A bayesian nonparametric estimator based on left censored by Walker S., Muliere P.

By Walker S., Muliere P.

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M. Similarly the mean column profile has ith coordinate xi . 2 Motivation Correspondence analysis is not unlike principal components analysis in its underlying geometrical bases. While principal components analysis is particularly suitable for quantitative data, correspondence analysis is appropriate © 2005 by Taylor & Francis Group, LLC 42 Theory for the following types of input data: frequencies, contingency tables, probabilities, categorical data, and mixed qualitative/categorical data. , the ijth table entry indicates the frequency of occurrence of attribute j for object i) the row and column “profiles” are of interest.

Variances) is employed. These methods are relatively powerful. They allow us to answer questions related to internal associations and correlations in our data. ” They provide visualizations to help us with communication of our conclusions to our clients or colleagues. They are tools (algorithmic, software) which are easy to use, and which let the data speak for themselves. 5 Correspondence Analysis of Globular Clusters Logarithms have been applied to the data (in the fourth variable, as given, and in the first variable by us).

With factors 1,2,... from cols. 2,3,... cproj <- sweep(sweep(temp,1,sqrt(fJ),FUN="/"), 2, sqrt(sres$values),FUN="/") # CONTRIBUTIONS TO FACTORS BY ROWS AND COLUMNS # Contributions: mass times projection distance squared. temp <- sweep( rproj^2, 1, fI, FUN="*") # Normalize such that sum of contributions for a factor = 1. sumCtrF <- apply(temp, 2, sum) # NOTE: Obs. x factors. # Read cntrs. with factors 1,2,... from cols. 2,3,... rcntr <- sweep(temp, 2, sumCtrF, FUN="/") temp <- sweep( cproj^2, 1, fJ, FUN="*") sumCtrF <- apply(temp, 2, sum) # NOTE: Vbs.

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