parallel-computing-with-r) on 05/25/2013, I’ve demonstrated the power of parallel computing with various R packages. However, in the real world, it is not straight-forward to utilize these powerful tools in our day-by-day computing tasks without carefully formulate the problem. In the example below, I am going to show how to use the

## ”How to draw the line” with ggplot2

May 30, 2013
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In a recent tutorial in the eLife journal, Huang, Rattner, Liu & Nathans suggested that researchers who draw scatterplots should start providing not one but three regression lines. I quote, Plotting both regression lines gives a fuller picture of the data, and comparing their slopes provides a simple graphical assessment of the correlation coefficient. Plotting