263 search results for "pca"

Interactive Heatmaps (and Dendrograms) – A Shiny App

July 7, 2013
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Interactive Heatmaps (and Dendrograms) – A Shiny App

Heatmaps are a great way to visualize data matrices. Heatmap color and organization can be used to  encode information about the data and metadata to help learn about the data at hand. An example of this could be looking at the raw data  or hierarchically clustering samples and variables based on their similarity or differences.

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Fun simulating Wimbledon in R and Python

July 4, 2013
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Fun simulating Wimbledon in R and Python

R and Python have different strengths. There's little you can do in R you absolutely can't do in Python and vice versa, but there's a lot of stuff that's really annoying in one and nice and simple in the other. I'm sure simulations can be run in R, but it seems frightfully tricky. Recently I wrote a simple Tennis simulator...

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Decluttering ordination plots part 3: ordipointlabel()

June 27, 2013
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Decluttering ordination plots part 3: ordipointlabel()

Previously in this series I looked at first the ordilabel() and then orditorp() functions in the vegan package as means to improve labelling in ordination plots. In this the third in the series I take a look at ordipointlabel().

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Using R: Two plots of principal component analysis

June 26, 2013
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Using R: Two plots of principal component analysis

PCA is a very common method for exploration and reduction of high-dimensional data. It works by making linear combinations of the variables that are orthogonal, and is thus a way to change basis to better see patterns in data. You either do spectral decomposition of the correlation matrix or singular value decomposition of the data

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Principal Components Analysis Shiny App

June 23, 2013
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Principal Components Analysis Shiny App

I’ve recently started experimenting with making Shiny apps, and today I wanted to make a basic app for calculating and visualizing principal components analysis (PCA). Here is the basic interface I came up with. Test drive the app for yourself using the code below or  check out the the R code HERE. Above is an example of the

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Prototyping A General Regression Neural Network with SAS

June 22, 2013
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Prototyping A General Regression Neural Network with SAS

Last time when I read the paper “A General Regression Neural Network” by Donald Specht, it was exactly 10 years ago when I was in the graduate school. After reading again this week, I decided to code it out with SAS macros and make this excellent idea available for the SAS community. The prototype of

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Sobol Sensitivity Analysis

June 10, 2013
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Sobol Sensitivity Analysis

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...

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Which Torontonians Want a Casino? Survey Analysis Part 2

May 17, 2013
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Which Torontonians Want a Casino?  Survey Analysis Part 2

In my last post I said that I would try to investigate the question of who actually does want a casino, and whether place of residence is a factor in where they want the casino to be built.  So, here … Continue reading →

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Eigen-analysis of Linear Model Behavior in R

May 7, 2013
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Eigen-analysis of Linear Model Behavior in R

This post is actually about replicating the figures in Otto and Day: A Biologist’s Guide to Mathematical Modeling in Ecology and Evolution. The figures I’m interested in for this post are Figures 9.1 and 9.2 in the chapter ‘General Solutions … Continue reading →

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Monitoring des médias 2

May 6, 2013
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Monitoring des médias 2

(This article was first published on Learning Data Science , and kindly contributed to R-bloggers) Petit monitoring de notre observatoire des médias sur Twitter.Chez Mediapart : Le Monde Le Figaro Le parisien Vue globaleLe code pour réaliser ce post : To leave a comment for the author, please follow the link and comment on his blog: Learning Data Science...

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