269 search results for "pca"

Orthogonal Partial Least Squares (OPLS) in R

July 28, 2013
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Orthogonal Partial Least Squares (OPLS) in R

I often need to analyze and model very wide data (variables >>>samples), and because of this I gravitate to robust yet relatively simple methods. In my opinion partial least squares (PLS) is a particular useful algorithm. Simply put, PLS is an extension of principal components analysis (PCA), a non-supervised  method to maximizing  variance explained in X,

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Simulation of Blackjack: the odds are not with you

July 19, 2013
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Simulation of Blackjack: the odds are not with you

I often want to simulate outcomes varying across a set of parameters. In order to accomplish this in an efficient manner I have coded up a little function that takes parameter vectors and produces results. First I will show how to set it up with some d...

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Oracle R Connector for Hadoop 2.2.0 released

July 19, 2013
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Oracle R Connector for Hadoop 2.2.0 is now available for download. The Oracle R Connector for Hadoop 2.x series has introduced numerous enhancements, which are highlighted in this article and summarized as follows:  ORCH 2.0.0  ORCH 2.1.0  ORCH...

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Evaluating the Potential Incorporation of R into Research Methods Education in Psychology

July 17, 2013
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I was recently completing some professional development activities that required me to write a report on a self-chosen topic related to diversity in student backgrounds. I chose to use the opportunity to reflect on the potential for using R to teach psychology students research methods. I thought I'd share the report in case it interests anyone. Abstract...

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Analyse discriminante linéaire ou Regression logistique

July 10, 2013
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Analyse discriminante linéaire ou Regression logistique

Supposons que l'on dispose d'iris de Paris (en population >100khabts) et qu'on veuille pouvoir les classer selon leurs caractéristiques sociodémos : Population taux de chômage Etudiants CSP etc... Une fois, les iris classés, on se demande si l'on peut transporter cette typologie à une autre grande ville (Lyon) par exemple : Il faudrait alors pouvoir utiliser un modèle d'affectation des iris selon leurs caractéristiques respectives...

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Plotting principal component analysis with ggplot #rstats

July 8, 2013
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Plotting principal component analysis with ggplot #rstats

This script was almost written on parallel to the sjPlotCorr script because it uses a very similar ggplot-base. However, there’s also a very nice posting over at Martin’s Bio Blog which show alternative approaches on plotting PCAs. Anyway, if you … Weiterlesen →

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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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