Predict response from new data
Usage
# S3 method for class 'pibblefit'
predict(
object,
newdata = NULL,
response = "LambdaX",
size = NULL,
use_names = TRUE,
summary = FALSE,
iter = NULL,
from_scratch = FALSE,
...
)Arguments
- object
An object of class pibblefit
- newdata
An optional matrix for which to evaluate predictions. If NULL (default), the original data of the model is used.
- response
Options = "LambdaX":Mean of regression, "Eta", "Y": counts
- size
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.
- use_names
if TRUE apply names to output
- summary
if TRUE, posterior summary of predictions are returned rather than samples
- iter
number of iterations to return if NULL uses object$iter
- from_scratch
should predictions of Y come from fitted Eta or from predictions of Eta from posterior of Lambda? (default: false)
- ...
other arguments passed to summarise_posterior
Value
(if summary==FALSE) array D x N x iter; (if summary==TRUE) tibble with calculated posterior summaries
Examples
sim <- pibble_sim()
fit <- pibble(sim$Y, sim$X)
predict(fit)[,,1:2] # just show 2 samples
#> , , 1
#>
#> s1 s2 s3 s4 s5 s6
#> log(c1/c10) -1.7541080 -1.6484152 -1.83595849 -1.7098286 -1.9030064 -1.6299860
#> log(c2/c10) -4.6367218 -4.3633032 -4.84846226 -4.5221745 -5.0219098 -4.3156284
#> log(c3/c10) -0.4404093 -0.3564041 -0.50546454 -0.4052158 -0.5587546 -0.3417565
#> log(c4/c10) -2.0441677 -1.8881727 -2.16497317 -1.9788144 -2.2639312 -1.8609725
#> log(c5/c10) 1.0182172 2.2799548 0.04110442 1.5468158 -0.7592992 2.4999586
#> log(c6/c10) 3.8787951 2.7632595 4.74268641 3.4114471 5.4503445 2.5687483
#> log(c7/c10) -1.7710388 -1.8850991 -1.68270847 -1.8188238 -1.6103525 -1.9049873
#> log(c8/c10) 1.2918321 1.1076658 1.43445398 1.2146766 1.5512829 1.0755535
#> log(c9/c10) 2.8233447 3.6907676 2.15159649 3.1867471 1.6013328 3.8420164
#> s7 s8 s9 s10 s11 s12
#> log(c1/c10) -1.6873601 -1.8534479 -1.38346245 -1.7822391 -1.9351062 -1.9801432
#> log(c2/c10) -4.4640506 -4.8937058 -3.67789223 -4.7094947 -5.1049492 -5.2214561
#> log(c3/c10) -0.3873578 -0.5193652 -0.14581826 -0.4627681 -0.5842677 -0.6200633
#> log(c4/c10) -1.9456526 -2.1907862 -1.49712131 -2.0856872 -2.3113081 -2.3777794
#> log(c5/c10) 1.8150387 -0.1676799 5.44290223 0.6823944 -1.1424991 -1.6801402
#> log(c6/c10) 3.1743041 4.9272781 -0.03318616 4.1757049 5.7891416 6.2644844
#> log(c7/c10) -1.8430710 -1.6638345 -2.17102744 -1.7406807 -1.5757115 -1.5271091
#> log(c8/c10) 1.1755261 1.4649287 0.64599401 1.3408497 1.6072157 1.6856912
#> log(c9/c10) 3.3711458 2.0080609 5.86523960 2.5924723 1.3378895 0.9682704
#> s13 s14 s15 s16 s17 s18
#> log(c1/c10) -1.8748964 -1.5986196 -2.2010202 -1.5577558 -1.7165224 -1.8368130
#> log(c2/c10) -4.9491914 -4.2344860 -5.7928469 -4.1287748 -4.5394909 -4.8506729
#> log(c3/c10) -0.5364126 -0.3168263 -0.7956175 -0.2843476 -0.4105361 -0.5061437
#> log(c4/c10) -2.2224427 -1.8146778 -2.7037782 -1.7543659 -1.9886940 -2.1662344
#> log(c5/c10) -0.4237274 2.8744042 -4.3169217 3.3622266 1.4669060 0.0309030
#> log(c6/c10) 5.1536565 2.2376910 8.5957328 1.8063943 3.4820974 4.7517058
#> log(c7/c10) -1.6406880 -1.9388369 -1.2887458 -1.9829357 -1.8116000 -1.6817863
#> log(c8/c10) 1.5023020 1.0208984 2.0705624 0.9496947 1.2263404 1.4359430
#> log(c9/c10) 1.8320326 4.0994414 -0.8444715 4.4348109 3.1318106 2.1445832
#> s19 s20 s21 s22 s23 s24
#> log(c1/c10) -1.6036039 -1.9901707 -1.7374903 -1.4936747 -1.5795223 -1.6844577
#> log(c2/c10) -4.2473800 -5.2473966 -4.5937330 -3.9630022 -4.1850830 -4.4565421
#> log(c3/c10) -0.3207879 -0.6280333 -0.4272015 -0.2334155 -0.3016477 -0.3850509
#> log(c4/c10) -1.8220344 -2.3925794 -2.0196411 -1.6597867 -1.7864917 -1.9413688
#> log(c5/c10) 2.8149022 -1.7998473 1.2165964 4.1272131 3.1023826 1.8496879
#> log(c6/c10) 2.2902983 6.3703206 3.7034028 1.1300495 2.0361292 3.1436699
#> log(c7/c10) -1.9334580 -1.5162876 -1.7889722 -2.0520900 -1.9594460 -1.8462032
#> log(c8/c10) 1.0295835 1.7031639 1.2628762 0.8380353 0.9876221 1.1704686
#> log(c9/c10) 4.0585348 0.8859738 2.9597270 4.9607259 4.2561726 3.3949665
#> s25 s26 s27 s28 s29 s30
#> log(c1/c10) -1.9523334 -1.5943117 -1.5910696 -1.8521395 -1.6574181 -1.7294884
#> log(c2/c10) -5.1495145 -4.2233419 -4.2149549 -4.8903211 -4.3865929 -4.5730329
#> log(c3/c10) -0.5979599 -0.3134024 -0.3108256 -0.5183253 -0.3635597 -0.4208415
#> log(c4/c10) -2.3367342 -1.8083198 -1.8035347 -2.1888552 -1.9014603 -2.0078309
#> log(c5/c10) -1.3481533 2.9258302 2.9645335 -0.1520607 2.1724804 1.3121207
#> log(c6/c10) 5.9709660 2.1922239 2.1580053 4.9134688 2.8582804 3.6189472
#> log(c7/c10) -1.5571205 -1.9434858 -1.9469845 -1.6652465 -1.8753835 -1.7976075
#> log(c8/c10) 1.6372335 1.0133922 1.0077429 1.4626488 1.1233530 1.2489333
#> log(c9/c10) 1.1965057 4.1347959 4.1614037 2.0187988 3.6168808 3.0253983
#>
#> , , 2
#>
#> s1 s2 s3 s4 s5 s6
#> log(c1/c10) -1.6316775 -1.6282873 -1.6343029 -1.6302572 -1.6364535 -1.6276962
#> log(c2/c10) -5.0469548 -4.9064852 -5.1557371 -4.9881058 -5.2448463 -4.8819921
#> log(c3/c10) -0.2150639 -0.1657285 -0.2532702 -0.1943951 -0.2845669 -0.1571261
#> log(c4/c10) -3.4691397 -3.0906066 -3.7622827 -3.3105552 -4.0024113 -3.0246034
#> log(c5/c10) 1.9144920 3.3261667 0.8212652 2.5059059 -0.0742535 3.5723143
#> log(c6/c10) 4.0509102 3.1467048 4.7511435 3.6720980 5.3247409 2.9890424
#> log(c7/c10) -2.0554650 -1.9529401 -2.1348621 -2.0125127 -2.1999004 -1.9350633
#> log(c8/c10) 1.4806087 1.4304432 1.5194578 1.4595921 1.5512811 1.4216961
#> log(c9/c10) 2.9056108 3.2862211 2.6108592 3.0650655 2.3694129 3.3525864
#> s7 s8 s9 s10 s11 s12
#> log(c1/c10) -1.6295365 -1.6348639 -1.61978876 -1.632580 -1.6374831 -1.6389277
#> log(c2/c10) -4.9582444 -5.1789811 -4.55435319 -5.084342 -5.2875081 -5.3473639
#> log(c3/c10) -0.1839073 -0.2614339 -0.04205354 -0.228195 -0.2995505 -0.3205729
#> log(c4/c10) -3.2300858 -3.8249200 -2.14169268 -3.569890 -4.1173749 -4.2786723
#> log(c5/c10) 2.8060028 0.5876703 6.86497920 1.538762 -0.5029904 -1.1045215
#> log(c6/c10) 3.4798800 4.9007657 0.88002572 4.291573 5.5993553 5.9846478
#> log(c7/c10) -1.9907177 -2.1518273 -1.69592880 -2.082753 -2.2310381 -2.2747251
#> log(c8/c10) 1.4489278 1.5277589 1.30468734 1.493961 1.5665168 1.5878929
#> log(c9/c10) 3.1459765 2.5478783 4.24034200 2.804308 2.2538185 2.0916361
#> s13 s14 s15 s16 s17 s18
#> log(c1/c10) -1.6355519 -1.6266901 -1.6460125 -1.6253793 -1.6304719 -1.6343303
#> log(c2/c10) -5.2074870 -4.8403049 -5.6409176 -4.7859955 -4.9970022 -5.1568728
#> log(c3/c10) -0.2714457 -0.1424848 -0.4236742 -0.1234104 -0.1975197 -0.2536691
#> log(c4/c10) -3.9017366 -2.9122662 -5.0697315 -2.7659149 -3.3345289 -3.7653433
#> log(c5/c10) 0.3011956 3.9912568 -4.0546419 4.5370489 2.4165002 0.8098515
#> log(c6/c10) 5.0842583 2.7207015 7.8742580 2.3711109 3.7293641 4.7584542
#> log(c7/c10) -2.1726329 -1.9046370 -2.4889818 -1.8649980 -2.0190059 -2.1356911
#> log(c8/c10) 1.5379391 1.4068084 1.6927289 1.3874131 1.4627693 1.5198634
#> log(c9/c10) 2.4706401 3.4655401 1.2962360 3.6126944 3.0409603 2.6077819
#> s19 s20 s21 s22 s23 s24
#> log(c1/c10) -1.6268500 -1.6392494 -1.6311445 -1.62332389 -1.6260775 -1.6294434
#> log(c2/c10) -4.8469293 -5.3606909 -5.0248692 -4.70082929 -4.8149240 -4.9543869
#> log(c3/c10) -0.1448114 -0.3252536 -0.2073071 -0.09349854 -0.1335706 -0.1825525
#> log(c4/c10) -2.9301174 -4.3145855 -3.4096241 -2.53641177 -2.8438705 -3.2196907
#> log(c5/c10) 3.9246839 -1.2384539 2.1364453 5.39294170 4.2463268 2.8447695
#> log(c6/c10) 2.7633427 6.0704341 3.9087447 1.82289471 2.5573242 3.4550492
#> log(c7/c10) -1.9094719 -2.2844521 -2.0393453 -1.80283762 -1.8861122 -1.9879022
#> log(c8/c10) 1.4091742 1.5926523 1.4727213 1.35699792 1.3977442 1.4475502
#> log(c9/c10) 3.4475910 2.0555257 2.9654530 3.84345694 3.5343111 3.1564286
#> s25 s26 s27 s28 s29 s30
#> log(c1/c10) -1.6380357 -1.626552 -1.6264479 -1.6348219 -1.6285761 -1.630888
#> log(c2/c10) -5.3104036 -4.834580 -4.8302708 -5.1772423 -4.9184503 -5.014234
#> log(c3/c10) -0.3075918 -0.140474 -0.1389607 -0.2608232 -0.1699309 -0.203572
#> log(c4/c10) -4.1790731 -2.896838 -2.8852266 -3.8202341 -3.1228499 -3.380966
#> log(c5/c10) -0.7330833 4.048794 4.0920965 0.6051455 3.2059207 2.243321
#> log(c6/c10) 5.7467343 2.683848 2.6561117 4.8895724 3.2237247 3.840289
#> log(c7/c10) -2.2477489 -1.900458 -1.8973133 -2.1505581 -1.9616731 -2.031583
#> log(c8/c10) 1.5746934 1.404764 1.4032250 1.5271379 1.4347163 1.468923
#> log(c9/c10) 2.1917818 3.481053 3.4927281 2.5525899 3.2538008 2.994268
#>