1764 search results for "Regression"

When SAP HANA met R – What’s new?

February 18, 2013
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When SAP HANA met R – What’s new?

Since I wrote my blog When SAP HANA met R - First kiss I had received a lot of nice feedback...and one those feedbacks was..."What's new?"...Well...as you might now SAP HANA works with R by using Rserve, a package that allows communication to an R Serv...

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Predictors, responses and residuals: What really needs to be normally distributed?

February 18, 2013
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Predictors, responses and residuals: What really needs to be normally distributed?

Introduction Many scientists are concerned about normality or non-normality of variables in statistical analyses. The following and similar sentiments are often expressed, published or taught: "If you want to do statistics, then everything needs to be normally distributed." "We normalized…Read more →

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Automatic spatial interpolation with R: the automap package

February 17, 2013
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Automatic spatial interpolation with R: the automap package

In case of continuously collected data, e.g. observations from a monitoring network, spatial interpolation of this data cannot be done manually. Instead, the interpolation should be done automatically. To achieve this goal, I developed the automap package. automap builds on… See more ›

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Version 1.0 of multilevelPSA Available on CRAN

February 14, 2013
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Version 1.0 of multilevelPSA Available on CRAN

Version 1.0 of multilevelPSA has been released to CRAN. The multilevelPSA package provides functions to estimate and visualize propensity score models with multilevel, or clustered, data. The graphics are an extension of PSAgraphics package by Helmreich and Pruzek. The example below will investigate the differences between private and public school internationally using the Programme of International Student Assessment...

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Parallel execution of randomForestSRC

February 13, 2013
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Parallel execution of randomForestSRC

I guess I’m the resident expert on resampling methods at work. I’ve been using bagged predictors and random forests for a while, and have recently been using the randomForestSRC (RF-SRC) package in R (http://cran.r-project.org/web/packages/randomForestSRC). This package merges the two randomForest… Continue reading →

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Large claims, and ratemaking

February 13, 2013
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Large claims, and ratemaking

During the course, we have seen that it is natural to assume that not only the individual claims frequency can be explained by some covariates, but individual costs too. Of course, appropriate families should be considered to model the distribution of the cost , given some covariates .Here is the dataset we’ll use, > sinistre=read.table("http://freakonometrics.free.fr/sinistreACT2040.txt", + header=TRUE,sep=";") > sinistres=sinistre...

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Exposure with binomial responses

February 9, 2013
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Exposure with binomial responses

Last week, we’ve seen how to take into account the exposure to compute nonparametric estimators of several quantities (empirical means, and empirical variances) incorporating exposure. Let us see what can be done if we want to model a binomial response. The model here is the following: , the number of claims  on the period  is unobserved the number of...

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Happy Birthday Florence Henderson

February 9, 2013
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Happy Birthday Florence Henderson

As a celebration of Florence Henderson’s 79th birthday (on February 14), I have created this scatterplot to use in my regression course. The plot depicts the relationship between time spent on mathematics homework outside of school (expressed as z-scores) and … Continue reading →

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Quantifying the international search for meaning

February 9, 2013
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Quantifying the international search for meaning

Inspired by Preis et al.’s article Quantifying the advantage of looking forward, recently published in Scientific Reports (one of Nature publishing group’s journals), I wondered if similar big-data web-based research methods might address a question even bigger than how much different countries wonder about next year. How about the meaning of life. Who is searching

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Extracting the Epidemic Model: Going Beyond Florence Nightingale Part II

February 7, 2013
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Extracting the Epidemic Model: Going Beyond Florence Nightingale Part II

This is the second of a two part reexamination of Florence Nightingale's data visualization based on her innovative cam diagrams (my term) shown in Figure 1. Figure 1. Nightingale's original cam diagrams (click to enlarge)RecapIn Part I, I showed that FN applied sectoral areas, rather than a pie chart or...

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