Data Analysis Using the R Project for Statistical Computing - NERSC

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B2.Kernel‐basedes8ma8on• Kernel density estimators are less sensitive tothe placement of the bin edges;• Goal: retrieve a dense group of particles withsimilar spatial and momentum characteristics: argmax f(x,y,px), Neighborhood: 2 µmPackages:misc3d,rgl,fields26
B3.Iden8fybeamcandidates• Detec8onofcompactgroupsofpar8clesindependentofbeingamaximuminoneofthevariables;27
Page 1: DataAnalysisusingtheRProjectforSta8
Page 4 and 5: is a language and environment forst
Page 6 and 7: 2.Rinthescien8ficcommunity6
Page 8 and 9: 3.Extensible• Add‐onpackages:-
Page 10 and 11: 4.1.Mul8variateplotsEx: Explanatory
Page 12 and 13: 4.3.PackageRGLfor3Dvisualiza8on•
Page 14 and 15: BasicsandbeyondEXPLORATORYDATAANALY
Page 16 and 17: 2.Linearregression• Ex:Findtheequ
Page 18 and 19: 3.Clusteranalysis• Hierarchical-
Page 20 and 21: 3.2.K‐means• Splitnobserva8onsi
Page 22 and 23: http://www.lbl.gov/publicinfo/newsc
Page 24 and 25: Framework• Goal:automatetheanalys
Page 28 and 29: B4.Clusterusingmixturemodels• Mod
Page 30 and 31: Packages,challengesandnewbusinesses
Page 32 and 33: • Parallelism:2.WhatisgoingonHPCi
Page 34 and 35: TakehomeSource: http://www.nettakea
Page 36: Acknowledgementshttp://www.sciviews

Data Analysis Using the R Project for Statistical Computing - NERSC

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