Last updated: 2022-09-20
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Knit directory: udr-data-application/
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| Rmd | 0f24122 | yunqiyang0215 | 2022-09-20 | wflow_publish("analysis/t2d_application.rmd") |
Data Description: In total, it contains 324 SNPs with 65 conditions. All SNPs are strong signals in at least one condition, meaning abs(z-score) >= 1.96. After remove SNPs with missing z-score, there are 305 SNPs.
Initialization: I randomly initialized 20 unconstrained Us and 4 rank1 Us for fitting. I use nuclear norm regularization with λ (regularization strength) equals the number of conditions. V is set to be identity matrix.
library("readxl")
library(udr)
library(gridExtra)
dat = read_excel("./data/t2d.xlsx")dat = data.frame(dat)
rownames(dat) = dat[,1]
dat = dat[,-1]summary(dat) MAGIC_dv1_2hrG ADIPOGen_dv1_Adiponectin BFpercent_dv1_BFP
Min. :-4.7016 Min. :-9.0448 Min. :-6.708333
1st Qu.:-0.1263 1st Qu.:-1.4799 1st Qu.:-1.081263
Median : 0.6257 Median :-0.4483 Median :-0.098020
Mean : 0.6327 Mean :-0.6359 Mean :-0.009601
3rd Qu.: 1.3767 3rd Qu.: 0.4888 3rd Qu.: 0.981013
Max. : 4.6160 Max. : 3.8193 Max. : 9.941176
NA's :3
GiantUKBB_eu_dv1_BMI MAGIC_dv1_FG MAGIC_dv2_FI MAGIC_eu_dv6_HBA1C
Min. :-8.4444 Min. :-3.636 Min. :-4.6341 Min. :-1.737
1st Qu.:-2.0192 1st Qu.: 0.297 1st Qu.:-0.7466 1st Qu.: 0.460
Median : 0.1177 Median : 1.050 Median : 0.2190 Median : 1.307
Mean : 1.3130 Mean : 1.210 Mean : 0.3513 Mean : 1.528
3rd Qu.: 3.1202 3rd Qu.: 1.805 3rd Qu.: 1.3587 3rd Qu.: 2.250
Max. :45.7647 Max. :13.953 Max. : 6.0000 Max. :11.200
NA's :5
GiantUKBB_eu_dv1_HEIGHT GIANT_eu_females_dv5_HIPCadjBMI
Min. :-40.7143 Min. :-12.4000
1st Qu.: -2.9706 1st Qu.: -1.4341
Median : -0.5333 Median : -0.3509
Mean : -0.8867 Mean : -0.5513
3rd Qu.: 1.8333 3rd Qu.: 0.5429
Max. : 24.9286 Max. : 4.6296
NA's :5 NA's :1
GIANT_eu_males_dv5_HIPCadjBMI MAGIC_dv1_HOMAB MAGIC_dv1_HOMAIR
Min. :-6.7308 Min. :-7.8378 Min. :-2.7660
1st Qu.:-1.2222 1st Qu.:-1.2320 1st Qu.:-0.5503
Median :-0.2778 Median :-0.3148 Median : 0.3604
Mean :-0.4019 Mean :-0.2984 Mean : 0.4397
3rd Qu.: 0.6455 3rd Qu.: 0.8188 3rd Qu.: 1.3436
Max. : 4.2708 Max. : 3.1429 Max. : 4.7500
NA's :11
MAGIC_dv4_ISIadjAgeSexBMI Leptin_eu_dv1_LEP Leptin_eu_dv1_LEPadjBMI
Min. :-6.4000 Min. :-3.8600 Min. :-4.2462
1st Qu.:-1.2121 1st Qu.:-0.5913 1st Qu.:-0.5949
Median :-0.2864 Median : 0.2083 Median : 0.2239
Mean :-0.4544 Mean : 0.1611 Mean : 0.2489
3rd Qu.: 0.4023 3rd Qu.: 0.9278 3rd Qu.: 1.0478
Max. : 2.9545 Max. : 4.9796 Max. : 5.2381
NA's :2 NA's :1 NA's :1
MAGIC_dv1_PI GIANT_eu_females_dv5_WAIST GIANT_eu_males_dv5_WAIST
Min. :-13.0000 Min. :-4.6000 Min. :-7.08333
1st Qu.: -0.4571 1st Qu.:-0.6598 1st Qu.:-1.09240
Median : 0.3392 Median : 0.3239 Median :-0.07896
Mean : 0.2867 Mean : 0.5726 Mean : 0.06923
3rd Qu.: 1.0064 3rd Qu.: 1.4706 3rd Qu.: 0.90079
Max. : 8.4444 Max. :15.7778 Max. :16.38298
NA's :1 NA's :6
GIANT_eu_females_dv5_WAISTadjBMI GIANT_eu_males_dv5_WAISTadjBMI
Min. :-4.3182 Min. :-5.3846
1st Qu.:-0.3024 1st Qu.:-1.0300
Median : 0.4615 Median :-0.1132
Mean : 0.6837 Mean :-0.1462
3rd Qu.: 1.4071 3rd Qu.: 0.7696
Max. :11.4286 Max. : 4.2466
NA's :1 NA's :8
GIANT_eu_females_dv5_WHR GIANT_eu_males_dv5_WHR GIANT_eu_females_dv5_WHRadjBMI
Min. :-5.40000 Min. :-5.7143 Min. :-4.1176
1st Qu.:-0.05289 1st Qu.:-0.5998 1st Qu.:-0.1176
Median : 0.72727 Median : 0.3158 Median : 0.6792
Mean : 1.04842 Mean : 0.3332 Mean : 1.0237
3rd Qu.: 1.83667 3rd Qu.: 1.1396 3rd Qu.: 1.7165
Max. :10.00000 Max. :10.4167 Max. :12.2000
NA's :1 NA's :9 NA's :1
GIANT_eu_males_dv5_WHRadjBMI MAGIC_dv5_CIR MAGIC_dv5_DI
Min. :-5.7143 Min. :-6.8182 Min. :-7.2727
1st Qu.:-0.6545 1st Qu.:-1.1805 1st Qu.:-1.1468
Median : 0.2600 Median :-0.3181 Median :-0.4317
Mean : 0.2605 Mean :-0.4284 Mean :-0.4514
3rd Qu.: 1.0727 3rd Qu.: 0.4436 3rd Qu.: 0.3856
Max. : 4.4000 Max. : 2.6500 Max. : 3.0938
NA's :11 NA's :2 NA's :2
UKBB_White.blood.cell..leukocyte..count_irnt_both
Min. :-18.2208
1st Qu.: -0.7697
Median : 0.6758
Mean : 0.6271
3rd Qu.: 2.4026
Max. : 15.3512
UKBB_Red.blood.cell..erythrocyte..count_irnt_both
Min. :-14.7225
1st Qu.: -0.9043
Median : 0.5586
Mean : 0.5325
3rd Qu.: 2.0537
Max. : 14.1924
UKBB_Haemoglobin.concentration_irnt_both
Min. :-16.5546
1st Qu.: -1.1570
Median : 0.2704
Mean : 0.2186
3rd Qu.: 1.5898
Max. : 10.6601
UKBB_Mean.corpuscular.volume_irnt_both
Min. :-21.6424
1st Qu.: -2.0429
Median : -0.5790
Mean : -0.6121
3rd Qu.: 0.7729
Max. : 34.0513
UKBB_Red.blood.cell..erythrocyte..distribution.width_irnt_both
Min. :-13.6756
1st Qu.: -1.6386
Median : -0.1589
Mean : 0.1490
3rd Qu.: 1.2334
Max. : 30.7538
UKBB_Platelet.count_irnt_both
Min. :-16.9557
1st Qu.: -1.6401
Median : -0.3097
Mean : -0.3738
3rd Qu.: 1.1401
Max. : 12.3754
UKBB_Mean.platelet..thrombocyte..volume_irnt_both
Min. :-12.5186
1st Qu.: -1.2068
Median : 0.1079
Mean : 0.2361
3rd Qu.: 1.2998
Max. : 22.8954
UKBB_Platelet.distribution.width_irnt_both UKBB_Lymphocyte.count_irnt_both
Min. :-9.9613 Min. :-16.6525
1st Qu.:-0.6762 1st Qu.: -0.7528
Median : 0.6511 Median : 0.5302
Mean : 0.7695 Mean : 0.6259
3rd Qu.: 1.8587 3rd Qu.: 2.0755
Max. :13.7195 Max. : 18.2743
UKBB_Monocyte.count_irnt_both UKBB_Eosinophill.count_irnt_both
Min. :-17.4017 Min. :-9.2045
1st Qu.: -0.9571 1st Qu.:-0.8276
Median : 0.3107 Median : 0.3684
Mean : 0.1146 Mean : 0.3035
3rd Qu.: 1.5570 3rd Qu.: 1.4853
Max. : 6.8904 Max. :11.6449
UKBB_Basophill.count_irnt_both UKBB_Mean.reticulocyte.volume_irnt_both
Min. :-4.78292 Min. :-12.3778
1st Qu.:-0.75775 1st Qu.: -1.7593
Median : 0.05358 Median : -0.4256
Mean :-0.01316 Mean : -0.2459
3rd Qu.: 0.79590 3rd Qu.: 0.8440
Max. : 4.60448 Max. : 35.6927
UKBB_High.light.scatter.reticulocyte.count_irnt_both
Min. :-10.3262
1st Qu.: -0.1231
Median : 1.4324
Mean : 1.5276
3rd Qu.: 3.0871
Max. : 33.5552
UKBB_Albumin..quantile._irnt_both
Min. :-22.2720
1st Qu.: -1.1065
Median : 0.5980
Mean : 0.5194
3rd Qu.: 1.9410
Max. : 13.0821
UKBB_Alkaline.phosphatase..quantile._irnt_both
Min. :-102.42449
1st Qu.: -1.08833
Median : 0.28181
Mean : 0.02559
3rd Qu.: 1.79791
Max. : 32.60653
UKBB_Alanine.aminotransferase..quantile._irnt_both
Min. :-5.60888
1st Qu.:-0.02731
Median : 1.50262
Mean : 1.91352
3rd Qu.: 3.31938
Max. :36.65126
UKBB_Apoliprotein.B..quantile._irnt_both
Min. :-62.2517
1st Qu.: -1.2973
Median : -0.2485
Mean : -0.3145
3rd Qu.: 1.0240
Max. : 9.5859
UKBB_Aspartate.aminotransferase..quantile._irnt_both
Min. :-14.4419
1st Qu.: -1.2424
Median : 0.3908
Mean : 0.5634
3rd Qu.: 1.9606
Max. : 36.1778
UKBB_Direct.bilirubin..quantile._irnt_both UKBB_Urea..quantile._irnt_both
Min. :-6.76699 Min. :-11.9129
1st Qu.:-0.87599 1st Qu.: -0.7853
Median :-0.03287 Median : 0.4104
Mean :-0.01376 Mean : 0.3251
3rd Qu.: 0.85249 3rd Qu.: 1.5182
Max. :12.96292 Max. : 7.2983
UKBB_Calcium..quantile._irnt_both UKBB_C.reactive.protein..quantile._irnt_both
Min. :-18.3632 Min. :-35.9112
1st Qu.: -0.7259 1st Qu.: -1.2164
Median : 0.5552 Median : 0.3625
Mean : 0.5275 Mean : 0.4326
3rd Qu.: 1.8539 3rd Qu.: 2.1565
Max. : 9.3335 Max. : 62.7113
UKBB_Gamma.glutamyltransferase..quantile._irnt_both
Min. :-32.1167
1st Qu.: -0.2238
Median : 1.3074
Mean : 1.2105
3rd Qu.: 2.8998
Max. : 11.3009
UKBB_Glucose..quantile._irnt_both UKBB_HDL.cholesterol..quantile._irnt_both
Min. :-8.193 Min. :-45.11552
1st Qu.: 0.992 1st Qu.: -3.47943
Median : 2.388 Median : -1.53624
Mean : 2.997 Mean : -2.31456
3rd Qu.: 4.031 3rd Qu.: 0.01377
Max. :31.513 Max. : 18.22511
UKBB_IGF.1..quantile._irnt_both UKBB_Lipoprotein.A..quantile._irnt_both
Min. :-8.5869 Min. :-3.33277
1st Qu.:-1.3329 1st Qu.:-0.91386
Median : 0.5221 Median :-0.19555
Mean : 0.5547 Mean : 0.09755
3rd Qu.: 2.0404 3rd Qu.: 0.61855
Max. :24.6888 Max. :85.39116
UKBB_Phosphate..quantile._irnt_both UKBB_SHBG..quantile._irnt_both
Min. :-8.51235 Min. :-90.5859
1st Qu.:-1.13720 1st Qu.: -4.4009
Median :-0.00023 Median : -2.2519
Mean : 0.17706 Mean : -2.7702
3rd Qu.: 1.20004 3rd Qu.: -0.8199
Max. :32.00750 Max. : 28.2952
UKBB_Total.bilirubin..quantile._irnt_both
Min. :-8.5200
1st Qu.:-1.2574
Median :-0.2294
Mean :-0.3209
3rd Qu.: 0.7218
Max. : 9.4678
UKBB_Testosterone..quantile._irnt_female
Min. :-6.2869
1st Qu.:-1.0626
Median : 0.0296
Mean :-0.0496
3rd Qu.: 0.9504
Max. : 8.3395
UKBB_Total.protein..quantile._irnt_both
Min. :-16.4540
1st Qu.: -0.9606
Median : 0.4092
Mean : 0.3411
3rd Qu.: 1.6455
Max. : 10.1773
UKBB_Triglycerides..quantile._irnt_both UKBB_Urate..quantile._irnt_both
Min. :-42.23156 Min. :-22.2579
1st Qu.: 0.08495 1st Qu.: -0.6106
Median : 1.56697 Median : 0.9630
Mean : 2.04582 Mean : 1.0268
3rd Qu.: 3.99707 3rd Qu.: 2.6742
Max. : 50.45309 Max. : 12.5405
UKBB_Vitamin.D..quantile._irnt_both
Min. :-6.2730
1st Qu.:-1.5909
Median :-0.5612
Mean :-0.5202
3rd Qu.: 0.4173
Max. : 8.1506
UKBB_Diastolic.blood.pressure..automated.reading_irnt_both
Min. :-6.1009
1st Qu.:-0.8588
Median : 0.3509
Mean : 0.4356
3rd Qu.: 1.8728
Max. : 5.6658
UKBB_Systolic.blood.pressure..automated.reading_irnt_both
Min. :-5.2925
1st Qu.:-0.2404
Median : 0.9313
Mean : 0.9694
3rd Qu.: 2.0875
Max. : 7.4847
# Select strong signals
signals = apply(dat, 1, function(x) sum(abs(x) >= 1.96, na.rm = TRUE) != 0)
# Remove NAs
nas = apply(dat, 1, function(x) sum(is.na(x)) == 0)
dat2 = dat[nas, ]R = ncol(dat2)
K = 20
V = diag(R) # Use identity matrix for now
set.seed(999)
U.init = c()
for (k in 1:K){
U.init[[k]] <- udr:::sim_unconstrained(R)
}
X = as.matrix(dat2)
f0 = ud_init(X = X, V = V, U_scaled = NULL, U_unconstrained = U.init)set.seed(1)
fit1 = ud_fit(f0, control = list(unconstrained.update = "ted", rank1.update = "ted", resid.update = 'none', scaled.update = "fa", maxiter=1e3, tol = 1e-02, tol.lik = 1e-2, n0 = 0,
lambda = R), verbose=TRUE)control$scaled.update is ignored when no scaled covariances are included in UPerforming Ultimate Deconvolution on 305 x 64 matrix (udr 0.3-140, "R"):
data points are i.i.d. (same V)
prior covariances: 0 scaled, 4 rank-1, 20 unconstrained
prior covariance updates: scaled (none), rank-1 (ted), unconstrained (ted)
mixture weights update: em
residual covariance update: none
max 1000 updates, tol=1.0e-02, tol.lik=1.0e-02
iter log-likelihood |w - w'| |U - U'| |V - V'|
1 -4.1778734067387224e+04 1.02e-01 1.05e+02 0.00e+00
2 -4.0097929195367360e+04 1.00e-01 2.38e+01 0.00e+00
3 -3.9650548119978972e+04 9.23e-03 7.31e+00 0.00e+00
4 -3.9436399991444137e+04 5.87e-03 2.35e+00 0.00e+00
5 -3.9319671088311617e+04 5.15e-03 1.32e+00 0.00e+00
6 -3.9225966249799269e+04 4.95e-03 1.71e+00 0.00e+00
7 -3.9168599634440987e+04 6.21e-03 4.65e+00 0.00e+00
8 -3.9141252687644723e+04 6.19e-03 2.95e+00 0.00e+00
9 -3.9110363554430682e+04 5.91e-03 2.15e+00 0.00e+00
10 -3.9099434787462196e+04 5.01e-03 1.41e+00 0.00e+00
11 -3.9073233712234512e+04 6.10e-03 1.23e+00 0.00e+00
12 -3.9021295382383149e+04 8.59e-03 1.69e+00 0.00e+00
13 -3.8997402612334037e+04 1.06e-02 2.89e+00 0.00e+00
14 -3.8968839870865981e+04 1.02e-02 2.15e+00 0.00e+00
15 -3.8928392166238060e+04 1.26e-02 1.07e+00 0.00e+00
16 -3.8894097402311178e+04 1.07e-02 1.07e+00 0.00e+00
17 -3.8840403658758070e+04 1.05e-02 1.03e+00 0.00e+00
18 -3.8802554338952163e+04 1.08e-02 1.01e+00 0.00e+00
19 -3.8733749360556962e+04 1.30e-02 1.22e+00 0.00e+00
20 -3.8663415318312036e+04 2.32e-02 1.03e+00 0.00e+00
21 -3.8623502786072750e+04 1.60e-02 9.55e-01 0.00e+00
22 -3.8590804570378175e+04 1.01e-02 9.42e-01 0.00e+00
23 -3.8557701327752344e+04 1.11e-02 9.71e-01 0.00e+00
24 -3.8536473589981557e+04 8.41e-03 9.18e-01 0.00e+00
25 -3.8503138309166519e+04 1.03e-02 9.26e-01 0.00e+00
26 -3.8453594678200003e+04 1.57e-02 1.39e+00 0.00e+00
27 -3.8410236331832406e+04 1.22e-02 1.57e+00 0.00e+00
28 -3.8375246079166420e+04 1.05e-02 9.99e-01 0.00e+00
29 -3.8349628900093827e+04 8.58e-03 9.85e-01 0.00e+00
30 -3.8317443475869019e+04 8.72e-03 9.80e-01 0.00e+00
31 -3.8301454543905966e+04 6.42e-03 9.74e-01 0.00e+00
32 -3.8287893103411290e+04 4.79e-03 9.69e-01 0.00e+00
33 -3.8251799318154888e+04 9.47e-03 9.64e-01 0.00e+00
34 -3.8224335041230632e+04 6.68e-03 1.42e+00 0.00e+00
35 -3.8209130520324448e+04 3.18e-03 9.53e-01 0.00e+00
36 -3.8164098426803852e+04 2.88e-03 9.47e-01 0.00e+00
37 -3.8144488444736264e+04 4.72e-03 9.42e-01 0.00e+00
38 -3.8144344114151128e+04 1.03e-03 9.36e-01 0.00e+00
39 -3.8145369174200059e+04 7.06e-04 9.31e-01 0.00e+00
40 -3.8140930576961939e+04 1.49e-03 9.25e-01 0.00e+00
41 -3.8138894293064805e+04 1.35e-03 9.20e-01 0.00e+00
42 -3.8140728444568107e+04 1.02e-03 9.14e-01 0.00e+00
43 -3.8136507001351958e+04 8.94e-04 9.09e-01 0.00e+00
44 -3.8125582730284084e+04 2.31e-03 1.77e+00 0.00e+00
45 -3.8121084511540648e+04 2.21e-03 8.98e-01 0.00e+00
46 -3.8120777636047387e+04 1.72e-03 8.92e-01 0.00e+00
47 -3.8117420453410603e+04 3.15e-03 8.87e-01 0.00e+00
48 -3.8109349682450586e+04 4.27e-03 1.31e+00 0.00e+00
49 -3.8095985214654458e+04 7.37e-03 8.76e-01 0.00e+00
50 -3.8079638680715339e+04 1.05e-02 8.71e-01 0.00e+00
51 -3.8075204713866646e+04 6.52e-03 8.65e-01 0.00e+00
52 -3.8073698601328164e+04 4.02e-03 8.60e-01 0.00e+00
53 -3.8074143456152888e+04 3.05e-03 8.55e-01 0.00e+00
54 -3.8074917378445192e+04 1.64e-03 8.49e-01 0.00e+00
55 -3.8075462784545038e+04 2.08e-03 8.44e-01 0.00e+00
56 -3.8073568288534923e+04 2.71e-03 8.38e-01 0.00e+00
57 -3.8071949173233188e+04 3.20e-03 8.33e-01 0.00e+00
58 -3.8072568325897388e+04 9.67e-04 8.28e-01 0.00e+00
59 -3.8033533007565653e+04 5.67e-03 1.59e+00 0.00e+00
60 -3.8015690616892483e+04 4.46e-03 8.18e-01 0.00e+00
61 -3.8016543161115806e+04 2.19e-03 8.12e-01 0.00e+00
62 -3.8012646180917996e+04 1.01e-03 1.47e+00 0.00e+00
63 -3.7994621379450335e+04 2.86e-03 8.77e+00 0.00e+00
64 -3.7998135233741217e+04 7.29e-04 7.96e-01 0.00e+00
65 -3.7998078888669246e+04 1.50e-03 7.91e-01 0.00e+00
66 -3.7973003502925647e+04 4.50e-03 7.86e-01 0.00e+00
67 -3.7961545452007806e+04 2.88e-03 7.81e-01 0.00e+00
68 -3.7944200324782279e+04 3.14e-03 1.01e+00 0.00e+00
69 -3.7945601602748487e+04 1.28e-03 7.70e-01 0.00e+00
70 -3.7940623157844624e+04 1.38e-03 1.33e+00 0.00e+00
71 -3.7907867019582838e+04 2.68e-03 1.45e+01 0.00e+00
72 -3.7905618468750050e+04 3.19e-03 7.55e-01 0.00e+00
73 -3.7904399453861079e+04 2.58e-03 7.50e-01 0.00e+00
74 -3.7905924964348640e+04 5.97e-04 7.45e-01 0.00e+00
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316 -3.7947586425956171e+04 7.19e-08 1.04e-01 0.00e+00
317 -3.7947664701895017e+04 7.10e-08 1.03e-01 0.00e+00
318 -3.7947742148554884e+04 7.01e-08 1.02e-01 0.00e+00
319 -3.7947818775523381e+04 6.92e-08 1.01e-01 0.00e+00
320 -3.7947894592263467e+04 6.84e-08 1.00e-01 0.00e+00
321 -3.7947969608114989e+04 6.75e-08 9.91e-02 0.00e+00
322 -3.7948043832295931e+04 6.66e-08 9.83e-02 0.00e+00
323 -3.7948117273903626e+04 6.58e-08 9.74e-02 0.00e+00
324 -3.7948189941915756e+04 6.50e-08 9.65e-02 0.00e+00
325 -3.7948261845191235e+04 6.41e-08 9.57e-02 0.00e+00
326 -3.7948332992470911e+04 6.33e-08 9.48e-02 0.00e+00
327 -3.7948403392378226e+04 6.25e-08 9.40e-02 0.00e+00
328 -3.7948473057886222e+04 6.17e-08 9.31e-02 0.00e+00
329 -3.7948541984601288e+04 6.09e-08 9.23e-02 0.00e+00
330 -3.7948610181295153e+04 6.01e-08 9.15e-02 0.00e+00
331 -3.7948677674539256e+04 5.92e-08 9.07e-02 0.00e+00plot_sharing = function(X, to_cor=FALSE, title="", remove_names=F) {
clrs <- colorRampPalette(rev(c("#D73027","#FC8D59","#FEE090","#FFFFBF",
"#E0F3F8","#91BFDB","#4575B4")))(128)
if (to_cor) lat <- cov2cor(X)
else lat = X/max(diag(X))
lat[lower.tri(lat)] <- NA
n <- nrow(lat)
if (remove_names) {
colnames(lat) = NULL
rownames(lat) = NULL
}
return(lattice::levelplot(lat[n:1,],col.regions = clrs,
xlab = "",ylab = "", main=title,
colorkey = TRUE,at = seq(-1,1,length.out = 128),
scales = list(cex = 0.2,x = list(rot = 45))))
}# Plot sharing patterns with w > 0.01
tol = 1e-2
U <- lapply(fit1$U,function (e) "[["(e,"mat"))
n_component = sum(fit1$w > tol)
w = order(fit1$w, decreasing = TRUE)
indx = w[1:n_component]plts = list()
for (i in 1:length(indx)){
title = paste(names(U)[indx[i]], "w =", round(fit1$w[indx[i]], 6))
plts[[i]] = plot_sharing(U[[indx[i]]], to_cor = TRUE, title = title)
}unit = 4
n_col = 3
n_row = ceiling(n_component / n_col)
pdf('t2d_patterns.pdf', width = unit * n_col, height = unit * n_row)
do.call("grid.arrange", c(plts, list(ncol = n_col, nrow = n_row)))
dev.off()quartz_off_screen
2
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin20.6.0 (64-bit)
Running under: macOS Monterey 12.0.1
Matrix products: default
BLAS: /usr/local/Cellar/openblas/0.3.18/lib/libopenblasp-r0.3.18.dylib
LAPACK: /usr/local/Cellar/r/4.1.1_1/lib/R/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gridExtra_2.3 udr_0.3-141 readxl_1.4.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 pillar_1.6.4 compiler_4.1.1 cellranger_1.1.0
[5] later_1.3.0 jquerylib_0.1.4 git2r_0.28.0 tools_4.1.1
[9] digest_0.6.28 lattice_0.20-44 gtable_0.3.0 evaluate_0.14
[13] lifecycle_1.0.1 tibble_3.1.5 pkgconfig_2.0.3 rlang_0.4.12
[17] yaml_2.2.1 mvtnorm_1.1-3 xfun_0.27 fastmap_1.1.0
[21] stringr_1.4.0 knitr_1.36 fs_1.5.0 vctrs_0.3.8
[25] rprojroot_2.0.2 grid_4.1.1 glue_1.4.2 R6_2.5.1
[29] fansi_0.5.0 rmarkdown_2.11 magrittr_2.0.1 whisker_0.4
[33] promises_1.2.0.1 ellipsis_0.3.2 htmltools_0.5.2 httpuv_1.6.3
[37] utf8_1.2.2 stringi_1.7.5 crayon_1.4.1