Mass-univariate GLM Analysis
Arguments
- x
tabular data: Predictor variables. Usually a small number of covariates.
- y
data.frame or similar: Each column is a different outcome. The function will train one GLM for each column of
y. Usually a large number of features.- scale_y
Logical: If TRUE, scale each column of
yto have mean 0 and sd 1. IfNULL, set to TRUE ifyis numeric, FALSE otherwise.- center_y
Logical: If TRUE, center each column of
yto have mean 0. IfNULL, set to TRUE ifscale_yis TRUE, FALSE otherwise.- verbosity
Integer: Verbosity level.
Examples
set.seed(2022)
y <- rnormmat(500, 40, return_df = TRUE)
x <- data.frame(
x1 = y[[3]] - y[[5]] + y[[14]] + rnorm(500),
x2 = y[[21]] + rnorm(500),
g = factor(sample(letters[1:3], 500, replace = TRUE))
)
massmod <- massGLM(x, y)
#> 2026-08-09 13:22:36
#> ▶
#> [massGLM]
#> 2026-08-09 13:22:36
#> Scaling and centering 40 numeric features...
#> [preprocess]
#> 2026-08-09 13:22:36
#> Preprocessing done.
#> [preprocess]
#> 2026-08-09 13:22:36
#> Fitting 40 GLMs of family gaussian with 3 predictors each...
#> [massGLM]
#> 2026-08-09 13:22:36
#> GLMs started (total: 40)
#>
#> 2026-08-09 13:22:36
#> ✔ GLMs 40/40 done in 0:00
#>
#> 2026-08-09 13:22:36
#> Done in 0.04 seconds.
#> [massGLM]
# Print table of coefficients, p-values, etc. for all models
summary(massmod)
#> Variable Coefficient_x1 SE_x1 t_value_x1 p_value_x1 Coefficient_x2
#> <char> <num> <num> <num> <num> <num>
#> 1: V1 -0.012679767 0.02338762 -0.54215734 5.879541e-01 -0.0055641132
#> 2: V2 -0.017687690 0.02339836 -0.75593717 4.500464e-01 0.0223463427
#> 3: V3 0.263170206 0.02025190 12.99484042 1.990964e-33 0.0221791440
#> 4: V4 -0.007338213 0.02344898 -0.31294379 7.544552e-01 0.0068974506
#> 5: V5 -0.235121664 0.02097459 -11.20983541 3.813115e-26 0.0072484894
#> 6: V6 0.018038565 0.02345932 0.76892958 4.423018e-01 0.0143364140
#> 7: V7 0.027054762 0.02340841 1.15577114 2.483323e-01 -0.0330508061
#> 8: V8 -0.020749927 0.02334962 -0.88866236 3.746162e-01 -0.0444140267
#> 9: V9 -0.006441816 0.02344281 -0.27478855 7.835933e-01 -0.0043508736
#> 10: V10 -0.055876325 0.02319404 -2.40908135 1.635691e-02 -0.0492601361
#> 11: V11 -0.029557745 0.02342761 -1.26166266 2.076644e-01 -0.0222802112
#> 12: V12 0.004220837 0.02328721 0.18125124 8.562446e-01 -0.0437112443
#> 13: V13 -0.004229181 0.02347677 -0.18014325 8.571138e-01 0.0048352185
#> 14: V14 0.283471523 0.01972047 14.37448442 2.096392e-39 0.0163899530
#> 15: V15 -0.003753707 0.02344075 -0.16013600 8.728393e-01 0.0385694766
#> 16: V16 -0.045650174 0.02313303 -1.97337642 4.900848e-02 0.0992406444
#> 17: V17 -0.041865066 0.02337468 -1.79104338 7.389718e-02 -0.0004062347
#> 18: V18 -0.026682510 0.02338832 -1.14084771 2.544850e-01 -0.0066714719
#> 19: V19 -0.029306127 0.02335566 -1.25477644 2.101520e-01 0.0215269145
#> 20: V20 0.001383420 0.02345633 0.05897854 9.529930e-01 -0.0376334976
#> 21: V21 -0.002274603 0.01573861 -0.14452374 8.851457e-01 0.5418357892
#> 22: V22 0.007540479 0.02340817 0.32213027 7.474900e-01 -0.0146422870
#> 23: V23 -0.020586168 0.02346035 -0.87748786 3.806474e-01 -0.0182728687
#> 24: V24 -0.006135203 0.02334924 -0.26275812 7.928463e-01 -0.0200066681
#> 25: V25 -0.034462947 0.02334355 -1.47633684 1.404893e-01 -0.0280117966
#> 26: V26 -0.002262082 0.02330800 -0.09705176 9.227246e-01 -0.0070509295
#> 27: V27 0.014813442 0.02341115 0.63275165 5.271880e-01 0.0414361694
#> 28: V28 -0.021035698 0.02344844 -0.89710432 3.700994e-01 0.0301940948
#> 29: V29 0.004514274 0.02341383 0.19280374 8.471918e-01 -0.0121115093
#> 30: V30 0.053492936 0.02323023 2.30272911 2.170797e-02 0.0595454585
#> 31: V31 0.003936868 0.02338207 0.16837124 8.663600e-01 0.0178697002
#> 32: V32 0.046017737 0.02337489 1.96868238 4.954707e-02 -0.0024797759
#> 33: V33 -0.004095243 0.02347454 -0.17445464 8.615795e-01 -0.0189508473
#> 34: V34 0.004495177 0.02344969 0.19169451 8.480601e-01 -0.0314526090
#> 35: V35 -0.035311775 0.02341822 -1.50787610 1.322242e-01 -0.0290734520
#> 36: V36 -0.016359856 0.02332380 -0.70142320 4.833686e-01 0.0708787691
#> 37: V37 0.032859132 0.02340056 1.40420270 1.608856e-01 -0.0401700391
#> 38: V38 -0.008043874 0.02334360 -0.34458580 7.305521e-01 0.0208488730
#> 39: V39 -0.022513189 0.02345359 -0.95990396 3.375722e-01 0.0156919202
#> 40: V40 -0.001901627 0.02343811 -0.08113398 9.353682e-01 -0.0399722768
#> Variable Coefficient_x1 SE_x1 t_value_x1 p_value_x1 Coefficient_x2
#> <char> <num> <num> <num> <num> <num>
#> SE_x2 t_value_x2 p_value_x2 Coefficient_gb SE_gb t_value_gb
#> <num> <num> <num> <num> <num> <num>
#> 1: 0.03280732 -0.16959974 8.653942e-01 -0.070138798 0.11065524 -0.63384976
#> 2: 0.03282239 0.68082610 4.962999e-01 0.055613285 0.11070608 0.50235079
#> 3: 0.02840865 0.78071796 4.353416e-01 0.045558724 0.09581904 0.47546630
#> 4: 0.03289340 0.20969100 8.339951e-01 0.039140779 0.11094558 0.35279260
#> 5: 0.02942241 0.24635947 8.055061e-01 0.041931998 0.09923833 0.42253832
#> 6: 0.03290791 0.43565256 6.632787e-01 0.077433185 0.11099450 0.69763083
#> 7: 0.03283649 -1.00652681 3.146540e-01 -0.029469927 0.11075361 -0.26608547
#> 8: 0.03275402 -1.35598698 1.757216e-01 -0.191779807 0.11047546 -1.73594934
#> 9: 0.03288475 -0.13230672 8.947955e-01 -0.013242076 0.11091640 -0.11938790
#> 10: 0.03253578 -1.51402970 1.306565e-01 -0.080744230 0.10973936 -0.73578187
#> 11: 0.03286343 -0.67796366 4.981115e-01 0.069675087 0.11084449 0.62858415
#> 12: 0.03266648 -1.33810681 1.814760e-01 -0.006842482 0.11018021 -0.06210265
#> 13: 0.03293238 0.14682262 8.833318e-01 0.073412758 0.11107705 0.66091743
#> 14: 0.02766317 0.59248274 5.537979e-01 -0.086614422 0.09330463 -0.92829713
#> 15: 0.03288185 1.17297157 2.413713e-01 -0.046203654 0.11090662 -0.41659961
#> 16: 0.03245020 3.05824468 2.346920e-03 -0.137750274 0.10945070 -1.25856000
#> 17: 0.03278918 -0.01238929 9.901200e-01 0.104013282 0.11059404 0.94049627
#> 18: 0.03280831 -0.20334702 8.389474e-01 0.189510373 0.11065857 1.71256844
#> 19: 0.03276249 0.65705975 5.114479e-01 -0.104753259 0.11050403 -0.94795871
#> 20: 0.03290371 -1.14374628 2.532817e-01 0.030051835 0.11098035 0.27078518
#> 21: 0.02207757 24.54236985 1.304594e-87 -0.135573346 0.07446503 -1.82063096
#> 22: 0.03283615 -0.44591969 6.558502e-01 -0.155296466 0.11075248 -1.40219400
#> 23: 0.03290935 -0.55524861 5.789755e-01 0.029027576 0.11099935 0.26151122
#> 24: 0.03275350 -0.61082541 5.415955e-01 -0.191182216 0.11047369 -1.73056781
#> 25: 0.03274551 -0.85543923 3.927219e-01 0.203823990 0.11044677 1.84545005
#> 26: 0.03269563 -0.21565355 8.293466e-01 0.217948509 0.11027853 1.97634581
#> 27: 0.03284033 1.26174636 2.076343e-01 0.094670768 0.11076658 0.85468712
#> 28: 0.03289265 0.91795881 3.590875e-01 -0.047289973 0.11094303 -0.42625457
#> 29: 0.03284409 -0.36875760 7.124661e-01 0.193600309 0.11077927 1.74762221
#> 30: 0.03258655 1.82730153 6.825636e-02 -0.168574171 0.10991061 -1.53373881
#> 31: 0.03279955 0.54481547 5.861258e-01 -0.227306718 0.11062901 -2.05467545
#> 32: 0.03278947 -0.07562719 9.397463e-01 -0.023765896 0.11059504 -0.21489116
#> 33: 0.03292926 -0.57550174 5.652135e-01 -0.005616914 0.11106653 -0.05057252
#> 34: 0.03289440 -0.95616905 3.394536e-01 0.027552269 0.11094895 0.24833285
#> 35: 0.03285025 -0.88502974 3.765703e-01 0.029592546 0.11080005 0.26708063
#> 36: 0.03271781 2.16636670 3.075970e-02 0.033483074 0.11035332 0.30341701
#> 37: 0.03282548 -1.22374555 2.216304e-01 0.066020699 0.11071650 0.59630409
#> 38: 0.03274558 0.63669267 5.246194e-01 0.235176531 0.11044700 2.12931564
#> 39: 0.03289986 0.47696005 6.336011e-01 -0.073933857 0.11096737 -0.66626666
#> 40: 0.03287815 -1.21577007 2.246520e-01 0.088192880 0.11089415 0.79528883
#> SE_x2 t_value_x2 p_value_x2 Coefficient_gb SE_gb t_value_gb
#> <num> <num> <num> <num> <num> <num>
#> p_value_gb Coefficient_gc SE_gc t_value_gc p_value_gc
#> <num> <num> <num> <num> <num>
#> 1: 0.52647165 0.152087843 0.10658242 1.42695060 0.15422459
#> 2: 0.61564418 0.186329352 0.10663138 1.74741573 0.08118535
#> 3: 0.63466439 0.042345845 0.09229228 0.45882325 0.64656249
#> 4: 0.72439411 -0.101359658 0.10686207 -0.94850923 0.34333311
#> 5: 0.67281562 -0.005012235 0.09558572 -0.05243707 0.95820159
#> 6: 0.48573564 0.064357048 0.10690919 0.60197864 0.54746398
#> 7: 0.79028418 -0.123630149 0.10667717 -1.15891854 0.24704816
#> 8: 0.08319504 -0.145185361 0.10640926 -1.36440538 0.17305998
#> 9: 0.90501649 -0.139949153 0.10683396 -1.30996874 0.19081400
#> 10: 0.46221195 0.152441212 0.10570024 1.44220304 0.14987750
#> 11: 0.52991127 0.086823979 0.10676470 0.81322743 0.41647865
#> 12: 0.95050616 0.240461941 0.10612487 2.26583971 0.02389227
#> 13: 0.50897274 0.075822464 0.10698870 0.70869600 0.47884676
#> 14: 0.35370589 -0.026857410 0.08987042 -0.29884594 0.76518302
#> 15: 0.67715188 0.057858110 0.10682454 0.54161815 0.58832528
#> 16: 0.20878257 0.013788875 0.10542221 0.13079668 0.89598929
#> 17: 0.34742179 -0.047783363 0.10652347 -0.44857123 0.65393728
#> 18: 0.08741821 0.033117604 0.10658562 0.31071361 0.75614914
#> 19: 0.34361302 0.117409243 0.10643677 1.10308910 0.27052446
#> 20: 0.78666913 0.044850285 0.10689556 0.41957108 0.67498085
#> 21: 0.06926661 0.010050172 0.07172424 0.14012239 0.88862029
#> 22: 0.16148411 -0.182024962 0.10667608 -1.70633345 0.08857324
#> 23: 0.79380708 0.058163564 0.10691386 0.54402266 0.58667083
#> 24: 0.08415209 0.068568067 0.10640755 0.64439100 0.51962041
#> 25: 0.06556913 0.050795265 0.10638161 0.47748162 0.63322997
#> 26: 0.04867033 -0.091163118 0.10621957 -0.85825162 0.39116891
#> 27: 0.39313784 -0.035526088 0.10668966 -0.33298530 0.73928636
#> 28: 0.67010766 -0.024548318 0.10685961 -0.22972494 0.81840041
#> 29: 0.08114953 0.061239901 0.10670188 0.57393461 0.56627274
#> 30: 0.12573279 -0.016917394 0.10586519 -0.15980129 0.87310280
#> 31: 0.04043473 -0.131780663 0.10655715 -1.23671342 0.21677993
#> 32: 0.82994068 -0.115071368 0.10652443 -1.08023451 0.28056380
#> 33: 0.95968656 -0.064616391 0.10697856 -0.60401251 0.54611204
#> 34: 0.80397988 -0.076527079 0.10686531 -0.71610774 0.47426251
#> 35: 0.78951831 -0.001775533 0.10672189 -0.01663701 0.98673290
#> 36: 0.76169957 -0.109343154 0.10629161 -1.02870921 0.30411874
#> 37: 0.55124469 0.013171456 0.10664142 0.12351164 0.90175209
#> 38: 0.03372102 -0.005588294 0.10638184 -0.05253052 0.95812717
#> 39: 0.50555106 -0.029370277 0.10688306 -0.27478890 0.78359308
#> 40: 0.42682668 -0.002915888 0.10681253 -0.02729912 0.97823216
#> p_value_gb Coefficient_gc SE_gc t_value_gc p_value_gc
#> <num> <num> <num> <num> <num>