Predict response from new data
# S3 method for pibblefit
predict(
object,
newdata = NULL,
response = "LambdaX",
size = NULL,
use_names = TRUE,
summary = FALSE,
iter = NULL,
from_scratch = FALSE,
...
)An object of class pibblefit
An optional matrix for which to evaluate predictions. If NULL (default), the original data of the model is used.
Options = "LambdaX":Mean of regression, "Eta", "Y": counts
the number of counts per sample if response="Y" (as vector or matrix), default if newdata=NULL and response="Y" is to use colsums of m$Y. Otherwise uses median colsums of m$Y as default. If passed as a matrix should have dimensions ncol(newdata) x iter.
if TRUE apply names to output
if TRUE, posterior summary of predictions are returned rather than samples
number of iterations to return if NULL uses object$iter
should predictions of Y come from fitted Eta or from predictions of Eta from posterior of Lambda? (default: false)
other arguments passed to summarise_posterior
(if summary==FALSE) array D x N x iter; (if summary==TRUE) tibble with calculated posterior summaries
currently only implemented for pibblefit objects in coord_system "default" "alr", or "ilr".
sim <- pibble_sim()
fit <- pibble(sim$Y, sim$X)
predict(fit)[,,1:2] # just show 2 samples
#> , , 1
#>
#> s1 s2 s3 s4 s5
#> log(c1/c10) -1.03761376 0.3871368 -1.47680134 0.08561532 -1.816292577
#> log(c2/c10) -0.79835240 1.3782416 -1.46930005 0.91760671 -1.987941424
#> log(c3/c10) 0.07237039 -0.8296609 0.35042673 -0.63876304 0.565363793
#> log(c4/c10) 0.04790629 0.1228250 0.02481216 0.10696988 0.006960437
#> log(c5/c10) -0.06571232 -0.3289576 0.01543455 -0.27324671 0.078160934
#> log(c6/c10) -0.88779717 -0.8590081 -0.89667158 -0.86510072 -0.903531480
#> log(c7/c10) 0.27653375 0.4184410 0.23279001 0.38840904 0.198976182
#> log(c8/c10) 0.13178964 -0.3848268 0.29103964 -0.27549473 0.414139612
#> log(c9/c10) -0.49822775 -0.3832991 -0.53365516 -0.40762160 -0.561040484
#> s6 s7 s8 s9 s10
#> log(c1/c10) -1.13647318 -0.96114432 -2.21319626 -0.05116560 -1.23186823
#> log(c2/c10) -0.94938011 -0.68152988 -2.59429189 0.70864624 -1.09511530
#> log(c3/c10) 0.13495979 0.02395642 0.81664955 -0.55216496 0.19535585
#> log(c4/c10) 0.04270789 0.05192734 -0.01391025 0.09977743 0.03769166
#> log(c5/c10) -0.04744648 -0.07984127 0.15149518 -0.24797426 -0.02982073
#> log(c6/c10) -0.88979476 -0.88625199 -0.91155148 -0.86786457 -0.89172235
#> log(c7/c10) 0.26668721 0.28415022 0.15944399 0.37478546 0.25718572
#> log(c8/c10) 0.16763619 0.10406171 0.55805742 -0.22589778 0.20222657
#> log(c9/c10) -0.50620232 -0.49205928 -0.59305703 -0.41865514 -0.51389744
#> s11 s12 s13 s14 s15
#> log(c1/c10) -0.50304830 -0.88441953 -2.46136774 0.7150784 -2.082732766
#> log(c2/c10) 0.01830423 -0.56431728 -2.97342391 1.8792386 -2.394982582
#> log(c3/c10) -0.26607114 -0.02461922 0.97377069 -1.0372857 0.734051132
#> log(c4/c10) 0.07601574 0.05596181 -0.02696003 0.1400694 -0.007049987
#> log(c5/c10) -0.16448177 -0.09401739 0.19734880 -0.3895500 0.127389984
#> log(c6/c10) -0.87699551 -0.88470166 -0.91656614 -0.8523815 -0.908915284
#> log(c7/c10) 0.32977727 0.29179212 0.13472574 0.4511045 0.172438343
#> log(c8/c10) -0.06204451 0.07624120 0.64804473 -0.5037389 0.510751180
#> log(c9/c10) -0.45510661 -0.48587022 -0.61307598 -0.3568455 -0.582533090
#> s16 s17 s18 s19 s20
#> log(c1/c10) -1.75089915 0.3116468 -1.49106459 -0.81987255 -0.74033589
#> log(c2/c10) -1.88803977 1.2629155 -1.49109004 -0.46570876 -0.34420045
#> log(c3/c10) 0.52396222 -0.7818670 0.35945700 -0.06548489 -0.11584076
#> log(c4/c10) 0.01039907 0.1188555 0.02406214 0.05935594 0.06353827
#> log(c5/c10) 0.06607846 -0.3150096 0.01806991 -0.10594346 -0.12063912
#> log(c6/c10) -0.90221011 -0.8605334 -0.89695979 -0.88339739 -0.88179024
#> log(c7/c10) 0.20548946 0.4109221 0.23136937 0.29822110 0.30614307
#> log(c8/c10) 0.39042787 -0.3574541 0.29621151 0.05283638 0.02399628
#> log(c9/c10) -0.55576547 -0.3893886 -0.53480571 -0.48066348 -0.47424760
#> s21 s22 s23 s24 s25
#> log(c1/c10) -1.786678493 -0.40509963 0.1197725 -0.62425056 -0.53518989
#> log(c2/c10) -1.942699935 0.16794059 0.9697886 -0.16685669 -0.03079853
#> log(c3/c10) 0.546614666 -0.32808394 -0.6603885 -0.18933615 -0.24572182
#> log(c4/c10) 0.008517656 0.08116625 0.1087660 0.06964247 0.07432562
#> log(c5/c10) 0.072689263 -0.18257934 -0.2795578 -0.14208773 -0.15854310
#> log(c6/c10) -0.902933085 -0.87501632 -0.8644105 -0.87944457 -0.87764498
#> log(c7/c10) 0.201925789 0.33953310 0.3918111 0.31770534 0.32657592
#> log(c8/c10) 0.403401506 -0.09756083 -0.2878802 -0.01809641 -0.05038993
#> log(c9/c10) -0.558651641 -0.44720550 -0.4048663 -0.46488348 -0.45769933
#> s26 s27 s28 s29 s30
#> log(c1/c10) 0.6102157 1.1396839 -1.919951213 -1.31893361 0.3830029
#> log(c2/c10) 1.7190397 2.5279092 -2.146300910 -1.22812523 1.3719262
#> log(c3/c10) -0.9708955 -1.3061099 0.630991653 0.25047826 -0.8270436
#> log(c4/c10) 0.1345553 0.1623968 0.001509677 0.03311344 0.1226076
#> log(c5/c10) -0.3701749 -0.4680026 0.097313511 -0.01373402 -0.3281938
#> log(c6/c10) -0.8545004 -0.8438018 -0.905626050 -0.89348163 -0.8590916
#> log(c7/c10) 0.4406600 0.4933958 0.188651628 0.24851388 0.4180293
#> log(c8/c10) -0.4657155 -0.6577014 0.451726372 0.23379659 -0.3833279
#> log(c9/c10) -0.3653043 -0.3225943 -0.569402189 -0.52092063 -0.3836326
#>
#> , , 2
#>
#> s1 s2 s3 s4 s5
#> log(c1/c10) -0.41831569 0.42099962 -0.677039474 0.243374436 -0.87703251
#> log(c2/c10) 0.27448437 1.14147635 0.007229097 0.957993920 -0.19935877
#> log(c3/c10) 0.20555435 -0.96498905 0.566381101 -0.717265709 0.84529956
#> log(c4/c10) 0.19803441 0.61491392 0.069528896 0.526689267 -0.02980564
#> log(c5/c10) 0.24536493 0.03589241 0.309936038 0.080223301 0.35984940
#> log(c6/c10) -0.47426246 -0.33780434 -0.516326457 -0.366683118 -0.54884186
#> log(c7/c10) -0.03065808 0.02153861 -0.046748008 0.010492173 -0.05918550
#> log(c8/c10) -0.11381960 -0.48672748 0.001131407 -0.407808588 0.08998833
#> log(c9/c10) -0.04815767 0.01774951 -0.068473937 0.003801503 -0.08417838
#> s6 s7 s8 s9 s10
#> log(c1/c10) -0.47655341 -0.37326782 -1.11084701 0.162797297 -0.53275028
#> log(c2/c10) 0.21432625 0.32101771 -0.44088337 0.874759728 0.15627627
#> log(c3/c10) 0.28677505 0.14272875 1.17138680 -0.604889543 0.36514950
#> log(c4/c10) 0.16910832 0.22040924 -0.14593896 0.486667410 0.14119589
#> log(c5/c10) 0.25989963 0.23412208 0.41820376 0.100333378 0.27392499
#> log(c6/c10) -0.48373090 -0.46693845 -0.58685604 -0.379783564 -0.49286754
#> log(c7/c10) -0.03427986 -0.02785657 -0.07372633 0.005481112 -0.03777472
#> log(c8/c10) -0.08794457 -0.13383437 0.19387214 -0.372008157 -0.06297630
#> log(c9/c10) -0.05273078 -0.04462029 -0.10253864 -0.002525812 -0.05714364
#> s11 s12 s13 s14 s15
#> log(c1/c10) -0.10340514 -0.32806953 -1.25704392 0.61418884 -1.03399145
#> log(c2/c10) 0.59977919 0.36770643 -0.59190116 1.34103603 -0.36149347
#> log(c3/c10) -0.23363276 0.07969337 1.37527897 -1.23441861 1.06420089
#> log(c4/c10) 0.35444733 0.24285877 -0.21855350 0.71086907 -0.10776557
#> log(c5/c10) 0.16677098 0.22284170 0.45469092 -0.01232288 0.39902250
#> log(c6/c10) -0.42306346 -0.45959000 -0.61062512 -0.30639513 -0.57436066
#> log(c7/c10) -0.01107391 -0.02504570 -0.08281826 0.03355298 -0.06894671
#> log(c8/c10) -0.25373439 -0.15391598 0.25882744 -0.57256147 0.15972521
#> log(c9/c10) -0.02342934 -0.04107110 -0.11401875 0.03291968 -0.09650357
#> s16 s17 s18 s19 s20
#> log(c1/c10) -0.83850949 0.37652877 -0.685441898 -0.29004514 -0.24319038
#> log(c2/c10) -0.15956544 1.09553906 -0.001450400 0.40698468 0.45538449
#> log(c3/c10) 0.79157377 -0.90296818 0.578099465 0.02666301 -0.03868255
#> log(c4/c10) -0.01067164 0.59282569 0.065355496 0.26174510 0.28501739
#> log(c5/c10) 0.35023499 0.04699124 0.312033077 0.21335174 0.20165795
#> log(c6/c10) -0.54257868 -0.34503453 -0.517692546 -0.45340789 -0.44579012
#> log(c7/c10) -0.05678976 0.01877299 -0.047270551 -0.02268098 -0.01976710
#> log(c8/c10) 0.07287255 -0.46696907 0.004864606 -0.17081021 -0.19162779
#> log(c9/c10) -0.08115336 0.01425744 -0.069133737 -0.03808523 -0.03440597
#> s21 s22 s23 s24 s25
#> log(c1/c10) -0.85958696 -0.045703936 0.263496285 -0.17480495 -0.12233963
#> log(c2/c10) -0.18133795 0.659383106 0.978779293 0.52602495 0.58022033
#> log(c3/c10) 0.82096928 -0.314105212 -0.745328461 -0.13405567 -0.20722594
#> log(c4/c10) -0.02114061 0.383106935 0.536683588 0.31898376 0.34504275
#> log(c5/c10) 0.35549541 0.152370172 0.075201381 0.18459062 0.17149656
#> log(c6/c10) -0.54600551 -0.413682248 -0.363411655 -0.43467183 -0.42614188
#> log(c7/c10) -0.05810056 -0.007485498 0.011743543 -0.01551424 -0.01225144
#> log(c8/c10) 0.08223728 -0.279371037 -0.416748727 -0.22201145 -0.24532179
#> log(c9/c10) -0.08280847 -0.018898357 0.005381569 -0.02903601 -0.02491617
#> s26 s27 s28 s29 s30
#> log(c1/c10) 0.552414609 0.86432238 -0.93809743 -0.58404017 0.41856436
#> log(c2/c10) 1.277224782 1.59941780 -0.26243732 0.10329507 1.13896078
#> log(c3/c10) -1.148265750 -1.58326506 0.93046319 0.43668048 -0.96159272
#> log(c4/c10) 0.680186426 0.83510789 -0.06013598 0.11572072 0.61370435
#> log(c5/c10) 0.003094453 -0.07475008 0.37508970 0.28672569 0.03650019
#> log(c6/c10) -0.316438546 -0.26572775 -0.55876995 -0.50120638 -0.33820028
#> log(c7/c10) 0.029711260 0.04910868 -0.06298310 -0.04096442 0.02138717
#> log(c8/c10) -0.545115169 -0.68369582 0.11711949 -0.04018819 -0.48564549
#> log(c9/c10) 0.028068861 0.05256140 -0.08897350 -0.06117117 0.01755828
#>