Component Density for a Parameterized MVN Mixture Model
cdensE.Rd
Computes component densities for points in a parameterized MVN mixture model.
Usage
cdensE(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensV(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensX(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensEII(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensVII(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensEEI(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensVEI(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensEVI(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensVVI(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensEEE(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensEEV(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensVEV(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensVVV(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensEVE(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensEVV(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensVEE(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensVVE(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensXII(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensXXI(data, logarithm = FALSE, parameters, warn = NULL, ...)
cdensXXX(data, logarithm = FALSE, parameters, warn = NULL, ...)
Arguments
- data
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
- logarithm
A logical value indicating whether or not the logarithm of the component densities should be returned. The default is to return the component densities, obtained from the log component densities by exponentiation.
- parameters
The parameters of the model:
mean
The mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model.
variance
A list of variance parameters for the model. The components of this list depend on the model specification. See the help file for
mclustVariance
for details.pro
Mixing proportions for the components of the mixture. If the model includes a Poisson term for noise, there should be one more mixing proportion than the number of Gaussian components.
- warn
A logical value indicating whether or not a warning should be issued when computations fail. The default is
warn=FALSE
.- ...
Catches unused arguments in indirect or list calls via
do.call
.
Value
A numeric matrix whose [i,j]
th
entry is the density of observation i in component j.
The densities are not scaled by mixing proportions.
Note
When one or more component densities are very large in magnitude, then it may be possible to compute the logarithm of the component densities but not the component densities themselves due to overflow.
Examples
# \donttest{
z2 <- unmap(hclass(hcVVV(faithful),2)) # initial value for 2 class case
model <- meVVV(data=faithful, z=z2)
cdensVVV(data=faithful, logarithm = TRUE, parameters = model$parameters)
#> [,1] [,2]
#> 1 -4.197962 -23.327906
#> 2 -23.342333 -2.639804
#> 3 -5.369492 -16.425216
#> 4 -15.294924 -3.232768
#> 5 -3.060038 -50.710265
#> 6 -13.290054 -7.761979
#> 7 -3.699704 -57.918268
#> 8 -5.334659 -26.752622
#> 9 -22.962876 -2.412796
#> 10 -3.034001 -44.933104
#> 11 -22.934312 -2.528941
#> 12 -3.676302 -32.771717
#> 13 -2.724501 -37.562422
#> 14 -27.479203 -3.332165
#> 15 -3.170498 -55.618395
#> 16 -20.214896 -2.518439
#> 17 -21.915710 -4.216529
#> 18 -3.455892 -59.675676
#> 19 -26.760842 -3.595670
#> 20 -2.679311 -39.367492
#> 21 -24.676501 -2.687279
#> 22 -27.479203 -3.332165
#> 23 -4.858440 -20.080789
#> 24 -7.357413 -10.919858
#> 25 -3.707075 -47.408698
#> 26 -4.826034 -25.481836
#> 27 -20.944700 -2.269342
#> 28 -2.921123 -33.631614
#> 29 -3.238694 -28.304652
#> 30 -2.776193 -45.080634
#> 31 -3.466375 -39.469218
#> 32 -3.010859 -45.669805
#> 33 -6.449804 -15.147156
#> 34 -2.893828 -33.676595
#> 35 -3.472704 -26.585813
#> 36 -21.731435 -2.312102
#> 37 -25.554020 -2.900109
#> 38 -3.677658 -59.639204
#> 39 -21.329762 -3.062301
#> 40 -4.247178 -61.991292
#> 41 -2.677669 -42.761619
#> 42 -20.946033 -2.714943
#> 43 -2.993015 -51.386831
#> 44 -22.685815 -3.270025
#> 45 -3.960485 -47.302375
#> 46 -6.598464 -20.802812
#> 47 -6.211837 -25.551593
#> 48 -20.374670 -2.305713
#> 49 -3.012673 -52.862464
#> 50 -19.230203 -2.593682
#> 51 -4.415900 -57.463454
#> 52 -4.155687 -59.620152
#> 53 -22.934312 -2.528941
#> 54 -3.677658 -59.639204
#> 55 -24.193790 -2.917515
#> 56 -3.702828 -62.476248
#> 57 -4.178361 -23.158393
#> 58 -22.961895 -5.472180
#> 59 -3.220395 -49.108148
#> 60 -2.680246 -42.118651
#> 61 -16.621517 -2.671928
#> 62 -2.929064 -49.161225
#> 63 -26.881283 -3.164999
#> 64 -3.442207 -58.957948
#> 65 -21.320242 -3.337036
#> 66 -4.816504 -50.784915
#> 67 -2.736993 -36.607377
#> 68 -3.452079 -54.126106
#> 69 -17.334988 -3.949120
#> 70 -4.544447 -53.441400
#> 71 -3.052678 -34.577311
#> 72 -20.567346 -2.316439
#> 73 -2.869289 -47.303898
#> 74 -3.793379 -30.306906
#> 75 -18.780967 -3.237428
#> 76 -5.551980 -68.561758
#> 77 -18.770649 -2.736682
#> 78 -3.090617 -49.336764
#> 79 -3.201066 -28.392013
#> 80 -4.826034 -25.481836
#> 81 -3.011010 -34.787362
#> 82 -2.723047 -43.012954
#> 83 -4.062219 -33.036185
#> 84 -11.298804 -5.524240
#> 85 -3.345808 -32.488922
#> 86 -4.208793 -66.504258
#> 87 -3.076628 -30.077285
#> 88 -2.841868 -48.182626
#> 89 -22.581164 -3.203542
#> 90 -3.857276 -35.951643
#> 91 -16.678900 -2.737894
#> 92 -4.225630 -47.419501
#> 93 -24.413541 -2.615064
#> 94 -3.870686 -58.569039
#> 95 -20.629889 -4.070450
#> 96 -3.710463 -39.347779
#> 97 -3.145918 -54.839806
#> 98 -3.587831 -24.925274
#> 99 -23.891950 -2.520996
#> 100 -3.791269 -62.817662
#> 101 -13.314929 -4.015144
#> 102 -3.620549 -47.127765
#> 103 -22.563187 -2.807059
#> 104 -2.852246 -48.725153
#> 105 -2.924853 -34.554491
#> 106 -26.172906 -3.091087
#> 107 -3.211476 -56.013763
#> 108 -24.406371 -2.696018
#> 109 -3.729394 -62.392134
#> 110 -4.061563 -25.984956
#> 111 -4.136478 -54.869882
#> 112 -15.940531 -2.852310
#> 113 -4.281280 -65.752555
#> 114 -2.758530 -44.560136
#> 115 -23.139134 -3.748921
#> 116 -3.024552 -52.518067
#> 117 -20.025202 -3.417245
#> 118 -3.127196 -52.956969
#> 119 -21.540990 -3.130198
#> 120 -3.362336 -48.054637
#> 121 -16.003038 -5.076151
#> 122 -4.349739 -32.007303
#> 123 -2.792823 -38.748925
#> 124 -20.567346 -2.316439
#> 125 -3.595774 -54.494859
#> 126 -3.719606 -27.759651
#> 127 -26.996373 -3.552868
#> 128 -2.807859 -48.321385
#> 129 -17.801948 -2.616670
#> 130 -4.095762 -57.353351
#> 131 -27.507973 -3.569953
#> 132 -2.932976 -38.642050
#> 133 -13.219165 -6.598774
#> 134 -3.924297 -46.755621
#> 135 -27.188030 -3.355339
#> 136 -2.728314 -44.556354
#> 137 -23.709163 -2.491718
#> 138 -3.983323 -65.582239
#> 139 -21.075819 -2.254025
#> 140 -3.650663 -26.069747
#> 141 -2.704316 -39.618171
#> 142 -16.326223 -2.794102
#> 143 -2.847325 -49.422198
#> 144 -4.045263 -58.391303
#> 145 -2.958232 -41.064783
#> 146 -19.435158 -2.621356
#> 147 -3.068862 -52.205973
#> 148 -23.376850 -2.685890
#> 149 -6.763224 -77.891170
#> 150 -23.754624 -2.623325
#> 151 -5.104238 -67.202584
#> 152 -2.943287 -31.692611
#> 153 -13.516733 -4.266377
#> 154 -2.956467 -51.358696
#> 155 -4.605864 -19.879599
#> 156 -4.048571 -30.189017
#> 157 -2.795904 -47.949919
#> 158 -6.048704 -43.036756
#> 159 -23.754624 -2.623325
#> 160 -4.868659 -37.175298
#> 161 -24.424929 -4.196397
#> 162 -3.473474 -39.792856
#> 163 -19.516176 -2.456391
#> 164 -3.285133 -27.903995
#> 165 -5.999115 -17.754059
#> 166 -3.425381 -49.479805
#> 167 -14.207790 -3.676640
#> 168 -4.424334 -69.131072
#> 169 -22.648320 -2.350947
#> 170 -5.022950 -58.257673
#> 171 -24.420125 -2.683646
#> 172 -18.892222 -2.315269
#> 173 -3.260305 -49.672861
#> 174 -6.086198 -14.806481
#> 175 -2.757288 -37.731271
#> 176 -2.681165 -42.607500
#> 177 -3.867798 -46.141116
#> 178 -19.239233 -4.027522
#> 179 -3.625995 -35.349204
#> 180 -3.163973 -35.561099
#> 181 -21.935642 -2.425455
#> 182 -3.260305 -49.672861
#> 183 -2.841467 -40.992268
#> 184 -4.038653 -28.833247
#> 185 -22.081202 -2.412819
#> 186 -2.849526 -44.792442
#> 187 -3.225392 -36.894643
#> 188 -27.188030 -3.355339
#> 189 -2.800981 -46.048960
#> 190 -18.619497 -2.376900
#> 191 -3.484011 -58.647539
#> 192 -21.874287 -2.752047
#> 193 -4.179506 -57.580043
#> 194 -3.189917 -37.341307
#> 195 -2.996970 -30.808798
#> 196 -2.704316 -39.618171
#> 197 -6.608032 -26.436920
#> 198 -2.869070 -42.356538
#> 199 -20.000070 -2.924230
#> 200 -3.351040 -52.911852
#> 201 -17.793378 -2.672037
#> 202 -2.722907 -43.533303
#> 203 -5.032900 -42.701164
#> 204 -22.946061 -2.429771
#> 205 -3.168950 -50.499176
#> 206 -27.737489 -3.448581
#> 207 -2.870129 -42.388558
#> 208 -3.912003 -31.230266
#> 209 -24.248577 -2.673448
#> 210 -2.852246 -48.725153
#> 211 -13.414135 -6.405328
#> 212 -3.242152 -54.626111
#> 213 -24.967565 -2.741435
#> 214 -3.377164 -26.866904
#> 215 -6.924280 -15.976850
#> 216 -2.891784 -37.973694
#> 217 -17.613974 -3.401969
#> 218 -5.470209 -65.202518
#> 219 -20.568685 -2.238339
#> 220 -2.888887 -35.527396
#> 221 -24.413541 -2.615064
#> 222 -2.742433 -41.035802
#> 223 -23.974824 -2.840374
#> 224 -3.366504 -45.814869
#> 225 -2.915115 -32.036623
#> 226 -2.753490 -35.545441
#> 227 -2.802601 -34.253551
#> 228 -2.722189 -39.554079
#> 229 -4.089498 -27.945174
#> 230 -2.958889 -49.009002
#> 231 -4.055045 -32.541083
#> 232 -16.953218 -3.397325
#> 233 -3.409814 -40.685358
#> 234 -20.879977 -2.964063
#> 235 -4.097194 -50.902135
#> 236 -22.330375 -2.393566
#> 237 -22.727043 -2.478507
#> 238 -2.805091 -39.753714
#> 239 -3.018993 -31.139851
#> 240 -14.374714 -3.799218
#> 241 -3.008801 -35.278518
#> 242 -21.777249 -4.372194
#> 243 -3.983323 -65.582239
#> 244 -9.754270 -7.750691
#> 245 -3.107232 -52.380234
#> 246 -3.616466 -29.739030
#> 247 -18.892222 -2.315269
#> 248 -2.724757 -44.058193
#> 249 -16.384450 -4.578577
#> 250 -3.320311 -41.221848
#> 251 -18.901661 -2.449105
#> 252 -2.815687 -47.100028
#> 253 -4.347960 -20.388537
#> 254 -3.867798 -46.141116
#> 255 -3.979570 -41.033588
#> 256 -3.437195 -28.390477
#> 257 -3.849179 -28.079956
#> 258 -2.815687 -47.100028
#> 259 -20.185417 -2.278720
#> 260 -2.679752 -40.358850
#> 261 -3.680269 -56.644036
#> 262 -2.956702 -50.248606
#> 263 -21.366355 -2.826749
#> 264 -2.841467 -40.992268
#> 265 -27.853767 -4.245748
#> 266 -16.147464 -2.829734
#> 267 -4.204452 -55.521276
#> 268 -2.817372 -36.347359
#> 269 -24.104705 -3.678010
#> 270 -4.123883 -49.898080
#> 271 -27.361985 -3.380903
#> 272 -3.541020 -45.116720
#> attr(,"logarithm")
#> [1] TRUE
#> attr(,"modelName")
#> [1] "VVV"
#> attr(,"returnCode")
#> [1] 0
data(cross)
z2 <- unmap(cross[,1])
model <- meEEV(data = cross[,-1], z = z2)
EEVdensities <- cdensEEV( data = cross[,-1], parameters = model$parameters)
cbind(cross[,-1],map(EEVdensities))# }
#> X1 X2 map(EEVdensities)
#> 1 1.262954285 -14.370462129 1
#> 2 -0.326233361 4.418706353 1
#> 3 1.329799263 3.794430288 1
#> 4 1.272429321 16.865135087 1
#> 5 0.414641434 9.310628915 1
#> 6 -1.539950042 0.736292793 2
#> 7 -0.928567035 -0.742713858 2
#> 8 -0.294720447 5.454660878 1
#> 9 -0.005767173 -7.986781308 1
#> 10 2.404653389 0.948792512 2
#> 11 0.763593461 3.175870260 1
#> 12 -0.799009249 4.953540226 1
#> 13 -1.147657009 -10.208978717 1
#> 14 -0.289461574 13.161163849 1
#> 15 -0.299215118 6.319050396 1
#> 16 -0.411510833 22.564000336 1
#> 17 0.252223448 -17.010244293 1
#> 18 -0.891921127 -5.308315112 1
#> 19 0.435683299 -15.430520672 1
#> 20 -1.237538422 -3.788981080 1
#> 21 -0.224267885 2.791272389 1
#> 22 0.377395646 15.323135274 1
#> 23 0.133336361 -3.990463239 1
#> 24 0.804189510 -10.787373750 1
#> 25 -0.057106774 -2.766428228 1
#> 26 0.503607972 5.589487825 1
#> 27 1.085769362 1.637119723 1
#> 28 -0.690953840 11.865608381 1
#> 29 -1.284599354 -2.690183821 1
#> 30 0.046726172 -14.833995679 1
#> 31 -0.235706556 8.563486227 1
#> 32 -0.542888255 -10.018106578 1
#> 33 -0.433310317 5.552698281 1
#> 34 -0.649471647 4.621443448 1
#> 35 0.726750747 3.325131934 1
#> 36 1.151911754 15.515047175 1
#> 37 0.992160365 -1.855301100 1
#> 38 -0.429513109 -11.827756259 1
#> 39 1.238304101 0.571266866 2
#> 40 -0.279346282 -2.087797060 1
#> 41 1.757903090 5.715542948 1
#> 42 0.560746091 14.711798569 1
#> 43 -0.452783973 -16.274948214 1
#> 44 -0.832043296 -1.924973563 1
#> 45 -1.166570547 0.633295272 2
#> 46 -1.065590580 4.947369026 1
#> 47 -1.563782051 -6.271411914 1
#> 48 1.156536997 3.515093481 1
#> 49 0.832047129 3.432701332 1
#> 50 -0.227328691 -0.111354928 2
#> 51 0.266137362 -1.119914878 1
#> 52 -0.376702719 13.200701339 1
#> 53 2.441364629 6.065358088 1
#> 54 -0.795339117 17.607827369 1
#> 55 -0.054877474 -2.421369111 1
#> 56 0.250141323 -11.200963665 1
#> 57 0.618243294 -3.561326303 1
#> 58 -0.172623503 0.876569891 1
#> 59 -2.223900274 -2.145482551 2
#> 60 -1.263614385 -3.706451609 1
#> 61 0.358728896 -14.194962440 1
#> 62 -0.011045478 -7.175484858 1
#> 63 -0.940649163 -9.866131045 1
#> 64 -0.115825322 2.774778738 1
#> 65 -0.814968709 3.103156113 1
#> 66 0.242263481 13.856832788 1
#> 67 -1.425098395 -2.965627731 1
#> 68 0.365941123 8.535504157 1
#> 69 0.248412649 -4.313300281 1
#> 70 0.065288182 -13.633981155 1
#> 71 0.019156392 3.910829903 1
#> 72 0.257338377 -4.675830003 1
#> 73 -0.649010078 -7.511031297 1
#> 74 -0.119168762 -6.809828484 1
#> 75 0.664135700 9.805531457 1
#> 76 1.100969102 14.151896257 1
#> 77 0.143771481 9.066279291 1
#> 78 -0.117753598 -2.458421938 1
#> 79 -0.912068367 -11.787115875 1
#> 80 -1.437586241 2.003164478 1
#> 81 -0.797089525 10.020898654 1
#> 82 1.254083106 7.536549576 1
#> 83 0.772142186 2.830650915 1
#> 84 -0.219515627 1.999962649 1
#> 85 -0.424810283 -7.592538509 1
#> 86 -0.418980099 3.994247931 1
#> 87 0.996986861 0.502173998 2
#> 88 -0.275778029 0.611751506 1
#> 89 1.256018817 -1.817655681 1
#> 90 0.646674390 -10.422137286 1
#> 91 1.299312303 -5.334689431 1
#> 92 -0.873262112 6.894588456 1
#> 93 0.008370960 0.350353050 1
#> 94 -0.880871723 0.131821312 2
#> 95 0.596259017 -1.676852712 1
#> 96 0.119717641 12.605317446 1
#> 97 -0.282173877 0.166370131 2
#> 98 1.455988401 2.242764105 1
#> 99 0.229019591 1.342986621 1
#> 100 0.996543929 -8.669098624 1
#> 101 0.781859185 -0.598200967 2
#> 102 -0.776776622 11.582298370 1
#> 103 -0.615989908 4.123127335 1
#> 104 0.046580303 -13.068254611 1
#> 105 -1.130385778 0.696080492 2
#> 106 0.576718782 5.039057457 1
#> 107 -1.280749432 -0.674519130 2
#> 108 1.625447303 7.044279280 1
#> 109 -0.500696596 -1.554017076 1
#> 110 1.678297208 -9.461644006 1
#> 111 -0.412519887 6.565061509 1
#> 112 -0.972286836 2.363987184 1
#> 113 0.025382868 4.892920732 1
#> 114 0.027475337 9.369542685 1
#> 115 -1.680182722 1.777555383 1
#> 116 1.053750863 -14.666204586 1
#> 117 -1.119599105 1.089362091 1
#> 118 0.335617210 -14.736797569 1
#> 119 0.494795767 -4.779387954 1
#> 120 0.138052709 8.583118128 1
#> 121 -0.118792026 -15.485855959 1
#> 122 0.197684262 0.956885579 1
#> 123 -1.068692711 -5.478016469 1
#> 124 -0.803213217 -2.710772844 1
#> 125 -1.113765136 8.785822722 1
#> 126 1.580091684 4.104078024 1
#> 127 1.497818761 11.649672607 1
#> 128 0.262645459 -10.198819855 1
#> 129 -1.232901200 -7.825143157 1
#> 130 -0.003723534 -6.794732627 1
#> 131 1.511672283 -1.166718152 2
#> 132 -0.475698284 -9.016212019 1
#> 133 0.797916438 -7.378814513 1
#> 134 -0.974002561 -8.770972218 1
#> 135 0.689372698 5.438784678 1
#> 136 -0.955839103 4.939088294 1
#> 137 -1.231707058 8.247893912 1
#> 138 -0.956891881 23.954097300 1
#> 139 -0.869782874 -1.622313589 1
#> 140 -0.910680682 6.165132895 1
#> 141 0.741276305 29.397730670 1
#> 142 0.068511533 5.045404136 1
#> 143 -0.323750755 -0.621155677 1
#> 144 -1.086503047 -8.751986424 1
#> 145 -1.015928947 -4.919279272 1
#> 146 -0.767790185 -15.198230952 1
#> 147 -1.119720061 -14.151354291 1
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