Predict either posterior probabilities for each group or latent positions based on new samples
an R6 object with class PLNmixturefit
A data frame in which to look for variables, offsets and counts with which to predict.
The type of prediction required. The default posterior are posterior probabilities for each group ,
response is the group with maximal posterior probability and latent is the averaged latent in the latent space,
with weights equal to the posterior probabilities.
User-specified prior group probabilities in the new data. The default uses a uniform prior.
a list-like structure for controlling the fit. See PLNmixture_param() for details.
additional parameters for S3 compatibility. Not used
A matrix of posterior probabilities for each group (if type = "posterior"), a matrix of (average) position in the latent space (if type = "position") or a vector of predicted groups (if type = "response").
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- PLNmixture(Abundance ~ 1 + offset(log(Offset)),
data = trichoptera, control = PLNmixture_param(smoothing = "none")) %>% getBestModel()
#>
#> Initialization...
#>
#> Adjusting 5 PLN mixture models.
#> number of cluster = 1
number of cluster = 2
number of cluster = 3
number of cluster = 4
number of cluster = 5
#> Post-treatments
#> DONE!
predict(myPLN, trichoptera, "posterior")
#> [,1] [,2] [,3] [,4] [,5]
#> 1 2.220446e-16 6.424339e-12 2.075454e-12 1.000000e+00 2.220446e-16
#> 2 1.503029e-10 1.000000e+00 1.182492e-13 2.084102e-08 2.220446e-16
#> 3 1.000000e+00 6.230272e-13 2.220446e-16 9.659413e-12 2.220446e-16
#> 4 1.000000e+00 2.220446e-16 2.220446e-16 2.220446e-16 2.220446e-16
#> 5 1.000000e+00 4.137427e-16 2.220446e-16 2.560782e-13 2.220446e-16
#> 6 1.731135e-08 9.999999e-01 3.211941e-14 1.058280e-07 2.343805e-14
#> 7 2.962746e-11 1.174848e-11 4.930929e-11 1.000000e+00 1.436619e-14
#> 8 1.000000e+00 6.363972e-13 2.220446e-16 1.450345e-09 3.272443e-15
#> 9 1.417948e-10 9.999997e-01 1.663344e-12 3.088539e-07 7.561223e-15
#> 10 3.834231e-10 5.920676e-11 2.893677e-10 1.000000e+00 2.009802e-12
#> 11 7.362231e-09 9.999994e-01 1.716587e-09 5.475832e-07 3.377947e-10
#> 12 9.999998e-01 5.073781e-11 2.429586e-09 2.044824e-07 6.190759e-09
#> 13 1.211417e-15 2.220446e-16 2.220446e-16 1.000000e+00 2.220446e-16
#> 14 2.220446e-16 2.220446e-16 1.631856e-15 1.000000e+00 2.220446e-16
#> 15 1.000000e+00 2.220446e-16 2.220446e-16 2.220446e-16 2.220446e-16
#> 16 1.000000e+00 2.220446e-16 2.220446e-16 2.220446e-16 2.220446e-16
#> 17 2.220446e-16 2.220446e-16 2.220446e-16 1.000000e+00 2.220446e-16
#> 18 2.220446e-16 2.220446e-16 2.220446e-16 1.000000e+00 2.220446e-16
#> 19 2.220446e-16 2.220446e-16 2.220446e-16 1.000000e+00 2.220446e-16
#> 20 2.220446e-16 2.220446e-16 2.220446e-16 1.000000e+00 2.220446e-16
#> 21 1.195147e-14 1.703986e-13 4.283113e-16 1.000000e+00 2.220446e-16
#> 22 5.461648e-13 3.156503e-12 1.526172e-09 1.000000e+00 4.043409e-16
#> 23 2.220446e-16 2.220446e-16 1.000000e+00 1.946094e-10 2.220446e-16
#> 24 2.316785e-09 6.283741e-11 1.219347e-09 1.000000e+00 1.096617e-10
#> 25 2.220446e-16 2.220446e-16 1.000000e+00 7.513817e-12 8.722912e-15
#> 26 2.220446e-16 2.220446e-16 1.000000e+00 2.220446e-16 2.220446e-16
#> 27 2.220446e-16 2.220446e-16 1.000000e+00 2.220446e-16 2.220446e-16
#> 28 2.220446e-16 2.220446e-16 1.000000e+00 9.039736e-11 3.962547e-12
#> 29 6.219415e-13 4.800307e-15 1.000000e+00 1.470165e-09 2.816704e-10
#> 30 2.220446e-16 2.220446e-16 2.220446e-16 2.220446e-16 1.000000e+00
#> 31 2.220446e-16 2.220446e-16 2.220446e-16 2.220446e-16 1.000000e+00
#> 32 2.220446e-16 1.000000e+00 2.220446e-16 2.220446e-16 2.220446e-16
#> 33 2.220446e-16 1.000000e+00 2.220446e-16 2.220446e-16 2.220446e-16
#> 34 2.220446e-16 1.000000e+00 2.220446e-16 2.220446e-16 2.220446e-16
#> 35 2.220446e-16 1.000000e+00 2.220446e-16 3.378036e-14 2.220446e-16
#> 36 1.000000e+00 4.504597e-13 2.220446e-16 5.972804e-13 2.220446e-16
#> 37 2.220446e-16 1.000000e+00 2.220446e-16 6.886163e-16 2.220446e-16
#> 38 3.831061e-16 6.396380e-12 1.463553e-11 1.000000e+00 2.220446e-16
#> 39 9.747562e-14 2.592122e-10 1.539561e-11 1.000000e+00 2.220446e-16
#> 40 1.000000e+00 7.531773e-12 8.422060e-15 6.226495e-09 1.094083e-11
#> 41 2.135303e-10 9.999999e-01 2.220446e-16 1.124455e-07 2.220446e-16
#> 42 2.220446e-16 2.220446e-16 1.117565e-14 1.000000e+00 2.220446e-16
#> 43 2.603341e-14 1.000000e+00 2.220446e-16 8.322292e-10 2.220446e-16
#> 44 2.220446e-16 2.220446e-16 2.220446e-16 1.000000e+00 2.220446e-16
#> 45 2.220446e-16 2.220446e-16 2.220446e-16 1.000000e+00 2.220446e-16
#> 46 2.220446e-16 2.220446e-16 2.220446e-16 1.000000e+00 2.220446e-16
#> 47 4.164156e-11 2.220446e-16 3.272878e-12 1.000000e+00 7.772385e-14
#> 48 2.220446e-16 2.220446e-16 1.000000e+00 1.955147e-09 2.220446e-16
#> 49 2.220446e-16 2.220446e-16 1.000000e+00 2.220446e-16 2.220446e-16
predict(myPLN, trichoptera, "position")
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> 1 -6.110182 -6.799941 -2.134668 -5.403469 -0.55688210 -3.688139 -5.022274
#> 2 -18.529808 -18.529808 -1.955237 -6.949181 -0.54742536 -3.533558 -18.529808
#> 3 -24.358992 -24.358992 -3.955424 -24.358992 -0.18481066 -3.810601 -24.358992
#> 4 -24.358992 -24.358992 -4.092619 -24.358992 -0.09206127 -3.762713 -24.358992
#> 5 -24.358992 -24.358992 -3.617314 -24.358992 -0.14439183 -3.651533 -24.358992
#> 6 -18.529807 -18.529807 -2.324391 -6.951523 -0.33038008 -3.269938 -18.529807
#> 7 -6.102318 -6.795996 -2.189533 -5.387560 -0.63172469 -3.603598 -4.999308
#> 8 -24.358992 -24.358992 -3.939424 -24.358992 -0.17475406 -3.607124 -24.358992
#> 9 -18.529805 -18.529805 -1.700861 -6.948710 -0.48717876 -3.521847 -18.529804
#> 10 -6.100806 -6.795239 -2.120386 -5.384492 -0.60863656 -3.585673 -4.994765
#> 11 -18.529802 -18.529802 -2.153728 -6.944931 -0.47996959 -3.415940 -18.529801
#> 12 -24.358989 -24.358989 -4.013541 -24.358988 -0.13420844 -3.624724 -24.358988
#> 13 -5.964736 -6.803678 -2.723391 -5.417897 -0.26300789 -3.500699 -5.043207
#> 14 -6.111592 -6.800688 -2.280998 -5.406289 -0.52978149 -3.432884 -4.876673
#> 15 -24.358992 -24.358992 -4.659984 -24.358992 -0.18314176 -3.499379 -24.358992
#> 16 -24.358992 -24.358992 -4.181649 -24.358992 -0.19467198 -3.211196 -24.358992
#> 17 -6.120702 -6.805290 -2.685922 -5.424297 -0.32182502 -3.658945 -5.052326
#> 18 -6.118169 -6.804050 -2.540682 -5.269709 -0.51969406 -3.265683 -5.045251
#> 19 -6.119980 -6.804921 -2.314947 -5.422881 -0.62411787 -3.063113 -5.050312
#> 20 -6.144964 -6.665361 -2.672686 -5.470784 -0.58554898 -3.447837 -4.979959
#> 21 -6.112328 -6.801061 -2.204073 -5.407756 -0.57413278 -3.709864 -5.028650
#> 22 -6.104198 -6.796939 -2.138407 -5.391367 -0.76358723 -3.625102 -5.004842
#> 23 -5.668569 -5.708153 -3.796903 -5.456468 -0.90015464 -5.665348 -5.665348
#> 24 -6.100427 -6.795050 -2.249059 -5.383722 -0.53918255 -3.581082 -4.993627
#> 25 -5.676162 -5.715458 -3.317827 -5.664670 -1.03874196 -5.474062 -5.474062
#> 26 -5.685850 -5.724785 -3.722198 -5.674467 -1.36921006 -5.682683 -5.682683
#> 27 -5.688789 -5.727622 -3.739324 -5.677439 -1.14308950 -5.685632 -5.685632
#> 28 -5.661627 -5.701478 -3.753709 -5.649968 -1.18669416 -5.658383 -5.658383
#> 29 -5.662403 -5.702224 -3.758707 -5.650754 -0.97865587 -5.659162 -5.659162
#> 30 -16.848772 -8.370860 -6.654430 -16.848772 -0.30984634 -7.223777 -6.458345
#> 31 -16.848642 -8.612756 -5.752849 -16.848642 -0.10981096 -7.523556 -6.590010
#> 32 -18.529809 -18.529809 -2.518679 -6.983021 -0.96713075 -3.242054 -18.529809
#> 33 -18.529809 -18.529809 -2.434398 -7.002944 -0.77876832 -2.995067 -18.529809
#> 34 -18.529810 -18.529810 -3.003542 -7.076246 -0.40097135 -2.878169 -18.529810
#> 35 -18.529809 -18.529809 -2.420933 -6.973369 -0.81907259 -2.753273 -18.529809
#> 36 -24.358992 -24.358992 -3.661824 -24.358992 -0.39701586 -3.552160 -24.358992
#> 37 -18.529809 -18.529809 -1.415859 -6.582228 -1.06487072 -3.445892 -18.529809
#> 38 -6.104948 -6.797316 -2.048360 -5.392881 -0.99426464 -3.633440 -5.007057
#> 39 -6.104574 -6.797128 -1.913930 -5.392125 -0.80059692 -3.629289 -5.005951
#> 40 -24.358992 -24.358992 -3.878994 -24.358992 -0.13812577 -3.723802 -24.358992
#> 41 -18.529808 -18.529808 -2.717527 -6.975140 -0.37776097 -3.961394 -18.529808
#> 42 -6.118894 -6.804422 -2.555857 -5.271354 -0.49861973 -3.770004 -5.047276
#> 43 -18.529809 -18.529809 -3.057054 -6.986047 -0.30838174 -3.852748 -18.529809
#> 44 -6.115258 -6.802561 -2.195877 -5.263068 -0.46569135 -3.605531 -4.741611
#> 45 -6.122140 -6.806025 -2.445598 -5.278696 -0.53254126 -3.431040 -4.910917
#> 46 -6.004316 -6.821753 -2.966199 -5.484723 -0.38470713 -3.904111 -5.001367
#> 47 -6.105323 -6.797504 -2.304706 -5.393635 -0.46314327 -3.637556 -5.008160
#> 48 -5.480910 -5.718347 -3.857267 -5.667706 -0.72733793 -5.675976 -5.675976
#> 49 -5.718598 -5.565624 -3.747439 -5.707568 -1.15685808 -5.715531 -5.715531
#> [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> 1 -4.725503 -6.786786 -2.839136 -3.303525 -4.876151 -5.390690 -5.292232
#> 2 -18.529808 -18.529808 -3.881874 -5.833595 -3.008714 -3.016967 -6.962054
#> 3 -6.615300 -24.358992 -4.583266 -5.264453 -3.746516 -6.460525 -24.358992
#> 4 -6.663363 -24.358992 -4.689943 -5.423000 -4.345786 -6.516044 -24.358992
#> 5 -6.628592 -24.358992 -4.669009 -5.311914 -4.427155 -6.475967 -24.358992
#> 6 -18.529807 -18.529807 -3.923274 -5.840582 -3.394980 -3.096962 -6.964367
#> 7 -4.694663 -6.782784 -2.652784 -3.181902 -5.003848 -5.374573 -5.429480
#> 8 -6.613982 -24.358992 -4.574036 -5.259557 -4.511855 -6.458991 -24.358992
#> 9 -18.529804 -18.529805 -3.873140 -5.832183 -3.661773 -2.999401 -6.961590
#> 10 -4.688579 -6.782017 -2.906680 -3.154894 -4.999354 -5.371465 -5.426551
#> 11 -18.529801 -18.529802 -3.796532 -5.820719 -3.568570 -2.831080 -6.957859
#> 12 -6.603637 -24.358989 -4.495689 -5.219742 -4.428573 -6.446916 -24.358988
#> 13 -4.608374 -6.790574 -2.971536 -3.397582 -4.900224 -5.405291 -5.458617
#> 14 -4.731157 -6.787544 -2.989160 -3.323723 -5.031002 -5.393540 -5.447460
#> 15 -6.399770 -24.358992 -3.793019 -5.028595 -4.963365 -6.638194 -24.358992
#> 16 -6.657394 -24.358992 -4.350485 -4.867584 -4.454489 -6.509196 -24.358992
#> 17 -4.765057 -6.638321 -2.815416 -3.092073 -5.056682 -5.411769 -5.464774
#> 18 -4.755805 -6.790952 -3.094756 -2.634449 -5.049635 -5.406740 -5.459993
#> 19 -4.762429 -6.791893 -3.011400 -2.868770 -5.054677 -5.410342 -5.463417
#> 20 -4.715702 -6.804895 -3.057982 -2.319137 -5.121282 -5.458788 -5.366963
#> 21 -4.733959 -6.787922 -2.543352 -3.205678 -5.033109 -5.395025 -5.448869
#> 22 -4.702189 -6.783741 -2.703599 -3.213675 -5.009407 -5.378429 -5.433132
#> 23 -4.956965 -5.668569 -3.287558 -3.314002 -5.675626 -2.509868 -3.551891
#> 24 -4.687033 -6.781825 -2.897770 -3.147895 -4.998223 -5.370685 -5.425810
#> 25 -4.972068 -5.676162 -3.526042 -3.050912 -5.683166 -2.630817 -3.432894
#> 26 -4.991010 -5.685850 -3.431296 -3.146374 -5.495897 -2.387094 -3.346097
#> 27 -4.996687 -5.688789 -2.873052 -3.029803 -5.695709 -2.021940 -3.853254
#> 28 -4.742303 -5.661627 -3.409062 -3.246116 -5.668732 -2.371724 -3.497437
#> 29 -4.944519 -5.662403 -3.415967 -3.066627 -5.669503 -2.389011 -3.696688
#> 30 -7.754605 -6.843484 -5.378608 -3.153921 -8.608318 -4.191009 -3.180111
#> 31 -7.856946 -6.910185 -5.918497 -4.071927 -8.477000 -5.165918 -4.018249
#> 32 -18.529809 -18.529809 -4.300928 -5.928268 -3.490288 -1.124052 -6.610318
#> 33 -18.529809 -18.529809 -3.831564 -5.639573 -2.428545 -1.885008 -7.015177
#> 34 -18.529810 -18.529810 -4.512129 -6.144420 -4.015608 -2.528696 -7.087711
#> 35 -18.529809 -18.529809 -3.466020 -5.902509 -3.555169 -2.977630 -6.985944
#> 36 -6.632093 -24.358992 -4.689729 -5.323887 -4.633544 -6.052042 -24.358992
#> 37 -18.529809 -18.529809 -3.672009 -5.531841 -3.534513 -2.640784 -6.984626
#> 38 -4.705168 -6.784123 -2.459894 -3.225839 -5.011610 -5.226195 -5.434585
#> 39 -4.703681 -6.783932 -2.849394 -3.219801 -5.010509 -5.225304 -5.433859
#> 40 -6.609339 -24.358992 -4.540267 -5.242002 -4.266069 -6.453577 -24.358992
#> 41 -18.529808 -18.529808 -3.493774 -5.907301 -3.812520 -3.587598 -6.987693
#> 42 -4.472109 -6.791329 -3.105344 -3.414557 -5.051654 -5.110512 -5.461365
#> 43 -18.529809 -18.529809 -2.526976 -5.582703 -4.177306 -3.196501 -6.998472
#> 44 -4.599081 -6.789433 -2.823086 -3.371090 -4.893153 -5.400919 -5.454464
#> 45 -4.487383 -6.793021 -2.559798 -3.334586 -4.915214 -5.414622 -5.467487
#> 46 -4.872049 -6.808889 -3.381596 -2.895119 -4.872906 -5.472878 -5.243388
#> 47 -4.554923 -6.784314 -2.866013 -3.091812 -5.012709 -5.380726 -5.435310
#> 48 -4.784674 -5.480910 -3.216726 -3.404184 -5.686149 -2.280476 -3.800664
#> 49 -5.052563 -5.718598 -3.638586 -3.153479 -5.725339 -1.055784 -3.994269
#> [,15] [,16] [,17]
#> 1 -4.025878 -5.438084 -2.703454
#> 2 -4.912559 -6.245884 -2.845294
#> 3 -5.033563 -7.267200 -3.243823
#> 4 -4.879566 -7.293043 -3.631568
#> 5 -5.091884 -7.274225 -3.202168
#> 6 -4.929283 -6.250563 -2.935152
#> 7 -4.269546 -5.422838 -2.634343
#> 8 -5.240027 -7.266509 -2.741581
#> 9 -4.909135 -6.244942 -2.825384
#> 10 -4.260234 -5.419877 -2.588748
#> 11 -4.880728 -6.237329 -2.630285
#> 12 -5.199434 -7.261116 -2.924840
#> 13 -4.355793 -5.302134 -2.957230
#> 14 -4.179449 -5.440936 -2.404471
#> 15 -5.397695 -7.153158 -2.546188
#> 16 -5.387874 -7.289768 -2.812457
#> 17 -4.372855 -5.458359 -2.704757
#> 18 -4.359635 -5.453549 -2.559458
#> 19 -4.369113 -5.456993 -2.331308
#> 20 -3.896441 -5.503414 -2.440876
#> 21 -4.328030 -5.442354 -2.423825
#> 22 -3.979795 -5.426513 -2.551350
#> 23 -1.571077 -18.789748 -2.323225
#> 24 -4.257911 -5.419134 -2.576645
#> 25 -1.491463 -18.789748 -2.599716
#> 26 -1.228548 -18.789749 -2.145872
#> 27 -1.551811 -18.789749 -2.404968
#> 28 -1.431042 -18.789748 -2.333747
#> 29 -1.466246 -18.789748 -2.351582
#> 30 -1.964561 -7.754605 -5.122116
#> 31 -2.907274 -7.856946 -6.408932
#> 32 -5.116131 -6.311247 -2.559464
#> 33 -3.973990 -6.347784 -2.980966
#> 34 -4.473480 -6.473240 -2.320653
#> 35 -5.064803 -6.293063 -1.535715
#> 36 -5.305486 -7.276091 -1.755610
#> 37 -5.057374 -6.290525 -1.923579
#> 38 -4.285406 -5.427975 -2.316421
#> 39 -4.283173 -5.427245 -2.431373
#> 40 -5.222138 -7.264081 -3.104159
#> 41 -5.074537 -5.556805 -2.006938
#> 42 -4.363448 -5.454930 -1.992261
#> 43 -5.131441 -6.316882 -2.843485
#> 44 -4.344086 -5.447985 -2.920336
#> 45 -4.380286 -5.312431 -2.730652
#> 46 -4.164865 -5.237481 -2.245894
#> 47 -4.287632 -5.428704 -2.714650
#> 48 -2.297349 -18.789748 -2.024014
#> 49 -1.949156 -18.789749 -2.737653
predict(myPLN, trichoptera, "response")
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
#> 4 2 1 1 1 2 4 1 2 4 2 1 4 4 1 1 4 4 4 4 4 4 3 4 3 3
#> 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
#> 3 3 3 5 5 2 2 2 2 1 2 4 4 1 2 4 2 4 4 4 4 3 3
#> Levels: 1 2 3 4 5