1572 search results for "regression"

Monotonic deshrinking in weighted averaging models

January 5, 2013
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Monotonic deshrinking in weighted averaging models

Weighted averaging regression and calibration is the most widely used method for developing a palaeolimnological transfer function. Such models are used to reconstruct properties of the past lake environment such as pH, total phosphorus, and water temperature with, it has … Continue reading →

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Monotonic deshrinking in weighted averaging models

January 5, 2013
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Monotonic deshrinking in weighted averaging models

Weighted averaging regression and calibration is the most widely used method for developing a palaeolimnological transfer function. Such models are used to reconstruct properties of the past lake environment such as pH, total phosphorus, and water temperature with, it has to be said, varying degrees of success and usefulness. In simple weighted averaging (WA) there is little to specify other...

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A new version of analogue for a new year

January 4, 2013
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A new version of analogue for a new year

Yesterday I rolled up a new version (0.10-0) of analogue, my R package for analysing palaeoecological data. It is now available from CRAN. There were lots of incremental changes to Stratiplot() to improve the quality of the stratigraphic diagrams produced … Continue reading →

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A new version of analogue for a new year

January 4, 2013
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Yesterday I rolled up a new version (0.10-0) of analogue, my R package for analysing palaeoecological data. It is now available from CRAN. There were lots of incremental changes to Stratiplot() to improve the quality of the stratigraphic diagrams produced and fix several annoying bugs. Also the definition of the standard error of MAT reconstructions was fixed; it...

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100 most read R posts in 2012 (stats from R-bloggers) – big data, visualization, data manipulation, and other languages

January 2, 2013
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100 most read R posts in 2012 (stats from R-bloggers) – big data, visualization, data manipulation, and other languages

R-bloggers.com is now three years young. The site is an (unofficial) online journal of the R statistical programming environment, written by bloggers who agreed to contribute their R articles to the site. Last year, I posted on the top 24...

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You can’t spell loss reserving without R

January 2, 2013
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You can’t spell loss reserving without R

Last year, I spent a morning trying to return to first principles when modeling loss reserves. (Brief aside to non-actuaries: a loss reserve is the financial provision set aside to pay for claims which have either not yet settled, or have not yet been reported. If that doesn’t sound fascinating, this will likely be a

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R code and data for book “R and Data Mining: Examples and Case Studies”

January 2, 2013
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R code and data for book “R and Data Mining: Examples and Case Studies”

R code and data for book “R and Data Mining: Examples and Case Studies” are now available at http://www.rdatamining.com/books/rdm/code. An online PDF version of the book (the first 11  chapters only) can also be downloaded at http://www.rdatamining.com/docs. Below are its … Continue reading →

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Software engineer’s guide to getting started with data science

December 30, 2012
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Software engineer’s guide to getting started with data science

Many of my software engineer friends ask me about learning data science. There are many articles on this subject from renowned data scientists (Dataspora, Gigaom, Quora, Hilary Mason). This post captures my journey (a software engin...

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Pearson’s r: Not a good measure of electoral persistence

December 30, 2012
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Pearson’s r: Not a good measure of electoral persistence

Pearson’s product-moment correlation, \(r\), is an incredibly useful tool for getting some idea about how two variables are (linearly) related. But there are times when using Pearson’s \(r\) is not appropriate and, even if linearity and all other assumptions hold, … Continue reading →

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My Intro to Multiple Classification with Random Forests, Conditional Inference Trees, and Linear Discriminant Analysis

December 27, 2012
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My Intro to Multiple Classification with Random Forests, Conditional Inference Trees, and Linear Discriminant Analysis

After the work I did for my last post, I wanted to practice doing multiple classification.  I first thought of using the famous iris dataset, but felt that was a little boring.  Ideally, I wanted to look for a practice … Continue reading →

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