Extract item scores from a fitted PCA/varimax model
Examples
# \donttest{
set.seed(42)
theta = rdirichlet(50, 1, k = 3)
phi = rdirichlet(3, 0.1, k = 20)
corpus = draw_corpus(rep(50L, 50), theta, phi)
model = tmfast(corpus, n = 3)
scores(model, k = 3)
#> [,1] [,2] [,3]
#> 1 0.4014702826 1.96161454 0.51109747
#> 2 -1.4499729503 -1.20424553 -0.45410837
#> 3 0.0260839173 1.02981145 0.68988311
#> 4 -1.4021662421 0.24256522 0.67327639
#> 5 0.1372938758 1.10345694 0.28127126
#> 6 0.4328891619 0.84973922 -1.39198030
#> 7 0.9615852015 -0.94379276 1.17114669
#> 8 -0.1220185520 1.88898146 -0.59160200
#> 9 0.2212553936 -0.84174835 -0.88505775
#> 10 -1.0762369874 0.75103141 1.16383027
#> 11 1.0526753880 -0.49088357 -2.37193316
#> 12 0.8202189048 0.19393072 1.28404116
#> 13 -0.4152359092 -0.34705225 1.12288963
#> 14 0.7475156973 1.14198405 0.08478722
#> 15 0.0517492396 -0.98274672 1.03067891
#> 16 0.5773347046 -1.16766035 0.31683409
#> 17 0.2324127062 1.35305057 0.42238105
#> 18 1.2831361813 -0.01259053 -1.26030471
#> 19 0.5149512775 -0.63157266 -1.98300194
#> 20 -0.4375624542 -1.04149646 -0.51035484
#> 21 -0.2862608206 -0.10939326 -2.55946022
#> 22 0.4691444281 -0.71306322 1.04061326
#> 23 1.0035422607 1.30641156 0.45863898
#> 24 1.6072248037 -0.39752603 0.58954203
#> 25 -1.7939181960 -0.45186674 -0.57491091
#> 26 -0.6243461886 -0.78416417 -0.06614102
#> 27 0.6574014411 -1.44357643 1.44523685
#> 28 -0.0451209455 1.08347196 0.70287329
#> 29 -0.3784345463 -0.57454084 -1.68428696
#> 30 0.8645408591 -1.06085215 -0.12426603
#> 31 -0.1577667303 2.50859519 0.23592421
#> 32 -0.3897296308 0.16213173 -1.10967611
#> 33 -0.2021437342 1.15223250 -0.11715326
#> 34 1.7564058594 -1.18734272 0.88753552
#> 35 1.7628548695 -1.13682764 0.55293121
#> 36 -0.2690457887 0.15819227 -1.21754134
#> 37 -0.2378929763 -0.04228513 0.23080548
#> 38 -0.3964277010 1.53290782 -0.27031529
#> 39 -0.7041751160 1.11511552 0.11189972
#> 40 0.0000789906 -0.45425520 -1.94722424
#> 41 -3.1420341023 -0.69924440 1.22915873
#> 42 1.0249841504 -0.43711148 1.16877688
#> 43 0.6364846266 0.69129009 0.86169846
#> 44 0.5739114053 -0.74859865 0.12961814
#> 45 -2.4463666514 -1.32959877 0.71134819
#> 46 0.3657761414 -0.68085847 0.21614615
#> 47 -0.7259982408 -1.01894397 -0.11729096
#> 48 0.6928067772 0.15344697 0.03639876
#> 49 -0.7326116991 0.66830099 -0.26432392
#> 50 -1.4402623823 -0.11442370 0.13967019
# }