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12 bhattacharyya.dist See Also plotcluster for straight forward discriminant plots. discrcoord for discriminant coordinates. rFace for generation of the example data used below. Examples set.seed(4634) face
hattacharyya.matrix 13 References Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition, 2nd edition, Academic Press, New York. Hennig, C. (2010) Methods for merging Gaussian mixture components, Advances in Data Analysis and Classification, 4, 3-34. Examples bhattacharyya.dist(c(1,1),c(2,5),diag(2),diag(2)) bhattacharyya.matrix Matrix of pairwise Bhattacharyya distances Description Computes Bhattachryya distances for pairs of components given the parameters of a Gaussian mixture. Usage bhattacharyya.matrix(muarray,Sigmaarray,ipairs="all", misclassification.bound=TRUE) Arguments muarray Sigmaarray matrix of component means (different components are in different columns). three dimensional array with component covariance matrices (the third dimension refers to components). ipairs "all" or list of vectors of two integers. If ipairs="all", computations are carried out for all pairs of components. Otherwise, ipairs gives the pairs of components for which computations are carried out. misclassification.bound logical. If TRUE, upper bounds for misclassification probabilities exp(-b) are given out instead of the original Bhattacharyya distances b. Value A matrix with Bhattacharyya distances (or derived misclassification bounds, see above) between pairs of Gaussian distributions with the provided parameters. If ipairs!="all", the Bhattacharyya distance is taken as 0 and the misclassification bound as 1. 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 and 8: ancoord 7 ancoord Asymmetric neighb
Page 9 and 10: awcoord 9 Arguments xd clvecd clnum
Page 11: atcoord 11 dom string. dom="mean" m
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
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kmeansCBI 69 Arguments x jitterv fa
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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
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mahalconf 81 mahalconf Mahalanobis
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mergenormals 83 numberstop renumber
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mergeparameters 85 Tantrum, J., Mur
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mixdens 87 Arguments n positive int
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mixpredictive 89 Details The predic
Page 91 and 92:
ncoord 91 See Also plotcluster for
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nselectboot 93 nselectboot Selectio
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pamk 95 Arguments data krange crite
Page 97 and 98:
piridge.zeroes 97 Author(s) Christi
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plotcluster 99 "arc" asymmetric dis
Page 101 and 102:
prediction.strength 101 Arguments x
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andconf 103 Value An n*cln-matrix.
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egmix 105 Details The result of the
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Face 107 Arguments n p nrep.top smi
Page 109 and 110:
idgeline.diagnosis 109 ridgeline.di
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solvecov 111 Arguments fpcobj an ob
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tdecomp 113 tdecomp Root of singula
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weightplots 115 Arguments y numeric
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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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