# 352 search results for "PCA"

## Computing and visualizing LDA in R

January 15, 2014
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$Computing and visualizing LDA in R$

As I have described before, Linear Discriminant Analysis (LDA) can be seen from two different angles. The first classify a given sample of predictors to the class with highest posterior probability . It minimizes the total probability of misclassification. To compute it uses Bayes’ rule and assume that follows a Gaussian distribution with class-specific mean

## Statistical Interests in Large Cities

January 10, 2014
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I always thought that there were some kind of schools in statistics, areas (not to say universities or laboratories) where people had common interest in term of statistical methodology. Like people with strong interest in extreme values, or in Lévy Processes. I wanted to check this point so I did extract information about articles puslished in about 35 journals...

## Summarising multivariate palaeoenvironmental data

January 9, 2014
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The horseshoe effect is a well known and discussed issue with principal component analysis (PCA) (e.g. Goodall 1954; Swan 1970; Noy-Meir & Austin 1970). Similar geometric artefacts also affect correspondence analysis (CA). In part 1 of this series I looked at the implications of these “artefacts” for the recovery of temporal or single dominant gradients from multivariate palaeoecological data....

## Decluttering ordination plots part 4: orditkplot()

December 31, 2013
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Earlier in this series I looked at the ordilabel() and then the orditorp() functions, and most recently the ordipointlabel() function in the vegan package as means to improve labelling in ordination plots. In this, the fourth and final post in the series I take a look at orditkplot(). If you’ve created ordination diagrams before or...

## Summarising multivariate palaeoenvironmental data

December 28, 2013
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Ordination methods that yield orthogonal axes of variation are often used to summarise the multivariate data obtained from sediment cores. Usually the first or, less often, the first few ordination axes are taken as directions of change or the main patterns of variance in the multivariate data. There is an oft-overlooked issue with this approach that has the potential...

## Conditional dependence measures

December 17, 2013
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$(Y_1,Y_2)$

This week, I spend some time at the Workshop on Nonparametric Curve Smoothing conference at Concordia. Yesterday afternoon, Noël Veraverbeke show an interesting graph, to illustrate conditional copulas (and the derivation of conditional dependence measures, such as Kendall’s tau, or Spearman’s rho). A long time ago, in my PhD thesis (mainly on conditional copulas) I did try to derive conditional...

## The Complexities of Customer Segmentation: Removing Response Intensity to Reveal Response Pattern

December 15, 2013
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At the end of the last post, the reader was left assuming respondent homogeneity without any means for discovering if all of our customers adopted the same feature prioritization. To review, nine features were presented one at a time, and each time res...

## New version of analogue on CRAN

December 14, 2013
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It has been almost a year since the last release of the analogue package. At lot has happened in the intervening period and although I’ve been busy with a new job in a new country and coding on several other R packages, activity on analogue has also progressed a pace. As the version 0.12-0 of the package hits a...

## A note on the co-moments in the IFACD model

December 11, 2013
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The Independent Factor Autoregressive Conditional Density (IFACD) model of Ghalanos, Rossi and Urga (2014) uniquely, in its class of parametric models, generates time varying higher co-moment forecasts, as a consequence of the ACD specification of the conditional density of the standardized innovations. In this short note I discuss in more detail the properties of the

## A note on the co-moments in the IFACD model

December 11, 2013
By

The Independent Factor Autoregressive Conditional Density (IFACD) model of Ghalanos, Rossi and Urga (2014) uniquely, in its class of parametric models, generates time varying higher co-moment forecasts, as a consequence of the ACD specification of the conditional density of the standardized innovations. In this short note I discuss in more detail the properties of the