Get a `tof_model`'s processed predictor matrix (for glmnet)
Source:R/modeling_helpers.R
tof_get_model_x.RdGet a `tof_model`'s processed predictor matrix (for glmnet)
Examples
feature_tibble <-
dplyr::tibble(
sample = as.character(1:100),
cd45 = runif(n = 100),
pstat5 = runif(n = 100),
cd34 = runif(n = 100),
outcome = (3 * cd45) + (4 * pstat5) + rnorm(100),
class =
as.factor(
dplyr::if_else(outcome > median(outcome), "class1", "class2")
),
multiclass =
as.factor(
c(rep("class1", 30), rep("class2", 30), rep("class3", 40))
),
event = c(rep(0, times = 30), rep(1, times = 70)),
time_to_event = rnorm(n = 100, mean = 10, sd = 2)
)
split_data <- tof_split_data(feature_tibble, split_method = "simple")
# train a regression model
regression_model <-
tof_train_model(
split_data = split_data,
predictor_cols = c(cd45, pstat5, cd34),
response_col = outcome,
model_type = "linear"
)
tof_get_model_x(regression_model)
#> cd45 pstat5 cd34
#> [1,] 0.0877035262 0.829793783 -0.968683447
#> [2,] 0.2918501460 0.319452371 0.800569439
#> [3,] -1.2269611704 -0.928646978 0.807730170
#> [4,] 0.3266083909 -0.607621377 -0.901594550
#> [5,] -0.1231041972 -0.444289623 -0.494594781
#> [6,] 1.5059998407 -0.900442264 1.020763855
#> [7,] 0.1174391067 -0.906308128 1.288819999
#> [8,] 0.6059688310 -0.054174815 -0.643953097
#> [9,] 0.1536267282 -0.027273746 -1.449196567
#> [10,] -1.6076978094 -0.480038641 0.152308688
#> [11,] -0.6057635688 1.149837807 1.559579738
#> [12,] -1.2133761908 -0.320409039 -1.520753810
#> [13,] -1.1736510272 -1.321028232 -1.635396484
#> [14,] -0.5110341672 0.440724908 0.364573728
#> [15,] -1.4754129765 -0.198923773 -1.035149436
#> [16,] 0.3246659766 1.272483299 -0.655049154
#> [17,] 1.3003916085 1.545365366 0.593805445
#> [18,] -0.1257498216 -1.131472650 0.922662129
#> [19,] -1.5982641004 1.108000792 -1.316715983
#> [20,] 0.1582729808 -1.492907432 0.370311344
#> [21,] 1.1089634539 0.943408190 -0.648307102
#> [22,] -1.2383270084 1.374553316 0.001903408
#> [23,] -1.0415443938 1.092551902 0.511497514
#> [24,] -0.5144447422 0.807921061 -1.762456106
#> [25,] 0.6025849687 1.380902083 0.585598230
#> [26,] -0.1234833745 0.389286831 -1.453363542
#> [27,] -1.0696533575 -0.009721015 -0.233546515
#> [28,] -0.8800708859 1.531075744 1.503963564
#> [29,] 0.9416140877 0.384510889 1.663362701
#> [30,] -0.7275012830 1.532845645 -0.037682001
#> [31,] 1.3178970986 -1.548102531 -1.212087715
#> [32,] -1.4915239562 -0.212303725 -0.195494079
#> [33,] -0.1161436057 1.182686680 0.268296391
#> [34,] -0.7647825642 1.286841294 -1.808555887
#> [35,] -0.5200457880 0.831032273 0.645547868
#> [36,] -0.9817538172 0.395569966 0.473801688
#> [37,] 0.7972317256 0.544322467 -0.670152761
#> [38,] 1.1053709483 -0.306839661 1.480771002
#> [39,] -0.1886989489 -0.046366861 0.462121536
#> [40,] 0.6288941216 -0.982222320 -1.424423188
#> [41,] -1.6273743429 -0.635951656 -0.473005223
#> [42,] 0.9809659311 -0.425944473 1.700537277
#> [43,] -1.5523262820 -0.856909935 -0.839871795
#> [44,] -0.6576770757 -0.977890262 -0.785642128
#> [45,] -0.9958291894 -1.594200427 1.676041390
#> [46,] 0.2828183780 1.539237636 -1.168691796
#> [47,] 0.0008241368 0.107879703 -0.031884374
#> [48,] 0.8467101981 1.564892225 1.204252566
#> [49,] -0.8175202372 1.156048099 -1.322301425
#> [50,] -1.4530059713 0.799186164 -0.938520711
#> [51,] 1.7121255067 1.386763854 -1.094021634
#> [52,] -0.7507502536 1.061895897 1.442621826
#> [53,] 1.3378727571 -0.777024917 -0.612565242
#> [54,] 1.5508399521 0.648075118 -0.394862653
#> [55,] 1.6910142455 0.196987192 -1.814924127
#> [56,] 0.8097644324 -1.837699125 -0.324791846
#> [57,] 1.2046067475 -1.434562840 0.633540750
#> [58,] 0.3373400699 1.538177325 -0.590777651
#> [59,] 1.4076883817 0.357989206 0.216105111
#> [60,] 0.7676643664 1.033249521 1.742619876
#> [61,] 1.0588637833 -0.172356097 -0.360171281
#> [62,] 1.4316085003 -0.768483954 0.748025427
#> [63,] 1.1211474035 -0.794173026 0.734620103
#> [64,] 1.4746509859 -1.767649605 1.312726068
#> [65,] 0.8382666407 1.040543856 1.534130853
#> [66,] -1.6959048626 -1.347244584 0.670641955
#> [67,] -1.1139537995 -0.541029219 0.644128079
#> [68,] -0.4834780130 -0.809639190 0.637378507
#> [69,] -1.2833087135 -1.345892974 0.635578574
#> [70,] 1.4278697403 0.157837180 0.334544325
#> [71,] 0.8179944057 -0.861551862 0.429984418
#> [72,] -0.3572864259 -1.737504943 0.309427107
#> [73,] 0.1073285464 1.509181482 1.004547019
#> [74,] -0.3276563590 0.351424101 0.573437637
#> [75,] -0.2599908109 0.086931422 -0.821823365
#> [76,] 0.1755280279 0.230063248 1.067548665
#> [77,] -0.3734789132 -0.205327829 -0.098932151
#> [78,] 0.7537409485 -0.735763209 1.084537004
#> [79,] 0.3607494988 -1.597975379 1.427041573
#> [80,] -0.4521013676 1.148874155 1.113521539
#> [81,] -0.1396013375 -0.042671954 -1.104942414
#> [82,] 0.7150689340 -0.774626941 -1.434992466
#> [83,] 1.6054448746 1.423435202 -0.533968844
#> [84,] 0.3902314924 -1.244460786 -0.380625796
#> [85,] -0.2840894171 0.249574019 0.500213366
#> [86,] 0.3580295343 1.090010312 -0.733583127
#> [87,] -1.3019738319 -0.118892177 -0.982532835
#> [88,] -1.4729152951 -0.939569685 -0.583579366
#> [89,] -1.1682440613 0.318443541 0.347178575
#> [90,] 0.1282435264 -1.514481669 -0.368335955
#> [91,] 1.2907191832 -0.703374750 -0.171244219
#> [92,] 0.5985112338 -1.734542668 1.295446904
#> [93,] 1.4654890208 1.176278968 1.627644913
#> [94,] -0.2046741225 0.174062751 -1.427151447
#> [95,] 0.7689103424 -1.187481662 -1.645146346
#> [96,] -1.2414118259 -0.881406684 -1.310608886
#> [97,] -1.1787887576 1.267665202 0.125200033
#> [98,] 1.5715367494 -0.523919150 0.748005728
#> [99,] -1.7782543970 0.398499084 -0.672255047
#> [100,] -0.4746675978 0.452923311 0.089694752