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8 awcoord Author(s) Christian Hennig http://www.homepages.ucl.ac.uk/~ucakche/ References Hennig, C. (2004) Asymmetric linear dimension reduction for classification. Journal of Computational and Graphical Statistics 13, 930-945 . Hennig, C. (2005) A method for visual cluster validation. In: Weihs, C. and Gaul, W. (eds.): Classification - The Ubiquitous Challenge. Springer, Heidelberg 2005, 153-160. See Also plotcluster for straight forward discriminant plots. discrproj for alternatives. rFace for generation of the example data used below. Examples set.seed(4634) face
awcoord 9 Arguments xd clvecd clnum mahal method the data matrix; a numerical object which can be coerced to a matrix. integer vector of class numbers; length must equal nrow(xd). integer. Number of the homogeneous class. "md" or "square". If "md", the points are weighted by the square root of the alpha-quantile of the corresponding chi squared distribution over the roots of their Mahalanobis distance to the homogeneous class, unless this is smaller than 1. If "square" (which is recommended), the (originally squared) Mahalanobis distance and the unrooted quantile is used. one of "mve", "mcd" or "classical". Covariance matrix used within the homogeneous class and for the computation of the Mahalanobis distances. "mcd" and "mve" are robust covariance matrices as implemented in cov.rob. "classical" refers to the classical covariance matrix. clweight logical. If FALSE, only the points of the heterogeneous class are weighted. This, together with method="classical", computes AWC as defined in Hennig (2003). If TRUE, all points are weighted. This, together with method="mcd", computes ARC as defined in Hennig (2003). alpha subsample countmode ... no effect numeric between 0 and 1. The corresponding quantile of the chi squared distribution is used for the downweighting of points. Points with a smaller Mahalanobis distance to the homogeneous class get full weight. integer. If 0, all points are used. Else, only a subsample of subsample of the points is used. optional positive integer. Every countmode algorithm runs awcoord shows a message. Details The square root of the homogeneous classes covariance matrix is inverted by use of tdecomp, which can be expected to give reasonable results for singular within-class covariance matrices. Value List with the following components ev units proj eigenvalues in descending order. columns are coordinates of projection basis vectors. New points x can be projected onto the projection basis vectors by x %*% units projections of xd onto units. Author(s) Christian Hennig http://www.homepages.ucl.ac.uk/~ucakche/
Page 1 and 2: Title Flexible procedures for clust
Page 3 and 4: fpc-package 3 mergenormals . . . .
Page 5 and 6: adcoord 5 Author(s) Christian Henni
Page 7: ancoord 7 ancoord Asymmetric neighb
Page 11 and 12: atcoord 11 dom string. dom="mean" m
Page 13 and 14: hattacharyya.matrix 13 References F
Page 15 and 16: can 15 See Also cluster.stats Examp
Page 17 and 18: classifdist 17 Examples set.seed(77
Page 19 and 20: clucols 19 clucols Sets of colours
Page 21 and 22: cluster.stats 21 Arguments n p cn i
Page 23 and 24: cluster.stats 23 Value cluster.stat
Page 25 and 26: cluster.varstats 25 See Also silhou
Page 27 and 28: clusterboot 27 fcc
Page 29 and 30: clusterboot 29 subtuning clustermet
Page 31 and 32: clusterboot 31 Value dbscanCBI an i
Page 33 and 34: cmahal 33 Arguments n p nmin cmin n
Page 35 and 36: confusion 35 # The same should be p
Page 37 and 38: cweight 37 Examples x
Page 39 and 40: dbscan 39 Details Value Clusters re
Page 41 and 42: diptest.multi 41 References J. A. H
Page 43 and 44: discrete.recode 43 Author(s) Christ
Page 45 and 46: discrproj 45 Usage discrproj(x, clv
Page 47 and 48: distancefactor 47 Arguments cat n c
Page 49 and 50: dridgeline 49 References Halkidi, M
Page 51 and 52: extract.mixturepars 51 Author(s) Ch
Page 53 and 54: fixmahal 53 ## S3 method for class
Page 55 and 56: fixmahal 55 Details Value A (crisp)
Page 57 and 58: fixmahal 57 calpha pointit subset m
Page 59 and 60:
fixreg 59 Usage linear regression r
Page 61 and 62:
fixreg 61 pch col gv Details Value
Page 63 and 64:
fixreg 63 sim coef var g coll vecto
Page 65 and 66:
flexmixedruns 65 Details Value disc
Page 67 and 68:
fpclusters 67 fpclusters Extracting
Page 69 and 70:
kmeansCBI 69 Arguments x jitterv fa
Page 71 and 72:
kmeansCBI 71 k scaling runs criteri
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kmeansCBI 73 Value claraCBI an inte
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kmeansruns 75 Value iter.max runs s
Page 77 and 78:
localshape 77 Details Value The dat
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mahalanofix 79 Arguments x2 mg covg
Page 81 and 82:
mahalconf 81 mahalconf Mahalanobis
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mergenormals 83 numberstop renumber
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mergeparameters 85 Tantrum, J., Mur
Page 87 and 88:
mixdens 87 Arguments n positive int
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mixpredictive 89 Details The predic
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ncoord 91 See Also plotcluster for
Page 93 and 94:
nselectboot 93 nselectboot Selectio
Page 95 and 96:
pamk 95 Arguments data krange crite
Page 97 and 98:
piridge.zeroes 97 Author(s) Christi
Page 99 and 100:
plotcluster 99 "arc" asymmetric dis
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prediction.strength 101 Arguments x
Page 103 and 104:
andconf 103 Value An n*cln-matrix.
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egmix 105 Details The result of the
Page 107 and 108:
Face 107 Arguments n p nrep.top smi
Page 109 and 110:
idgeline.diagnosis 109 ridgeline.di
Page 111 and 112:
solvecov 111 Arguments fpcobj an ob
Page 113 and 114:
tdecomp 113 tdecomp Root of singula
Page 115 and 116:
weightplots 115 Arguments y numeric
Page 117 and 118:
zmisclassification.matrix 117 Argum
Page 119 and 120:
Index ∗Topic arith can, 15 cweigh
Page 121 and 122:
INDEX 121 dip.test, 40, 41, 83 dipp
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